Hate Speech in Social Media Linguistic Approaches 9783031382475, 9783031382482

Table of contents :
Contents
Notes on Contributors
List of Figures
List of Tables
Part I: Introduction: Online Hate Speech—Object, Approaches, Issues
1: Building and Analysing an Online Hate Speech Corpus: The NETLANG Experience and Beyond
1 Introduction
2 Linguistic Approaches to Hate Speech
3 Genesis of the Book: Construction and Preparation of an Online Hate Speech Corpus
4 Book Layout: Parts and Chapters
References
2: Distinguishing Online Hate Speech from Aggressive Speech: A Five-Factor Annotation Model
1 Introduction
2 Related Work
2.1 Defining Hate Speech
2.1.1 Outside Academia
2.1.2 In Academic Discourse
2.2 Annotating Hate Speech
3 A Five-Factor Annotation Model for Hate Speech
3.1 Hate Speech as Part of Antisocial Discourse
3.2 The Five Factors
3.2.1 Content: Does the Message Express Prejudice?
3.2.2 Target: Is the Message Aimed at (a Member of) a Disadvantaged Group?
3.2.3 Purpose: Does the Message Intend to Cause Harm?
3.2.4 Agent: Does the Sender of the Message Identify with a Dominant Group?
3.2.5 Channel: Is the Message Publicly Transmitted?
4 Applying the Model
5 Extending the Analysis
6 Conclusion
References
Part II: Structural Patterns in Hate Speech
3: Improving NLP Techniques by Integrating Linguistic Input to Detect Hate Speech in CMC Corpora
1 Introduction
2 Related Work
2.1 CMC Hate Speech Corpora
2.2 Opinion Mining
2.3 Hate Speech Detection
2.3.1 Hate Speech Classification Approaches
2.3.2 Challenges for Automatic Detection of Hate Speech in CMC
3 Research Approach
4 Corpus Compilation
5 Pre-Processing
5.1 Tokenisation
5.2 Lemmatisation
5.3 Part-of-Speech Tagging
6 Using Linguistic-Pragmatic Patterns to Detect HS
6.1 Linguistic Pattern Identification
6.2 Analysis of Results
6.2.1 Pattern 1: (a | you) bunch of ((ADJ) + NN)
6.2.2 Remaining Patterns: if you think, (x) people like you, ((if | whether) you) like it or not
7 Conclusion
References
4: First-Person Verbal Aggression in YouTube Comments
1 Introduction
2 Background
3 Methods
4 Analysis
4.1 Functions of First-Person Verbal Aggression
4.1.1 Threats of Physical Aggression
Simple Threats
Direct Threats: I’d kill that shrimp
Indirect Threats: Good thing I know how to break a wrist with my bare hands
Conditional Threats
Prototypical: If I was there, I would slap her
Retaliatory: If someone grabs me, I’ll kick their ass
Punitory: I still punch a toxic woman in their face
Disciplinary: I would have to beat her ass if I were her parents
4.1.2 Expressions of Mental Aggression
Boulomaic Expressions
Agentive Wishes: I wanna slap her so hard
Nonagentive Wishes: I hope she dies
Emotive Expressions
Violent Reactions: I wanna kill myself after this video
Contemptuous Reactions: I would not fuck her for gold bars
4.2 Functions Associated with Verbs
4.3 Targets of First-Person Verbal Aggression
5 Discussion and Conclusion
References
5: Emotional Deixis in Online Hate Speech
1 Introduction
2 Emotional Deixis
3 Demonstrative Determiners in Portuguese
4 Method
4.1 The NETLANG Corpus
4.2 Corpus Tools
5 Results and Discussion
5.1 Overall Results
5.2 Proper Nouns versus Common Nouns
6 Conclusion
References
6: Derogatory Linguistic Mechanisms in Danish Online Hate Speech
1 Introduction
2 The Corpus
3 Morphology: Compounding and Affixation
3.1 Pejorative Word Formation
3.2 Compound Slurs
4 Counter Hate and Secondary Hate Speech
5 Compounds as Narrative and Stereotype Carriers in the Immigrant/Refugee Discourse
5.1 Stereotype: Culture of Violence
5.2 Stereotype: Muslim Culture Is Primitive
5.3 Stereotype: Islam Is Misogynous and Paedophilic
5.4 HS Narrative: Immigration by Cheating and Welfare Tourism
5.5 HS Narrative: Islamisation Master Plan
6 Dehumanising Metaphors
6.1 Metaphor: Immigration as a Natural Disaster
6.2 Metaphor: Islam Is a Disease
6.3 Animal Metaphors
7 The Use of Emoglyphs in HS
8 Syntactic Mechanisms
8.1 Generalisation Mechanisms
8.2 Othering and Insults Using Personal Pronoun Constructions
9 Humour
9.1 Language-Based Humour: Word Plays
9.2 Irony and Sarcasm
10 Polarity-Inverted HS Constructions
11 Word Embedding
11.1 Which Kind of Perker?
11.2 Criminal Foreigner or Radical Muslim?
12 Conclusion
References
Part III: Lexical and Rhetorical Strategies in the Expression of Hate Speech
7: Humorous Use of Figurative Language in Religious Hate Speech
1 Introduction
1.1 Internet as a Place to Express (Negative) Emotions Through Metaphors
1.2 Definitions
1.3 Figurative Language and Humour
2 Methodology
2.1 Case Selection Principles
2.2 Deliberate and Conventional Metaphor Identification Procedure
2.3 Context and Content of the Selected Threads
3 Analysis
3.1 Hate Speech + Conventional Metaphor + No Humour
3.2 Hate Speech + Deliberate Metaphor + Humour
3.3 Very Inappropriate + Conventional + Humour
3.4 Very Inappropriate + Deliberate + Humour
3.5 Inappropriate + Conventional + Humour
3.6 Inappropriate + Deliberate + Humour
3.7 Mildly Inappropriate + Deliberate + Humour
4 Conclusion
References
8: Rhetorical Questions as Conveyors of Hate Speech
1 Introduction
2 Implying Hateful Meanings
2.1 Defining Hate Speech
2.2 Research Approaches to Hate Speech
2.3 Rhetorical Questions
3 Data and Method
4 Findings
5 Conclusion
References
9: Enabling Concepts in Hate Speech: The Function of the Apartheid Analogy in Antisemitic Online Discourse About Israel
1 Introduction
2 The Meaning(s) of Apartheid
2.1 The Meaning of Analogy
2.2 The Apartheid Analogy in Online Discourse
3 Corpora
4 Research Design
5 Empirical Findings
5.1 General Observations
5.2 Stereotypes Enabled by the Apartheid Analogy
5.2.1 Evil
5.2.2 Guilt
5.2.3 Power and Instrumentalisation
5.2.4 Child Murder
5.3 Intensifying Analogies Enabled by the Apartheid Analogy
5.3.1 Nazi Analogy
5.3.2 Colonialism Analogies
5.3.3 References to Fascism and Terrorism
6 Conclusion
References
Sources
10: Hate Speech in Poland in the Context of the War in Ukraine
1 Introduction
2 A Definition Problem
3 Method
4 Polish-Ukrainian Relations
4.1 Historical Background
4.2 Resentments
4.3 Ukrainian Refugees in Poland in 2022
4.4 Linguistic Irritations
5 Hate Speech Against Ukrainians in Polish Twitter Entries
5.1 Simple Derogatory Derivatives
5.1.1 Ukry for Ukrainians
5.1.2 Banderowcy for Ukrainians
5.1.3 Upadlina or UPAdlina for Ukraine
5.2 Ukrainisation of Poland
5.3 Reminders of the Volhynia Slaughter
5.4 The Ukrainians Provoke a Third World War
6 Discussion and Conclusion
References
Part IV: The Interactional Dimension of Hate Speech: Negotiating, Stance-Taking, Countering
11: Stance-Taking and Gender: Hateful Representations of Portuguese Women Public Figures in the NETLANG Corpus
1 Introduction
2 Hate Speech and Misogynistic Language, Verbal Aggression and Impoliteness
3 Theoretical Framework and Methodology
4 Discussion
5 Conclusion
References
12: Negotiating Hate and Conflict in Online Comments: Evidence from the NETLANG Corpus
1 Introduction
2 From Hate Speech to Body Shaming and Conflict Talk
2.1 Hate Speech
2.2 Body Shaming
2.3 Conflict Talk
3 Material and Data
4 Analysis
4.1 Conflicting Representations
4.2 Extended Disagreements
4.3 Conflict Escalation
5 Discussion of Findings and Conclusion
References
13: Linguistic Markers of Affect and the Gender Dimension in Online Hate Speech
1 Introduction
2 Linguistic Features as Markers of Affect
3 Data and Study Design
3.1 The FRENK Corpus
3.2 Production of SUD Comments per Gender
3.3 Analysed Linguistic Features
4 Results
4.1 Typographical Level
4.2 Grammatical Level
4.3 Lexical Level
4.4 Expressivity and Gender
5 Discussion and Conclusion
References
14: Counteracting Homophobic Discourse in Internet Comments: Fuelling or Mediating Conflict?
1 Introduction
2 Effectiveness of Counterspeech
3 Types, Content, and Strategies of Counterspeech
4 Data, Methods, and Contextual Background
5 Analysis
5.1 Extent of Counteracting
5.2 Discursive Strategies in Homophobic Comments
5.3 Discursive Strategies in Counterspeech
5.3.1 Argumentation Strategies Expressing Hostility
5.3.2 Argumentation Strategies Based on Constructive Argumentation
5.3.3 Perspectivation and Involvement Strategies
5.3.4 Intensification and Mitigation Strategies
6 Conclusion
References
Index

Citation preview

Hate Speech in Social Media Linguistic Approaches Edited by Isabel Ermida

Hate Speech in Social Media

Hate Speech in Social Media Linguistic Approaches Edited by Isabel Ermida

Editor Isabel Ermida University of Minho Braga, Portugal

ISBN 978-3-031-38247-5    ISBN 978-3-031-38248-2 (eBook) https://doi.org/10.1007/978-3-031-38248-2 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: © Alex Linch shutterstock.com This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Contents

Part I Introduction: Online Hate Speech—Object, Approaches, Issues   1 1 Building  and Analysing an Online Hate Speech Corpus: The NETLANG Experience and Beyond  3 Isabel Ermida 2 Distinguishing  Online Hate Speech from Aggressive Speech: A Five-Factor Annotation Model 35 Isabel Ermida Part II Structural Patterns in Hate Speech  77 3 Improving  NLP Techniques by Integrating Linguistic Input to Detect Hate Speech in CMC Corpora 79 Idalete Dias and Filipa Pereira 4 First-Person  Verbal Aggression in YouTube Comments107 Ylva Biri, Laura Hekanaho, and Minna Palander-­Collin

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5 Emotional  Deixis in Online Hate Speech139 Joana Aguiar and Pilar Barbosa 6 Derogatory  Linguistic Mechanisms in Danish Online Hate Speech165 Eckhard Bick Part III Lexical and Rhetorical Strategies in the Expression of Hate Speech 203 7 Humorous  Use of Figurative Language in Religious Hate Speech205 Liisi Laineste and Władysław Chłopicki 8 Rhetorical  Questions as Conveyors of Hate Speech229 Vahid Parvaresh and Gemma Harvey 9 Enabling  Concepts in Hate Speech: The Function of the Apartheid Analogy in Antisemitic Online Discourse About Israel253 Matthew Bolton, Matthias J. Becker, Laura Ascone, and Karolina Placzynta 10 Hate  Speech in Poland in the Context of the War in Ukraine287 Lucyna Harmon Part IV The Interactional Dimension of Hate Speech: Negotiating, Stance-Taking, Countering 309 11 Stance-Taking  and Gender: Hateful Representations of Portuguese Women Public Figures in the NETLANG Corpus311 Rita Faria

 Contents 

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12 Negotiating  Hate and Conflict in Online Comments: Evidence from the NETLANG Corpus341 Jan Chovanec 13 Linguistic  Markers of Affect and the Gender Dimension in Online Hate Speech369 Kristina Pahor de Maiti, Jasmin Franza, and Darja Fišer 14 Counteracting  Homophobic Discourse in Internet Comments: Fuelling or Mediating Conflict?397 Jūratė Ruzaitė I ndex431

Notes on Contributors

Joana  Aguiar holds a PhD in Language Sciences, Specialization in Sociolinguistics, with the thesis “Mechanisms of clause connection: the importance of social variables”. She is an invited asistant professor at the School of Education of the Polytechnic University of Bragança and External Researcher at the Centre for Humanistic Studies of the University of Minho, Portugal. She has integrated research projects financed by the Portuguese Foundation for Science and Technology in the areas of phonological frequency patterns, sociolinguistic variation, and hate speech. Her main research interests are syntactic variation and forensic authorship attribution. Laura Ascone  research focuses on computer-mediated communication, the expression of emotions, as well as hate speech. She defended her PhD in Linguistics at the Université Paris-Seine. Her thesis on The Radicalisation through the Expression of Emotions on the Internet dealt with the rhetorical strategies used in both jihadist propaganda and institutional counternarrative. She is currently a postdoctoral fellow at the Technische Universität Berlin in the international project Decoding Antisemitism. She is also part of various research groups dealing with social issues, such as Draine, established as part of the Horizon 2020 European project PRACTICIES. ix

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Notes on Contributors

Pilar  Barbosa is Associate Professor of General and Portuguese Linguistics at the University of Minho, and a member of the Linguistics Program of the Federal University of Rio de Janeiro. She holds a PhD in Theoretical Linguistics from the Department of Linguistics and Philosophy at the Massachusetts Institute of Technology (MIT). Her research interests are theoretical linguistics, formal grammar, comparative syntax, experimental syntax, language variation, Portuguese, and Romance linguistics. She is currently director of the master’s programme in Linguistics at the University of Minho and coordinator of the research group Theoretical and Experimental Linguistics at CEHUM. Matthias J. Becker  is the lead of the pilot project Decoding Antisemitism and postdoc researcher at the ZfA at Technische Universität in Berlin. A consistent link between all his research activities is the question of how implicit hate speechapparently accepted within various milieus of the political mainstream—is constructed and what conditions its production is subject to. His research is based on disciplines such as pragmatics, cognitive linguistics, (critical) discourse and media studies, research on prejudice and nationalism, as well as on social media studies. He is the author of the book Antisemitism in Reader Comments (Palgrave, 2021). Eckhard Bick  is a German-born linguist who works as a researcher in computational linguistics at the University of Southern Denmark, where he is a professor at the Institute of Language and Communication. He has written computational grammars and lexica for most Germanic and Romance languages and is a leading expert in the area of Constraint Grammar, with numerous publications within the area of Corpus Linguistics and Natural Language Processing. Current research interests include semantic corpus annotation, hate speech in social media, and rule-base proofing tools. He is a member of the XPEROHS project on hate speech in German and Danish social media. Ylva Biri  is a doctoral researcher in language studies at the University of Helsinki. In her research, she uses corpus linguistics to study pragmatics, discourse, and sociolinguistics. Specialising in computer-mediated communication, she studies the discourses, registers, and group norms of online interaction. Her PhD dissertation in English linguistics analyses

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how social media texts express subjective evaluation through stance. She has also worked with quantitative multi-dimensional register analysis to study interpersonal language in news and opinion writing. Matthew Bolton  is a researcher on the Decoding Antisemitism project at the Zentrum für Antisemitismusforschung (ZfA) at Technische Universität Berlin. His work focuses on conceptual history, critical theory, antisemitism, and legal theory. He is the co-author of Corbynism: A Critical Approach (Emerald, 2018), and his work has appeared in journals such as Philosophy and Social Criticism, British Politics, Political Quarterly, and the Journal of Contemporary Antisemitism. He is a fellow of the London Centre for the Study of Contemporary Antisemitism. Władysław Chłopicki  is Professor of Linguistics and Translation at the Jagiellonian University in Kraków, Poland. His academic interests include interdisciplinary humour research in the context of cognitive linguistics, linguistic pragmatics, narratology, and cultural studies (most recently humour in the public sphere). He has convened regular conferences on Communication Styles held in Poland since 2013, coedited the volume Culture’s Software (2015) and a special issue of the Journal of Pragmatics (2019), which both dealt with this fascinating field of study. He is a founding editor of The European Journal of Humour Research and Tertium Linguistic Journal. Jan Chovanec  is Professor of English linguistics at Masaryk University in Brno, the Czech Republic, specialising in discourse analysis and socio-­ pragmatics. He has done research on the discursive processes of othering, for exmple in relation to the presentation of the ethnic minorities in online reader comments. He is the author of Pragmatics of Tense and Time in News (2014) and The Discourse of Online Sportscasting (2018), and coeditor of a number of publications, includingRepresenting the Other in European Media Discourses (2017), The Dynamics of Interactional Humour (2018), and Analyzing Digital Discourses: Between Convergence and Controversy (2021). Idalete Dias  is Assistant Professor at the School of Arts and Humanities of the University of Minho, where she coordinated the creation of the Master’s Degree Programme in Digital Humanities (2017), the first of its

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kind in Portugal. She is currently a member of the Steering Board of the European Master in Lexicography—an Erasmus Mundus Programme funded by the European Education and Culture Executive Agency. Her main research interests include Digital Humanities, Lexicography, Terminology, Corpus Linguistics, and Electronic Textual Editing applied to the Humanities. She was Co-PI of the NETLANG project. Isabel Ermida  is Professor of Linguistics at the Department of English Studies of the University of Minho in Portugal. Her research activity has involved the analysis of implicitness and indirectness in language, as regards such apparently disparate phenomena as humour and hate. She has delved into the former in literary texts (The Language of Comic Narratives, Mouton, 2008) and in news satire. The latter has been the focus of her latest publications, which apply input from impoliteness studies and speech act scholarship to the analysis of prejudice and discrimination in social media discourse. She was the head of the NETLANG project. Rita  Faria  is Assistant Professor at Universidade Católica Portuguesa (UCP), in Lisbon, where she coordinates the BA in Applied Foreign Languages and teaches a number of language and discourse-related BA and MA courses. She completed her PhD in Contrastive Linguistics in 2010 at UCP, her MPhil in Linguistics in 2002 at the University of Cambridge, and her BA in Portuguese and English Studies at the University of Lisbon, in 2001. She is a researcher at CECC–UCP and her research interests include forms of address, im/politeness, language and gender, pragmatics, hate speech, computer-mediated discourse, and sociolinguistics. Darja Fišer  is an associate professor at the University of Ljubljana and Senior Research Fellow at the Institute of Contemporary History. She is leading the new national research programme for Digital Humanities in Slovenia and is National Coordinator of DARIAH-SI and Executive Director of CLARIN ERIC. Her background is in corpus linguistics and language resource creation. As a researcher and lecturer, she is heavily involved in integrating corpus-­linguistics methods and natural language processing with SSH disciplines that work with language material.

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Jasmin Franza  is a doctoral researcher at the University of Ljubljana, a researcher at the Slovene Research Institute in Trieste, and a translator. She obtained her master’s degree in Translation Studies. Her fields of interest are sociolinguistics, corpus linguistics, and computer-mediated communication. Her studies focus specifically on emotion recognition and analysis of online socially unacceptable discourse targeted at LGBTQ+ people and migrants. Moreover, her research activity also focuses on linguistic matters relating to the Slovene community in Italy. As a translator, she deals with Slovene, Italian, and English texts. Lucyna  Harmon  is head of the Chair of Translation Studies at the Department of English, University of Rzeszow. Her research encompasses general and literary translation, screen adaptation, and social phenomena of a linguistic nature. Her most recent publications include the edited volume Kosinski’s novel The Painted Bird in Thirteen Languages (Brill 2022) and the articles “Idiom as a Translation Technique: A Theoretical Postulate” (Cadernos de Tradução 2021) and “Agatha Christie’s PoirotNovels as Fairy-Tales: Two Case Studies” (Literator 2021). Her book on adaptation strategies in the Poirot TV series will appear in 2023. Gemma  Harvey  is a postgraduate researcher with a Vice Chancellor’s PhD Studentship at Anglia Ruskin University, Cambridge, UK. Her current PhD research investigates perceptions and responses to interpersonal conflict involving impolite, offensive, and hateful language in the workplace, from the perspectives of people who live and work in socioeconomically deprived market towns in North Cambridgeshire. The research aims to improve workplace policies and general attitudes towards inclusivity, intercultural awareness, and interpersonal confidence. Her main research interests are intercultural pragmatics, offensive and hateful language, and multimodal evaluative communication. Laura Hekanaho  (University of Helsinki/Tampere University) is a postdoctoral researcher in language studies. Her research interests lie in the field of sociolinguistics, focusing on language and gender research. In her previous studies, she has utilised survey methods, corpus methods, and discourse-analytical approaches to investigate the relationship between language, gender, and identity, as well as related language attitudes and

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ideologies. Her PhD dissertation explored attitudes towards generic and nonbinary English pronouns, while in her postdoctoral research she has examined gender-exclusive language use in Finnish, and identity construction in online discourse. Liisi Laineste  is a senior researcher at the Department of Folkloristics of the Estonian Literary Museum. Her main research object is folk humour and its online manifestations. She has published articles and edited books and journal issues on ethnic humour, internet folklore, and online communication, many of which represent an interdisciplinary angle and combine folkloristics with linguistics, psychology, sociology, or communication studies. She has a standing interest in digital humanities, organising the Estonian yearly DH conferences and promoting digital methods through workshops, lectures, and research. Kristina  Pahor  de  Maiti  is a doctoral researcher at the University of Ljubljana and CY Cergy Paris University. She earned her MA in Interpreting Studies at the University of Ljubljana and later worked as a translator and interpreter in the private sector. Her research interests include spoken language, parliamentary discourse, and computer-­ mediated communication, which she investigates in sociolinguistic and corpus-linguistic frameworks. She is currently focusing on a corpus-based analysis of socially unacceptable discourse online, with a special emphasis on its figurative dimension. Minna Palander-Collin  is Professor of English Language and Director of the Doctoral School at the University of Helsinki. Her previous leadership roles include vice-dean at the Faculty of Arts and director of the Helsinki Collegium for Advanced Studies. Since 2009, she has been PI of several funded research projects. Her main research interests include historical sociolinguistics, historical pragmatics, language and social change, language and identity, and corpus linguistics. She is also one of the compilers of the Corpus of Early English Correspondence (CEEC-400). She is an elected member of the Finnish Academy of Science and Letters. Vahid Parvaresh  is Associate Professor in the School of Humanities and Social Sciences at Anglia Ruskin University, Cambridge, UK. His areas of interest include pragmatics theory as well impoliteness research. When it

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comes to the former, he is particularly interested in investigating how theories of pragmatics could move away from the heavy interdependence which is often assumed to exist between words and meanings. With regard to the latter, he is interested in investigating hate speech and other forms of language aggression. Filipa Pereira  is a software engineer, with a master’s degree in Informatics Engineering (2022) from the University of Minho, Portugal, in the areas of Language and Knowledge Processing and Data Science. Her dissertation, titled “SAQL: Query Language for Corpora with Morpho-syntactic Annotation”, focused on corpus annotation and hate speech detection. She was a research grantee in the international FCT NETLANG project, being responsible for designing scraping tools and performing the extraction and annotation of the bilingual corpus. Her research interests include Natural Language Processing, Document Annotation, and Web Development. Karolina Placzynta  is a linguist and political scientist with an interest in pragmatics, sociolinguistics, and critical discourse analysis. Her research is centred on the discourses of antisemitism, racism, misogyny, gender inequality as well as their intersections, mainstreaming, and normalisation. She also explores how language reflects and influences social identities over time. Before joining the UK team of the Decoding Antisemitism project, she researched patterns of discursive representations of immigration in the British press. She is a member of the DiscourseNet association. Jūratė Ruzaitė  is a professor of English linguistics and a senior researcher at the Department of Foreign Language, Literary, and Translation Studies at Vytautas Magnus University, Kaunas, Lithuania. She is the author of a number of articles and a book on vague language in spoken academic discourse, Vague Language in Educational Settings: Quantifiers and Approximators in British and American English (2007, Frankfurt am Main: Peter Lang). Her research interests include sociolinguistics, corpus linguistics, pragmatics, discourse analysis, language and ideology, disinformation, and hate speech.

List of Figures

Fig. 2.1 Five-factor model for distinguishing hate speech from aggressive speech Fig. 3.1 Pre-processing result for tokens ‘dont’ and ‘tbh’ Fig. 3.2 Polarity D=distribution for instances of Pattern 1 Fig. 4.1 Taxonomy of first-person verbal aggression Fig. 5.1 Distribution of sentiment analysis according to value (ranging from −1000 to 1000) Fig. 5.2 Sentiment analysis according to type of prejudice (Sexism, Racism, and both) Fig. 5.3 Distribution of determiners according to sentiment analysis Fig. 5.4 Distribution of exclamatory comments according to lemma and sentiment analysis Fig. 5.5 Distribution of non-exclamatory comments according to lemma and sentiment analysis Fig. 5.6 Distribution of determiners according to the type of noun and sentiment analysis (NETLANG subcorpus) Fig. 5.7 Distribution of determiners according to the type of noun and sentiment analysis (Portuguese Web 2018 Subcorpus) Fig. 5.8 Distribution of Demonstrative Determiner + Proper Noun according to sentiment analysis and exclamativity Fig. 5.9 Distribution of Demonstrative Determiner + Common Noun according to sentiment analysis and exclamativity Fig. 6.1 Parse tree with sample annotation fields

59 95 98 117 151 151 152 154 155 156 157 158 158 168 xvii

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List of Figures

Fig. 6.2 Perker-scale for minority target groups 193 Fig. 6.3 Vector similarity of negative emoticons/emojis: angry, sad, horror, sceptical (rows ordered for perker-scale)194 Fig. 6.4 Cross-language comparison of emoticon-negativity for key lexemes (Bick, 2020) 196 Fig. 6.5 Minority terms graded for ±criminal (kriminel) and ±radical (ekstremistisk)198 Fig. 11.1 Corpus annotation following representation of social actors and Appraisal (Judgement and Appreciation) divided by social actor—in percentages 323 Fig. 11.2 Corpus annotation for Impoliteness divided by social actor—in percentages 329 Fig. 13.1 Distribution of comments by their length (measured in words) per language and gender 376

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 3.1 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 5.1 Table 5.2 Table 5.3

Five-factor annotation of NETLANG corpus comments on ethnicity 61 Five-factor annotation of NETLANG corpus comments on gender 63 Five-factor annotation of NETLANG corpus comments on ageing 65 Linguistic features of NETLANG corpus comments on ethnicity, gender, and age annotated as hate speech 67 Total number of occurrences of opinion markers in the racism and sexism subcorpora 90 Distribution of initial items and verbs included in the analysis115 Clustering of verbs and proportional frequencies of functions127 Verbs associated with targets of first-person verbal aggression129 Functions associated with targets of first-person verbal aggression129 Demonstrative determiners in Portuguese 144 Demonstrative determiners in Portuguese: distribution in our sample 149 Distribution of sentiment analysis (negative, neuter and positive)150 xix

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List of Tables

Table 5.4

Distribution of sentiment analysis (negative, neuter and positive) according to determiner 153 Table 11.1 Van Leeuwen’s (1996, 2008) categorisation of social actors 320 Table 11.2 Impoliteness framework following Culpeper (2010, 2011) and Culpeper et al. (2017) 321 Table 11.3 Key to Fig. 11.1 324 Table 11.4 Key to Fig. 11.2 330 Table 13.1 The absolute and relative number of English comments per gender and SUD type (with token information in brackets) 375 Table 13.2 The absolute and relative number of Slovene comments per gender and SUD type (with token information in brackets) 375 Table 13.3 List of analysed linguistic features 378 Table 13.4 The total number of affective units and the ratio showing the relation with regard to the total number of comments for the English dataset 381 Table 13.5 The total number of affective units and the ratio showing the relation with regard to the total number of comments for the Slovene dataset 381 Table 13.6 The total number of affective units and the ratio showing the relation with regard to the total number of comments for the English dataset 382 Table 13.7 The total number of affective units and the ratio showing the relation with regard to the total number of comments for the Slovene dataset 382 Table 13.8 The total number of affective units and the ratio showing the relation with regard to the total number of comments for the English dataset 383 Table 13.9 The total number of affective units and the ratio showing the relation with regard to the total number of comments for the Slovene dataset 383 Table 13.10 The total number of affective units and the ratio showing the relation with regard to the total number of comments for the Slovene dataset 384 Table 13.11 The total number of affective units and the ratio showing the relation with regard to the total number of comments for the English dataset 385 Table 14.1 Distribution of homophobic and counteracting comments 410 Table 14.2 Mitigation through interrogatives in counterspeaking comments422 Table 14.3 Topoi in homophobic comments and counterspeech 424

Part I Introduction: Online Hate Speech— Object, Approaches, Issues

1 Building and Analysing an Online Hate Speech Corpus: The NETLANG Experience and Beyond Isabel Ermida

1 Introduction Every edited volume is a joint enterprise, resulting from the concerted efforts of a set of people with common research interests who happen to have crossed paths at a certain, fruitful, point in time. This book is no exception, but its group dynamics go deeper than the occasional CFP. Half the following chapters are by researchers who interacted closely for four years, the distance and the pandemic notwithstanding, as members of an

I. Ermida (*) University of Minho, Braga, Portugal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_1

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international project, “NETLANG”.1 The other half is by scholars who flew many miles to meet the team in Portugal, during a heat wave that did not manage to prevent the project’s final conference from being a prosperous forum of discussion. The topic, granted, is dark—but the dialogue around it was bright and inspiring, so much so that the following pages are a mosaic of different shafts of light, different views, and different perspectives, each unveiling a special facet of hate speech. The “darkness” of hate speech does not confine it to the dark web (Gehl, 2016). Instead, hate speech promenades along mainstream social media in broad daylight. “Social media”, by the way, is here understood as “Internet-based channels of masspersonal communication” which “derive value primarily from user-generated content” and allow users to “opportunistically interact and selectively self-present” (Carr & Hayes, 2015: 8). No special web browsers or tailored routers are needed to access hate speech: a connection to the Internet is all it takes. That is why surfing the web, handy and affordable as it has become, may nowadays be a perilous pastime. Online platforms admittedly keep the world open to millions, and allow communicators to creatively interact on a global scale, but they also set traps to the unwary user. Chauvinism, discrimination, oppression—all have found their way into online forums, collecting victims at the same rate as followers, and feeding ever greater polarisation, inequality, and radicalisation in society. Whenever prejudiced content is voiced online, an indeterminate, but potentially vast, range of people are subject to its pernicious impact. First and foremost, the targets, who directly bear the full force of the attack, both individually and collectively, are thence humiliated, offended, dehumanised, “othered”, and, by means of fear, silenced (e.g. Benesch, 2014). Gradually and surreptitiously, hate speech contributes to isolate,  Funded by the Portuguese Foundation for Science and Technology, the NETLANG project integrated linguists from five different European countries: besides Portugal, Czech Republic, Estonia, Finland, and Poland. It also integrated researchers from other areas besides linguistics: computer scientists, psychologists, law and education scholars. The project’s full title, which bears the initial adoption of a term (“cyberbullying”) which later came to be overshadowed and definitely replaced by “hate speech”, is “The Language of Cyberbullying: Forms and Mechanisms of Online Prejudice and Discrimination in Annotated Comparable Corpora of Portuguese and English” (ref. PTDC/ LLT-LIN/29304/2017). See https://sites.google.com/site/projectnetlang/team 1

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marginalise, disparage, and demonise vulnerable individuals and the communities they represent, causing them to see their rights jeopardised and their public image soiled. But the bystanders, or interlocutors, are also (indirectly) affected, and play a central, perlocutionary role in the hate speech act: they absorb, more or less distractedly, the biased social meanings fed to them, and may be persuaded to replicate them. In other words, they are the targets of incitement, and simultaneously the recipients, or decoders, of hatred aimed at third parties (Assimakopoulos, 2020; O’Driscoll, 2020). If they yield to such a malign influence, they may end up reproducing it and becoming hate emissaries themselves, or “hate recruits”, as Langton (2018) puts it, possibly engaging in active discrimination and even actual, physical violence. Here lies a major danger of online hate speech: the contamination effect, due to its “toxicity” (see Konikoff, 2021) and potential for “pollution” (Nagle, 2009). Haters are, actually, agents of ideological contagion at a global scale, lurking, with perceived impunity, behind the anonymous or pseudonymous hideout that a mere login provides (Woods & Ruscher, 2021), while the online permanence of their written commentary allows it to re-emerge and recapture support over time. The disease they spread is prejudice; the side-effects are heightened bigotry and intolerance. From a control and regulation perspective, the focus lies in employing ever more intelligent hate detection algorithms and establishing anti-­ discrimination policies, which necessarily have different legal expressions across countries (Banks, 2010). Public entities, from governments and political organisations to the digital companies responsible for supplying the social media services, all have struggled to keep hate speech under control, all the while respecting the overarching right to freedom of expression. This is a very challenging compromise, of which haters are well aware—and due to which they skilfully dodge the moderation obstacles by re-inventing themselves, constantly finding renewed, imaginative ways to convey their hateful contents. The centrality of language to understanding and monitoring hate speech is therefore undeniable: it is by manipulating it that haters accomplish their agendas, and it is by scrutinising it that moderators can spot and counter such agendas. Academics and non-academics alike have thus concentrated on capturing the ever evasive language used in hate speech. The NETLANG

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project team, just like the other researchers that joined them at the final conference, tried to tackle this very challenge. All through the duration of the project, as well as through the three days of its epilogue, hate-speech language was laid on a stretcher, stripped of its many outfits and disguises, and dissected to the bone. Many questions came out unanswered, alas, but many others were raised. Before turning to the analytical outcomes of the NETLANG project, together with the analyses of hate speech provided by the external contributors to this collection, a synopsis of existing research into the language of hate is in order.

2 Linguistic Approaches to Hate Speech Linguistic research into hate speech has grown steadily over the past few years, covering different levels of linguistic analysis and springing from various theoretical frameworks. This section offers an overview of such scholarship, which encompasses an array of phenomena, ranging from overt, explicit forms of hatred to covert, implicit strategies to convey prejudice and discrimination, most of which surface, at one time or another, throughout the present book. The lexical features of hateful language, prominent and manifest as they are, have naturally attracted much academic attention. Slurs and taboo words used in prejudiced discourse have been extensively analysed by such authors as Stokoe and Edwards (2007), who examine racial insults in police interrogations; Williamson (2009), who discusses the semantics of pejoratives and the merits of inferentialism, a non-truth-­ conditional approach to slurs; and Hedger (2013), who looks at the semantic grounding of derogatory racial epithets. Similarly, Anderson and Lepore (2013) investigate the fluctuations in the offensive potential of insults, whereas Nunberg (2018) reflects upon the dual—both descriptive and evaluative—nature of slurs.  The lexis of hate has also been explored in terms of keyness, frequency, and collocation, with the help of computational tools (e.g. Waseem & Hovy, 2016; Fersini et al., 2018; Zampieri et al., 2019). Automatic detection models based on lexical elements depart from the assumption that the presence of certain negative words (such as invectives and disparaging

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nouns and adjectives) can be used as a feature for classifying a text as hateful (Schmidt & Wiegand, 2017). This task requires lexical resources that contain such predictive elements, like ontologies and dictionaries. Some of these dictionaries focus on content words, such as insults and swearwords (see “Noswearing”, by Liu & Forss, 2015), profane words, including acronyms and abbreviations (Dadvar et al., 2012), and “label specific features”, such as forms of verbal abuse and widespread stereotypes (see the Ortony Lexicon, by Dinakar et al., 2011), all of which can be searched as potentially hateful keywords. Despite the popularity of keyness approaches (the NETLANG project also resorts to them—see below), they have been revealed to be problematic (e.g. Scott, 2010). As MacAvaney et al. (2019) rightly remark, keyword-based approaches may have high precision (i.e. high percentage of relevant hits from the set rated as hate speech) but low recall (i.e. low percentage of relevant hits from the global dataset). This implies that a system relying only on keywords would not identify hateful content that does not use them (false negatives). Conversely, it would also create false positives, since certain potentially pejorative keywords—like “trash” or “swine”—may be used in neutral, “clean” passages. Crucially, keyword-based approaches cannot handle figurative, implicit or nuanced language, which skilfully phrases hate in disguised ways. For instance, comments such as “Hold on, where did I put my harpoon?”, or onomatopoeias like “Oink!”, produced  in response to a text about an overweight model, sport no hateful keyword whatsoever—and yet express body shaming as regards obese people, a type of discrimination against a vulnerable group (Ermida, 2014). One last hitch overshadowing keyword lexical approaches is the deliberate obfuscation of words and phrases to evade automatic detection, as is the case of racist misspellings like “ni9 9er”, “whoopiuglyniggerratgoldberg” and “JOOZ” (Nobata et al., 2016). Many other linguistic approaches to hate speech stem from the realm of pragmatics, assuming an integrative view of hate speech in its social and interactional context. Critical Discourse Analysis (CDA) informs most scholarship in this regard (see Chovanec, and Ruzaitė, this volume). After all, as Assimakopoulos et al. (2020) point out, the CDA analytical paradigm typically looks at the linguistic expression of ideologically charged attitudes, especially as regards discrimination. Brindle (2016),

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for instance, has combined CDA with corpus linguistics to study frequency, collocations, and concordances expressing gender prejudice in a white supremacist online forum. Sharifi et al. (2017), likewise, use a critical discourse analytic approach to tackle the prejudiced construction of Islam in Western talk shows. Along similar lines, Esposito and Zollo (2021) apply a so-called Social Media Critical Discourse Studies perspective to the analysis of online misogyny, covering such textual phenomena as animal metaphors, body shaming insults involving synaesthesia, and slurs concerning purported gender identity, while Raffone (2022) combines CDA with a social-semiotic framework to analyse disability hate speech on TikTok. In particular, the sociocognitive paradigm within Critical Discourse Analysis has been very productive: see, for instance, Đorđević’s (2019) sociocognitive approach to readers’ comments on Serbian news websites, and Sirulhaq et al.’s (2023) comprehensive survey of a sociocognitive-CDA methodology in hate speech studies. Rather tellingly, Van Dijk (2005), an exponent of sociocognitive CDA, has dedicated his life’s research to studying prejudice, especially racist prejudice, in public discourse, and has recently inflected his attention to anti-racist discourse, one form of counterspeech (cf. Van Dijk, 2021). Cognitive Linguistics is another theoretical pool with pragmatic import for hate speech inquiry, especially as applied to conceptual metaphor (see Laineste & Chlopicki, this volume). Two examples are Musolff (2017) and Prażmo (2020), who look into the dehumanising metaphors that are used, respectively, in UK anti-immigrant debates on social media, and misogynistic prejudice in the InCel online community. Cognitive Linguistics has also been productive in exploring the concept of mental spaces in hate speech. Lewis (2012) elaborates on how mental spaces, blending, and related cognitive domains underlie the emotional construction of offense in hate speech, while Raj and Usman (2021) also resort to the notion of mental spaces in conceptualising hate speech on Facebook, in terms of base spaces and space builders that affect the perception or interpretation of hateful language. Speech Act Theory is a very fruitful framework for the analysis of hate speech. Austin and Searle’s account of intentionality, in particular, has  proved important for feminist and anti-racist views of language, which have asked for regulatory intervention against hate speech. In the

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1990s, a  raging debate involved such scholars as Mackinnon (1993), Langton (1993), Matsuda et  al. (1993), Hornsby (1995), and Lederer and Delgado (1995), all of whom regard hate speech as conveying a linguistically identifiable intent (illocutionary force) to cause harm, namely to subordinate and marginalise members of an oppressed group, and hence as a practice that should be subject to restriction. In particular, Langton’s (1993) “Speech Acts and Unspeakable Acts” applies speech act insight into the analysis of women’s oppression through pornography. A highly polemical response was Butler’s (1997) Excitable Speech: A Politics of the Performative, which stresses the “open-ended” nature of speech acts and the difficulty in attributing intentional blame, and advises against hate speech censorship on the grounds that it could paradoxically “silence” the victims, who otherwise could be roused to defy hate speech by “resignifying” and “restaging” it (on a counter-response to Butler, see Schwartzman, 2002). The dispute around the performativity of hate speech and the accountability of the illocutionary expression of discrimination and prejudice has regularly re-emerged. Tsesis (2009: 518), for instance, speaking from a legal standpoint, points out that “speech acts that rely on culturally recognized images of subordination” are not simply “the sentiments of a single person”; instead, they rely on “the symbolic efficacy of group slogans to express acceptable conduct toward a named class of individuals” (see also Gelber, 2017; and Weston, 2022, on whether or not speech can “perform” regulable action). Along similar lines, Carney (2014), adopts a Searlean framework to defend that examining the speech acts of a verbal exchange does make it possible to assess whether the speaker’s words are either hurtful or harmful (see also Özarslan, 2014, on applying speech act theory to hate speech studies online, in the era of Web 2.0). More recently, O’Driscoll (2020) has put forth interesting Searlean considerations, trying to theorise vaguer, indeterminate forms of intentionality and incitement, whereas MacDonald and Lorenzo-Dus (2020) have offered a Speech Act Theory discussion of persuasion with regard to terrorism incitement. Significantly, Assimakopoulos (2020) has examined how incitement to discriminatory hatred relates to illocution and perlocution, and he proposes a reworked Searlean notion of felicity conditions.

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Impoliteness Studies also rank amongst the most prevalent approaches to the language of hate (see Faria, and Ruzaitė, this volume). Actually, a good few years before the term “hate speech” became popular in linguistic research, a number of forerunning studies were already approaching hateful language in computer-mediated communication (CMC) from the perspective of impolite phenomena. In 2011, for instance, Lorenzo-­ Dus et  al. examined the impoliteness of prejudiced online polylogues, and in 2014 Lange discussed perceptions of impoliteness and inappropriateness in face of speech by haters, as opposed to “ranters”, on YouTube, while Carney (2014) also applied impoliteness concepts to tell hurtful from harmful content in public media. In the same year, Ermida’s (2014) analysis of sexism and body shaming in online newspaper comment boards also resorted to the impoliteness framework, as did her study of classist prejudice against the poor and unemployed in the Daily Mail message forums four years later (Ermida, 2018). The application of impoliteness studies to online hate speech became gradually established in the second part of the decade. Hardaker and McGlashan’s (2016) influential article on hate against women on Twitter is a central example of this trend, which Culpeper et al. (2017) crucially epitomise, by focusing on extreme religious hate speech assuming criminal contours (e.g. threat). Two other cases deserving mention are Kienpointer’s (2018) analysis of what he labels “destructively impolite utterances” in hate speech across a variety of online genres (discussion forums, blogs, social media, tweets, homepages), and Carr et al.’s (2020) proposal of an impoliteness annotation scheme to improve the precision of hate speech detection. In his recent programmatic article, Culpeper (2021) compares and contrasts the two phenomena, impoliteness and hate speech, in a metapragmatic, first-order approach, and concludes that users employ the qualifier “hateful”, rather than “impolite”, to characterise more extreme behaviours with associations of prejudice. Argumentation Studies—which historically stem from the combined fields of logic, dialectic, and rhetoric—are yet another relevant theoretical source for the linguistic analysis of hate speech, given its applications to civil debate, conversation, and persuasion (see Faria, and Ruzaitė, this volume). Examples are Burke et al. (2020), who examine argument and reasoning in Islamophobic and anti-Semitic discussions on Facebook,

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Domínguez-Armas et  al. (2023), who explore the argumentative functions, and the provocative effect, of mentioning ethnicity in prejudiced headlines, and Pettersson and Sakki (2023), who look into argumentation and polarisation in online debates around gender and radical-right populism. Humour Studies are a characteristically interdisciplinary field of research, but the linguistic embedding of humour, together with its contextual dependency, make it a privileged object of, respectively, semantic and pragmatic attention. Linguistic approaches to humour in hate speech research are various and enlightening (see Laineste & Chlopicki, as well as Bick, and Ruzaitė, this volume), springing from the enlarged discussion of implicit forms of hate speech, and especially of the ways to downplay the assaulting force of the utterance and to avoid accountability. Vasilaki (2014), for instance, looks at name-calling in hate speech being mitigated through humour, while Godioli et al. (2022) also examine how humour can help defendants accused of hate speech in courts of law. Other authors investigate racist humour and discuss the limits of freedom of speech: Leskova (2016), for example, uses the pun “Black humour” to study hate speech against racial minorities in Europe; Trindade (2020) concentrates on “disparagement humour” being used to convey gendered racism on social media in Brazil; and Menon (2022: 5–6), focusing on white supremacist forums in America, scrutinises how humour “slips under the radar as a tool for spreading incendiary ideas” and how “the infrastructures of hate masquerade as harmless harbingers of humour”. Further linguistic studies of humour in hate speech assume a computational stance and resort to Sentiment Analysis and Emotion Detection methods: Badlani et al. (2019) explore ways to disambiguate sentiment expressed in humour as used in hate speech, while Kazienko et al. (2023) also try to process subjective content in speech that is simultaneously humorous and hateful. Although pragmatics has gained the upper hand in the critical landscape of hate language, grammatical studies have also been occasionally offered. As early as the 1980s, Van Dijk (1987) was discussing the use of nominalisation to replace SVO syntactic structures in racist discourse, where subject and verb would otherwise give away the agent of the problematic action: for instance, substituting the full clause “The police raid

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killed Mrs Jarrett” (a Black woman who died from a heart attack during a police raid on her house), for the nominalised phrase “Mrs Jarrett’s raid death” manages to not identify the police as the agents responsible for the raid, thus concealing their active role and accountability (Van Dijk, 1987: 78). Passivation is another case in point: Van Dijk (1987) also treats it as a strategy to divert the focus of an action from the actor to the recipient of the action. Similarly, Ehrlich (2001) studies the language used by men in rape trials, and she finds passive voice to be a strategy they employ to mitigate their responsibility (see also Ehrlich, 2014). A very recent book, edited by Knoblock (2022), The Grammar of Hate: Morphosyntactic Features of Hateful, Aggressive, and Dehumanising Discourse, supplies an exciting view into the role that grammatical elements play in conveying hate (and aggression). Some studies in the collection focus on morphological elements, such as English suffixes: Mattiello (2022) for instance, looks at the dysphemic use of the slang suffix –o (as in lesbo, weirdo, thicko), which adds a negative connotation to the stem, while Tarasova and Fajardo (2022) analyse the pejorative potential of the diminutive suffixes –ie/y (as in brownie and blackie). The exploitation of articles and pronouns for vilification purposes is the focus of other chapters: Lind and Nübling (2022) discuss how using German forms usually reserved for inanimate objects in reference to humans, namely women, is gravely disparaging, whereas Ohlson (2022) examines how switching from “he/she” to “it” is a powerful dehumanising strategy. Word formation processes inform yet other contributions to the book: Beliaeva (2022) investigates how lexical blends can be used both as humorous and as derogatory terms, whereas Korecky-Kröll and Dressler (2022) study expressive compound patterns including taboo nouns and deprecating adjectives. Finally, hateful discourse can also find an outlet in certain grammatical constructions, as is the case of those using imperative verbs (Bianchi, 2022) and syntactic patterns, such as the infamous “I am no racist but…” (Geyer et al., 2022). Grammatical features have also informed automated models of hate speech detection, which try to consider the words in their syntactic environment. Gitari et  al. (2015), for instance, have refined lexicon-based searches with co-occurring words in the sentence sequence, by resorting to part-of-speech (POS) tagging: if a noun like “Jews” appears as an

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object of the verb “kill”, the probability of hate speech to occur is higher and the intensity greater. In the same vein, Warner and Hirschberg (2012) have used three-element structures, or POS trigrams, to tackle similar issues, as in “DT Jewish NN” (e.g. “that Jewish b*stard”). Silva et  al. (2016) have also applied syntactic structures to detect word relevance, as in “I”, e.g. “I f*cking hate Asian people”. Along similar lines, regular expressions, combining lexical elements with syntactic order, have been found to recur in the NETLANG corpus, and are good candidates to detect hate in other corpora as well. As Dias and Pereira show (this volume), some syntactic sequences produce good hate speech identification hits: this is the case of “(a | you) bunch of ((ADJ) + NN)”, “if you think”, “(x) people like you”, “((if | whether) you) like it or not”. Similarly, Ermida et  al. (2023) have scraped the NETLANG corpus for the presence of adversative constructions signalling conflictual opinion exchanges, namely “I ”, and the syntactic pattern does seem to be productive in detecting hate speech content. In the present collection, morphological and syntactic insight, for instance into the role of demonstratives (Aguiar & Barbosa), first-person verb forms (Biri et al.), and compounds (Bick), also provides a glimpse at the productivity and plasticity of specific structural elements in the hate speech sequence. Crucially, and not surprisingly, computational linguistics has taken centre stage in research into the language of hate speech, given the automatic detection needs that digital companies experience when moderating user-generated content on their platforms. Corpus linguistics tools provide an important avenue for quantitative and automatic (often preliminary) treatments of large corpora such as the ones at hand. Many of the following chapters strive to combine quantitative methods with a qualitative examination of specific, manageable subsets, constructed by scraping massive databases. Only in this way can the contextual information surrounding the hateful comment, or embedded in its very co-­ textual structure, be reliably  deciphered. Existing automatic detection tools—used, for instance, within Sentiment Analysis (or Opinion Mining) and Emotion Detection frameworks—manage to identify the negative polarity of a text and the probability that it expresses hateful

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emotion (see Dias & Pereira, this volume). SentiWordNet (Baccianella et  al., 2010), VADER (Valence Aware Dictionary and sEntiment Reasoner, see Hutto & Gilbert 2014), and JAMMIN3 (Argueta et al., 2016) are three such cases. Other popular corpus linguistics tools include Sketch Engine, which allows for a valuable range of exploratory inquiries and quantitative outputs, such as KWIC mappings and n-grams. In particular, Word Sketch analyses collocations, also in terms of grammatical relations, and produces statistical, rank-ordering results through logDice. Most of the chapters in this collection exemplify the use of one or another of these tools. Importantly, the chapters that spring from the NETLANG project, just like those that result from similar projects (e.g. FRENK, XPEROPHS, Decoding Antisemistim), sport a computational linguistics design and assume, to a greater or lesser extent, a corpus-based methodology. So as to contextualise the present book and lay out the process that led to its creation, I will next introduce the construction of the NETLANG corpus and the methodological decisions that were made along the way.

3 Genesis of the Book: Construction and Preparation of an Online Hate Speech Corpus The NETLANG project departed from the construction of a bilingual (English and Portuguese) corpus of potential hate speech, with a view to providing a large dataset basis for analysis, as well as material for future research. By the time the project reached its conclusion, a corpus of 50.5 million words had been built from scratch and made freely available online. This vast endeavour was motivated by the absence of a commonly accepted benchmark corpus for hate speech analysis, which has driven many other researchers to collect and label their own data from a variety of social media. Examples of such hate speech corpora are GermanTwitter (Ross et al., 2016), Hatebase (Davidson et al., 2017), Stormfront (Gilbert et al., 2018), XPEROHS (Baumgarten et al., 2019, see Chap. 6), and the FRENK corpus (Ljubešić et al., 2019, see Chap. 13). It should be noted

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that, according to MacAvaney et al. (2019), existing hate speech corpora vary greatly in terms of size, scope, relevance, annotated data features, and annotated hate speech features. In the case of NETLANG, this time-­ consuming, complex data compilation task lasted, as expected, throughout the four years of the project. A brief summary of the team’s work will frame this book, and shed light on the steps that led to the construction of the corpus, to its preparation in terms of data pre-processing, and to the various linguistic analyses for which it paved the way. The bilingual nature of the corpus was initially intended to provide a balanced output of online verbal productions in the two languages (Portuguese, after all, is the eighth most spoken language worldwide, with c. 230 million native speakers, official language status in seven countries around four continents, and co-official status in another three). So, we tried to produce a similar number of “prompts” per language, i.e. of online posts, both verbal (texts) and audiovisual (videos), that might trigger the user-generated content—the comments—to analyse. But we soon realised, as the extraction process evolved, that the English subcorpus proved to be much more productive, both in terms of number of comments per post, and of number of words per comment. From the figure above, 43 million words overwhelmingly concern the English subcorpus alone. These were mainly extracted from the comment boards of YouTube, but also from newspaper sites, namely The Metro, The Daily Express, and The Daily Mail. The Portuguese subcorpus also resorts to YouTube comments, as well as to Portuguese newspaper sites, namely O Público, Sol, and Observador. Our software engineering team colleagues designed a set of bespoke extraction programmes and scraping tools, which had to be constantly customised so as to dodge the armoured online platforms and their ever-changing source codes (Henriques et al., 2019). The primary aim of the project was to understand how user-generated content in social media expresses hate—i.e. prejudice and discrimination—against groups that are disadvantaged, be it in social, political, economic, legal, historical, physical, or symbolic terms.2 So as to tackle the  The phrase “disadvantaged groups” should be viewed from a variety of competing terms, such as “vulnerable groups”, “oppressed groups”, “protected groups”, and “minority groups”, each of which is bound to trigger a controversy of their own. See Chap. 2, Sect. 3.2.2. 2

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variety and complexity of such an intricate object, the NETLANG team started by determining what exactly such groups might be and by arranging them into a programmatic table. A list of ten social variables (see e.g. Burgess, 2018) was formulated, including gender, ethnicity, age, nationality, and social class, as well as other identity-forging factors, namely sexual orientation, religion, gender identity, physical features (including disability issues), and behaviour issues (esp. drug abuse). Some of the variables were further divided into subtypes, in a hierarchical conceptual structure: for instance, the “gender” variable was broken down into different categories, namely “female physical appearance”, “female sexuality”, and “female intelligence”, since women were deemed to be prevailing targets of gender discrimination, especially with regard to three defining features (their looks, sexual conduct, and intellectual capacity). Then, each social variable row on the table was matched with the sort of prejudice it typically produces: respectively, sexism, racism, ageism, nationalism, classism, homophobia, religious intolerance, transphobia, body shaming, and ableism. The selection of the online texts was carried out by searching news articles and videos that were likely to arouse hateful responses. The way to do this was to keep track of daily news events around sensitive topics, capable of stirring hateful reactions, such as racial killings, the refugee crisis, euthanasia laws, social subsidies, transitioning, and domestic violence, among others. Thematic, documentary-type YouTube videos were also scrutinised, together with the “related content” list on the side, which supplies a sometimes long history of similar videos and comment trends. Once the online texts were extracted, and turned into JSON files containing the entire comment threads existing at the time of extraction, they were automatically classified according to the table of social variables and types of prejudice. This was done by applying NetAC (Elias et al., 2021), a keyword analysis tool specifically designed for the NETLANG project, which uses a statistical framework to calculate the most frequent lexical items in the text, and automatically assign the corresponding prejudice label. The tool resorts to the list of keywords that the NETLANG team prepared for each of the social variables and corresponding types of prejudice, so as to provide a set of probable lexical cues signalling prejudiced discourse passages (on keyword shortcomings, see Scott, 2010;

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MacAvaney et al., 2019, and Sect. 2 above). In the case of classist prejudice, for example, words deemed to prompt discriminatory content as regards social status were, among many others, Beggar, Boor, Bum, Bumpkin, Clodhopper, Hayseed, Hick, Hillbilly, Loser, Peasant, Pikey, Redneck, Scrounger, Trailer trash, Underdog, Vagrant, White trash, and Yokel. Similar keywords were devised for every other type of prejudice on the table. NetAC, therefore, automatically classifies each comment thread according to the presence of such keywords. It should be noted that many texts were classified under more than one type of prejudice (which, pardon the layman extrapolation, hints at the proverb that trouble never comes singly). For instance, racist comments were often found to be sexist as well. Alternatively, prejudiced comments frequently prompted an equally prejudiced reply by an interlocutor, but targeting a different social group. A set of essential pre-processing operations followed the corpus assembling phase. Tokenisation, lemmatisation, and part-of-speech tagging were carried out, with a view to enabling linguistic annotation and computational analysis of the electronic texts. The particulars of online language, with its typical informality and speech-like nature (or “silent orality”, as Soffer 2010 puts it), make it challenging for computational treatment. In the case of an online hate speech corpus, the issue of creativity, patent in deliberate misspellings and other strategies to avoid detection, presents further difficulties (see Dias & Pereira, this volume). Constructing and preparing NETLANG’s bilingual comparable corpus was the first step of the project. Analysing subsets of data extracted from the NETLANG corpus was the second step, which gave rise to seven of the chapters in the present collection. I will next introduce them, together with the rationale underlying the organisation of the book.

4 Book Layout: Parts and Chapters The chapters in this book reflect the richness and variety of linguistic analyses which the NETLANG corpus understandably generates. So exciting are the online users’  comment texts, despite their deep-seated negativity, so outrageous the dialogues they prompt and so intense the

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intellectual and emotional interaction they express, that the present set of articles is but a sample of a very large territory to explore. The contributions that deal with other hate speech corpora, by researchers external to the NETLANG project, are no less stimulating in that they also look at various textual topics from rather different theoretical perspectives—and, it should be stressed, in a number of different languages. Indeed, an important pointer to the polymorphous character of this book is the range of languages analysed: besides English and Portuguese, Danish, Lithuanian, Persian, Polish, and Slovenian data are examined. As a consequence, an array of geopolitical contexts for hate speech are discussed, especially featuring anti-refugee and anti-immigrant discourse, as is the case of Danish social media content against foreigners. Such contexts also  involve stressful neighbouring relationships in countries like Poland and Ukraine, Iran and Afghanistan, and Israel and Palestine. In terms of analysis, the diversity of approaches is not only thematic but methodological: indeed, it resides not only in the various types of prejudice covered—from sexism to nationalism, racism, antisemitism, religious intolerance, ageism, and homo/transphobia—but also in the various analytical methods employed and theoretical frameworks used to support the linguistic analyses. The three parts that compose this book are based on the different linguistic phenomena under focus, rather than on thematic organisation. If the latter were the case, the contributions could be grouped under the different types of prejudice and social variables covered, for instance sexism and gender issues, racism and ethnicity issues, and so on. Instead, the focus is on the angle and level of linguistic analysis, covering a variety of approaches and an array of overt to covert textual data, namely (i) structural and explicit elements, like syntactic and morphological patterns which recur throughout the texts, (ii) lexical and stylistic elements, aiming at the often implicit ways in which vocabulary choices and rhetorical devices signal the expression of hate, and (iii) interactional elements, focusing on the pragmatic relationships established in the online communicative exchanges. An important proviso is that this division is not rigid or clear-cut. As many of the chapters intersect, dealing with more than one textual feature, or even tackling multiple linguistic facets simultaneously and rather comprehensively, they could be viewed differently

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and placed elsewhere. For the sake of simplification, then, let us consider the three broad Parts as mere indicators of the theoretical and methodological diversity of the volume. The Introduction to the book is called “Online Hate Speech: Object, Approaches, Issues” and it is divided into two preliminary chapters, where I provide the general theoretical framework of the book and its design. The chapter you are reading now presents the collection and outlines the NETLANG project, from which it originates, as well as the external contributions to the study of online hate speech that integrate the volume. The second chapter is also of an introductory nature, as it is a definitional and programmatic reflection on the object under focus. What is hate speech, after all? An overview of the extant scholarship reveals a tendency towards imprecision, dismissiveness, and all-­ inclusiveness. “Hateful” is often found amidst a variety of related, but by no means synonymous, adjectives, such as aggressive, offensive, insulting, abusive, rude, toxic, unacceptable, and extreme. Yet, using aggressive language, no matter how violently, in situations of disagreement, dislike, or annoyance, ought to be distinguished from the intentional expression of prejudice towards (members of ) disadvantaged groups, with likely harmful ideological agendas. By revisiting the classic communication framework involving senders, messages, channels, and receivers, and enlightening it with current linguistic insight, I put forth an annotation model of five factors, the positive co-occurrence of which signals hate speech. I then test the model against a range of examples from three subsets of the NETLANG corpus, classified under the sexism, racism, and ageism variables. Finally, I extend the analysis into other, linguistic, features of online hate speech. This preliminary, introductory discussion is therefore meant as a definitional layout of the object under focus in the book. Part II of the volume, “Structural Patterns in Hate Speech”, covers four chapters, each dealing with a different grammatical structure or element that recurs in the corpus as a marker of hate: (a) regular expressions with a stable structure, (b) verbs used in the first person, (c) demonstrative determiners, and (d) compounds and syntactic patterns, all of which are found to reappear in sequences of prejudice expression. This set of studies illustrates treatments of hate speech that lie outside strict content word analysis and instead reside in form and grammar. Most automatic

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tools designed to detect hate speech in large corpora have tended to concentrate on lexicon-based instruments targeting (negative) opinions end emotions. But the role of functional elements, such as determiners and conjunctions, in providing detection cues may prove very productive, supplying functionally embedded meanings. Besides, certain syntactic structures have been found to recur in hate speech corpora, which makes them promising research material. The first chapter in Part II is by Idalete Dias and Filipa Pereira—respectively, the co-principal researcher of the NETLANG project and its full-­ time grantee. In “Improving NLP Techniques by Integrating Linguistic Input to Detect Hate Speech in CMC Corpora”, they focus on automatic models that resort to Machine Learning (ML) and lexicon-based approaches in order to identify occurrences of hate speech in large corpora. They begin by describing details of the NETLANG corpus construction, namely of the Natural Language Processing (NLP) techniques used to obtain a tokenised, lemmatised, and part-of-speech tagged English-Portuguese corpus. Given the limitations of existing annotation tools, optimised for standard written production, instead of the highly informal and manipulated nature of CMC language, they discuss how their performance can be improved by integrating linguistic input. The mixed methods approach they put forth combines linguistic knowledge—including lexical, syntactic, and pragmatic input—with NLP techniques to trace fixed expressions conveying hate in user-generated content. Their aim is to analyse the behaviour of opinion markers that exhibit a certain degree of fixedness in the English subset of the NETLANG corpus as potential pointers to hateful (namely sexist and racist) content. In Chap. 4, Ylva Biri, Laura Hekanaho, and Minna Palander-Collin (the latter a member of the NETLANG team), analyse the Sexism subset of the NETLANG corpus in English, with a view to identifying instances where YouTube commenters explicitly express their personal intention to carry out physical violence against individual targets. So as to do this, the Authors concentrate on aggression verbs in constructions with the first-­ person singular subject pronoun (e.g. I punch, I kill), which signal the speaker’s assumption of an agentive position. The approach combines exploratory corpus-based investigation of patterns of ‘I + aggression verb’

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with an inductive approach, based on a qualitative, manual inspection of the comments in context. Taking advantage of the big data nature of the corpus, the Authors lay out a general taxonomy of first-person singular aggression online, expressing sexist and misogynistic language, including references to sexual assault. The taxonomy is broadly divided into “threats of physical aggression” (simple or conditional) and “expressions of mental aggression” (boulomaic and emotive), showing the variety of manifestations of violent and hostile verbal behaviour against women on the Internet. Chapter 5, co-authored by Joana Aguiar and Pilar Barbosa, home members of the NETLANG team, sets out to explore the use of a single, yet manifold, grammatical element—demonstrative determiners—to convey the speaker’s emotional involvement in, or detachment from, the subject under discussion, and to create solidarity effects between speaker and addressee. Deixis, a concept that usually concerns the speaker’s spatial and temporal perception of certain objects or persons, is here understood in the Lakoffian sense of an emotional marker of closeness versus remoteness as regards the referent and/or the addressee. By focusing on the Portuguese subset of the NETLANG corpus, the Authors depart from the hypothesis that the three-way system of demonstratives in Portuguese may be used with pragmatic functions, namely to establish either proximal or distal evaluative meanings. After encoding all occurrences through a preliminary Sentiment Analysis approach, they examine the presence of two analytical variables: markers of exclamativity and proper nouns following the determiner. The results suggest that the interaction of the two variables may boost the affective potential of demonstrative determiners, making the combination a possible pointer to hate speech in computer-mediated discourse. In Chap. 6, Eckhard Bick analyses a variety of language phenomena targeting immigrant and refugee minorities in Denmark, with a special emphasis on morphological processes of compound formation and on the role of syntax in generalisation and othering. Besides such specific grammar-based features, Bick also looks into more general prejudiced stereotypes and narratives, dehumanising metaphors, target-specific slurs, and the role of emojis as conveyors of sentiment and metaphor, making his chapter one of the more comprehensive accounts of hate-speech

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language in the volume, escaping a clear categorisation. The analysis is based on a large social media corpus compiled under the auspices of a bilingual Danish-German project, XPEROHS, and annotated at the morphological, syntactic, and semantic levels. The chapter also sports quantitative, corpus-based methods meant to (i) enable qualitative examination of the data based on linguistic pattern searches, (ii) allow for the inspection of co-occurrences and relative frequencies, so as to identify typical features of target concepts, and (iii) devise sentiment ranking on the basis of word vector distances and machine-learned word embeddings. Part III, “Lexical and Rhetorical Strategies in the Expression of Hate Speech”, deals with the way in which particular vocabulary choices and stylistic tactics manage to convey hateful content in strategically implicit ways. This covert expression of hate is achieved through indirect devices such as metaphor and irony, presupposed claims, and various allusions, not to mention mock politeness and humorous manoeuvres. Instead of being entertaining or solidarity-based, these creative moves may be meant to circumvent censorship, since the more manifest hatred is, the more probable to get reported. The four chapters in this  Part explore different aspects of figurative language, rhetorical resources, specific epithets, and certain analogies which seem to be employed to conceal the otherwise more easily censored expression of prejudice. In Chapter 7, Liisi Laineste and Władysław Chłopicki, both of whom were members of the NETLANG project, set out to inspect how figurative language, in particular complex metaphors, may be exploited to disguise the hateful import of religion-based comments in the NETLANG corpus. They start by discussing the propensity of the Internet to express negative emotions, and move on to ascertain how humour, together with figurative devices such as metaphor, irony and exaggeration, can mitigate such emotional negativity. By applying Deliberate Metaphor Theory to the analysis of a set of religious hate speech comments on YouTube, they focus on conventional and deliberate metaphor usage from the three-fold perspective of metaphor in mind, language, and communication. Then they concentrate on its crucial combination with humour, hypothesising that humorous metaphors play an important role in alleviating the hatefulness of the message and hence in avoiding detection. The analysis reveals the existence of a scale of social inappropriateness from the (less

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inappropriate) complex, deliberate metaphors to the (more inappropriate) simpler, conventional ones, which suggests a continuum of metaphorically expressed hate as a function of context. In Chap. 8, Vahid Parvaresh and Gemma Harvey examine the way in which hate is implicitly conveyed  by means of rhetorical questions in Instagram comments targeted at Afghan people. The corpus comprises numerous  comment threads totalling 700 individual comments published on the official Instagram account of Persian BBC between early 2019 and early 2021, i.e. prior to the taking over of Afghanistan by the Taliban. The findings indicate a ubiquitous use of rhetorical questions by Iranians in communicating meanings which in some way attack the dignity of Afghan people, express biased opinions about them or convey harmful stereotypes. Parvaresh and Harvey divide their analysis of occurrences of rhetorical questions into (i) those that evoke confirmatively positive responses to hateful illocutions, (ii) those that evoke confirmatively negative responses, (iii) those that do not allow any response but a negative one, and (iv) those that evoke a wider range of hateful responses. All such strategies seem to confirm the pervasive expression of prejudice and discrimination against a certain national and ethnic group, namely the Afghans, as well as the attempt to convey such hateful content indirectly and implicitly. Chapter 9, co-authored by Matthew Bolton, Matthias J. Becker, Laura Ascone, and Karolina Placzynta, sets out to explore the role which so-­ called “enabling concepts” play in expediting and legitimating the expression of prejudiced ideas and concepts against Israel. By focusing on one such enabler, namely the Apartheid analogy, it offers a qualitative analysis of a set of more than 10,000 comments posted on 24 mainstream web news forums on the May 2021 Arab-Israeli conflict, where a range of anti-Semitic stereotypes, together with other analogies, surface in the vicinity of the Apartheid trope. These stereotypes are classed into four categories (evil, guilt, power and instrumentalisation, and child murder), whereas the analogies are grouped under the labels of Nazism, colonialism, fascism, and terrorism. The study springs from the Decoding Antisemitism project and is deemed to be pragmalinguistic, taking into account the immediate context of the comment thread and broader world knowledge. It departs from a general historical, political, and legal

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overview of the meaning(s) of Apartheid and of how its purportedly widespread acceptability makes it an enabler of a range of anti-Semitic attacks, which would otherwise be more easily countered. In Chap. 10, Lucyna Harmon offers an analysis of Twitter hateful content against the influx of Ukrainian refugees in Poland since the beginning of the Russian war on Ukraine. She starts by providing an overview of Polish-Ukrainian relations, including historic conflicts regarding territory boundaries, painful scars, like the Volhynian massacre, and ensuing resentments, which jeopardise  the officially collaborative connection between the two neighbours, the epitome of which is the 2022 massive migration of 7.3 million Ukrainian people into Poland. Harmon then focuses on a set of Polish creative neologisms used on Twitter comments which constitute derogatory derivatives made on the basis of clippings and blends (such as ukry, Banderowcy, upadlina), used by Poles to designate, and denigrate, the Ukrainians. This word-formation analysis of hateful vocabulary, which includes metaphorical extensions, is accompanied by an examination of prejudiced narratives, like the purported Ukrainisation of Poland. Harmon’s content analysis resorts to a classification according to Hart’s (2010) CDA topoi list, including such categories as burden, character, and danger, among others. The overall analysis seems to confirm a biased portrayal of Ukrainians as usurpers and invaders, rather than victims, along the lines of the typical anti-refugee discourse that affects other nationalities seeking shelter. Part IV, “The Interactional Dimension of Hate Speech: Negotiating, Stance-Taking, Countering”, looks at how the meanings of hate speech are dialogically constructed, on the basis of the interactional dynamics established between the various participants in the communicative situation. Even though most online platforms are asynchronous, the sort of conversation they enable is highly reactive, and it may be built as a result of, and at the same rate as, the very comments that are produced. The four chapters that constitute this final Part of the collection explore different facets of how social media commenters negotiate their participation, construct their contributions, react to their interlocutors’ comments or to those directed at third parties, and assume certain personas and stances as the discussions, sometimes heatedly, unfold.

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In Chap. 11, Rita Faria, a member of the NETLANG Project, sets out to examine how patterns of misogynistic hateful discourse emerge in the Portuguese newspaper section of the NETLANG corpus. She adopts a qualitative methodological approach which integrates different research strands, namely the guiding notion of stance-taking anchored in the Theory of Social Actors, elements of the Appraisal framework, and input from Impoliteness Studies, with a minor quantitative dimension offered to reinforce the qualitative analysis. Particular attention is paid to the discursive realisations of such linguistic devices as suffixation, possessivation, and collocations based on demonstrative determiners. By focusing on particular targets, namely women public figures (whether public office holders or well-known media figures), the analysis reveals how participants consistently take an aggressive, adversarial stance of misalignment and opposition, and voice openly discriminatory opinions about such women on the basis of their gender identity, thus reproducing a range of related sexist biases and stereotypes. Chapter 12, by Jan Chovanec, also a member of the NETLANG project, departs from a discussion of such interrelated concepts as anti-social discourse, hate speech, aggressive speech, and conflict talk. By adopting a sociopragmatic conception of conflict talk as a multi-dimensional phenomenon with several key dimensions (structure, linguistic realisation, and meaning), it extends the notion of conflict by integrating its sociolinguistic indexicality, in terms of identity construction and status assertion. The analysis focuses on a range of discursive strategies that are employed to express conflict in a NETLANG subset on body shaming and physical impairments. It describes how commenters use conflicting representations, engage in extended conflictual discussions, and escalate the mutual conflict, while gradually shifting from idea-oriented to person-oriented strategies. The findings suggest that conflict can be exploited to delegitimise the other, while simultaneously strengthening the accord of the in-­ group, united in what Chovanec calls “harmony in hatred”. In Chapter 13, Kristina Pahor de Maiti, Jasmin Franza, and Darja Fišer explore the role of gender in the production of online hate speech. More specifically, they examine gender differences regarding emotional expressiveness in what they call “socially unacceptable discourse” (SUD) online. By resorting to a corpus of English and Slovene Facebook

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comments, they look at three levels of linguistic analysis—typographical, grammatical, and lexical—so as to assess the use of explicit markers of affect by males and females. Two research questions guide the analysis: whether men and women differ in the quantity of production, and whether women and men differ in their use of affective linguistic features in SUD comments. The results show statistically significant differences in the use of linguistic markers of affect between English-speaking or Slovene-speaking male and female commenters with regard to their hate speech production. As predicted, men are more likely to post shorter and more violent comments as opposed to offensive ones, while women tend to include more linguistic markers of affect in their comments on all levels of analysis. Last but not least, Chap. 14, by Jūratė Ruzaitė, aims to study how participants in the comment section of a Lithuanian news portal resist and repel occurrences of LGBTQIA-targeted hate. Counterspeech, or bystander’s intervention in attacks to third parties, which some advocate to be the key to combatting hate speech, is put under scrutiny, not only in terms of its incidence but especially of its linguistic construction. The chapter combines a Critical Discourse Analysis perspective with input from impoliteness theory and argumentation studies, especially in terms of topoi analysis. The quantitative results show that counterspeaking is scarce and more common only in the section of registered users. The qualitative analysis reveals that the argumentation used in counter-­ comments contains a high degree of hostility and often resembles that of homophobic comments, with the proviso that the target of the attacks is not a disadvantaged group. Only in a small number of comments does the argumentation aim at constructive dialogue. Even so, the cases analysed give promising evidence of the existence of alternative, resistance voices in a rather hegemonic prejudice-laden cyberspace. One final note is in order in this introductory chapter. As is expectable in a book on hate speech, the following pages contain language and sequences of virtual dialogue which may be not only unpleasant but offensive, possibly disturbing, and even shocking at times. However, it is the linguist’s onus to look into all things linguistic, including hateful and discriminatory communication. All the chapters in this collection analyse real, actual user-generated content, publicly posted in open online forums

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with no registration required—which, according to the European Commission’s guidance note (2021: 14), makes any expectations of privacy unreasonable. And even though the given (or purported, pseudonymous) identity of the commenters is not revealed, no change has been made to the actual phrasing of the comments. By extension they include all original insults, slurs, swearwords, taboo terms, etc., as well as all orthographic, grammatical, and typographical idiosyncrasies and infelicities of the users’ texts. Of course, all verbatim occurrences of hateful language throughout the book must be viewed as instances of “mention” rather than “use” (e.g. Anderson & Lepore, 2013), that is, as “reported” hate speech, “mentioned” for the sake of academic inquiry. Acknowledgement  This work was sponsored by FCT (Foundation for Science and Technology, Portugal), under the auspices of the NETLANG project, ref. PTDC/LLT-LIN/29304/2017.

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Lorenzo-Dus, N., Blitvich, P. G.-C., & Bou-Franch, P. (2011). On-line polylogues and impoliteness: The case of postings sent in response to the Obama Reggaeton YouTube video. Journal of Pragmatics, 43, 2578–2593. MacAvaney, S., Yao, H.  R., Yang, E., Russell, K., Goharian, N., & Frieder, O. (2019). Hate speech detection: Challenges and solutions. PLoS One, 14(8), e0221152. Macdonald, S., & Lorenzo-Dus, N. (2020). Intentional and performative persuasion: The linguistic basis for criminalizing the (direct and indirect) encouragement of terrorism. Criminal Law Forum, 31(4), 473–512. MacKinnon, C. A. (1993). Only Words. Harvard University Press. Matsuda, M.  J., Lawrence, C.  L., Delgado, R., & Crenshaw, K.  W. (1993). Words that Wound: Critical Race Theory, Assaultive Speech, and the First Amendment. Westview Press. Mattiello, E. (2022). Language aggression in English slang: The case of the-o suffix. In N. Knoblock (Ed.), The Grammar of Hate (pp. 34–58). Cambridge University Press. Menon, P. (2022). Laughter is the Best Poison: Antagonistic Humor as the Handmaiden of Hate Speech. University of Michigan – Ann Arbor. Musolff, A. (2017). Dehumanizing metaphors in UK immigrant debates in press and online media. Journal of Language Aggression and Conflict, 3(1), 41–56. Nagle, J. C. (2009). The idea of pollution. UC Davis Law Review, 43(1), 1–78. Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., & Chang, Y. (2016). Abusive language detection in online user content. In Proceedings of the 25th International Conference on World Wide Web, 145–153. Nunberg, G. (2018). The social life of slurs. In D. Fogal, D. Harris, & M. Moss (Eds.), New work on Speech Acts (pp. 237–295). Oxford University Press. O’Driscoll, J. (2020). Offensive Language: Taboo, Offence and Social Control. Bloomsbury. Ohlson, L.  F. (2022). The power of a pronoun. In N.  Knoblock (Ed.), The Grammar of Hate (pp. 161–176). Cambridge University Press. Özarslan, Z. (2014). Introducing two new terms into the literature of hate speech, “hate discourse” and “hate speech act”: Application of speech act theory to hate speech studies in the era of web 2.0. Galatasaray Üniversitesi İletişim Dergisi, 20, 53–75. Pettersson, K., & Sakki, I. (2023). ‘You truly are the worst kind of racist!’: Argumentation and polarization in online discussions around gender and radical-right populism. British Journal of Social Psychology, 62(1), 119–135.

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Prażmo, E. (2020). Foids are worse than animals. A cognitive linguistics analysis of dehumanizing metaphors in online discourse. Topics in Linguistics, 21(2), 16–27. Raffone, A. (2022). “Her leg didn’t fully load in”: A digitally-mediated social-­ semiotic critical discourse analysis of disability hate speech on TikTok. International Journal of Language Studies, 16(4), 17–42. Raj, S. M., & Usman, A. (2021). The use of mental spaces in the conceptualization of hate speech. GPH  - International Journal of Social Science and Humanities Research, 4(05), 12–21. Ross, B., Rist, M., Carbonell, G., Cabrera, B., Kurowsky, N., & Wojatzki, M. (2016). Measuring the reliability of hate speech annotations: The case of the European refugee crisis. In The 3rd Workshop on Natural Language Processing for Computer-Mediated Communication. Schmidt, A., & Wiegand, M. (2017). A survey on hate speech detection using natural language processing. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media of the Valencia Association for Computational Linguistics, 1–10. Schwartzman, L. H. (2002). Hate speech, illocution, and social context: A critique of Judith Butler. Journal of Social Philosophy, 33(3), 421–441. Scott, M. (2010). Problems in investigating keyness, or clearing the undergrowth and marking out trails…. In M. Bondi & M. Scott (Eds.), Keyness in Texts (pp. 43–58). John Benjamins. Sharifi, M., Ansari, N., & Asadollahzadeh, M. (2017). A critical discourse analytic approach to the discursive construction of Islam in Western talk shows: The case of CNN talk shows. International Communication Gazette, 79(1), 45–63. Silva, L.  A., Mondal, M., Correa, D., Benevenuto, F., & Weber, I. (2016). Analyzing the targets of hate in online social media. ICWSM, 687–690. Sirulhaq, A., Yuwono, U., & Muta’ali, A. (2023). Why do we need a sociocognitive-­ CDA in hate speech studies? A corpus-based systematic review. Discourse & Society. https://doi.org/10.1177/0957926522112659 Soffer, O. (2010). “Silent orality”: Toward a conceptualization of the digital oral features in CMC and SMS texts. Communication Theory, 20(4), 387–404. Stokoe, E., & Edwards, D. (2007). ‘Black this, black that’: Racial insults and reported speech in neighbour complaints and police interrogations. Discourse & Society, 18, 337–372.

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2 Distinguishing Online Hate Speech from Aggressive Speech: A Five-Factor Annotation Model Isabel Ermida

1 Introduction Everywhere and anytime, at a click on a desktop mouse or a tap on a mobile phone screen, social media are instantly available to millions of digital users worldwide. Over roughly a two-decade time span, the Internet has grown into a generalised commodity and individuals have gained affordable access to a gigantic pool of fellow communicators and potential audiences. Understandably, this has created a widespread awareness of a genuinely democratic possibility of speaking out what one thinks “urbi et orbi”. Internet users have become self-­appointed opinion makers, often appearing not to think twice before saying, or rather typing, whatever crosses their mind. Yet, the participatory craze in today’s online world comes at a price. A high price. Words do hurt, against the popular

I. Ermida (*) University of Minho, Braga, Portugal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_2

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saying—but, more seriously yet, they harm (Waldron, 2012; Gelber, 2017). Despite the pervasiveness of hate speech in the online world (a majority of social media users have encountered it at least once—see Oksanen et al., 2014), its impact on society at large, and its popularity in academic inquiry, the phenomenon lacks a consensual definition—or, rather, it lacks a consensual awareness of the need for a definition. Most confuse it with offensive language (as Davidson et al., 2017, rightly remark), many with abusive and insulting speech (Fortuna et al., 2020; Paasch-Colberg et al., 2021), others with extreme speech (Udupa et al., 2021), and some go as far as mistaking it for mere profanity (Malmasi & Zampieri, 2018). And while many agree on the advantages of widespread participation in public speech, and vocally defend the inalienable right to freedom of expression, fewer are capable of drawing the line between free speech and hate speech (Yong, 2011; Howard, 2019). The different legal contexts which, from country to country, introduce the hate crime variable into the debate also make it harder to pinpoint what hate speech is. The conceptual vagueness and the theoretical imprecision in approaches to hate speech are rife throughout a variety of disciplinary approaches, including linguistics, where many recent contributions are also plagued with a misperception of what is aggressive and what is hateful. The need for an accurate and unambiguous definition of the phenomenon is particularly central to practical, applied areas of control and detection. Legal scholarship and natural language processing (NLP) research, respectively, strive to circumscribe the concept, as governmental institutions, international organisations, and media stakeholders all seek to establish anti-cyberhate regulations and recommendations. While law experts have struggled to determine how far control should go, or whether banning is useful at all (Buyse, 2014; Weinstein, 2017), computer scientists, who apply their NLP expertise to devising automatic detection tools, have also struggled to use exact definitions so as to better feed their machine learning systems. Establishing hate speech dataset  properties, for instance, is particularly dependent on definitional issues (Fortuna et al., 2020). Forerunning attempts to automatically spot “hostile” online speech surprisingly date as far back as the late 1990s (Spertus, 1997), but

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the evolution towards ever more fine-grained algorithms has been clouded by conceptual fuzziness regarding the notion of hate speech. Some scholars view the definitional enterprise as doomed, in light of one major hindrance: the fragility of the universality claim. Influential definitions by respected organisations, like the United Nations or the Council of Europe (see below), do not have binding power across a multiplicity of nations with different cultural and historical perceptions of the features and social groups they list (Herz & Molnár, 2012). It comes as no surprise, therefore, that the laws regarding hate speech drastically differ across countries (George, 2015; Brown, 2017), which in turn makes online hate crime—occurring on global platforms—notoriously difficult to combat at a transnational level. On the other hand, the role of context in interpreting hate speech by those taking part in the communicative situation (a first-order approach) is another major challenge to an attempted definition, which also ties in with its non-universal nature. The contextual conditions surrounding the occurrence of what may look like a hate speech act sometimes alter its perception entirely: for instance, the communicative expectations and the relationships between the interlocutors in certain online communities may actually allow for the purported hate speech instance to be received positively, or at least neutrally. Consider, for example, the situation of linguistic reclamation, i.e. appropriation of a derogatory moniker by its target(s), as is the case of insulting epithets like queer, n*gger, or c*nt (Brontsema, 2004; see also Warner & Hirschberg, 2012; Davidson et al., 2017): instead of being markers of hate speech, they may signal more or less conventionalised communicative situations meant as innocuous bonding and membership routines. Despite all the hurdles, (re)defining the concept of hate speech is in order—lest a productive, accurate spotting of its occurrences should continue to elude researchers. This chapter therefore offers a five-factor annotation model for identifying hate speech occurrences. Devised within the framework of the NETLANG corpus-based project (see Chap. 1),1 it is  I am grateful to my NetLang team colleagues for their feedback, receptiveness, and support in the seminars where I introduced the model and proposed it as a framework for the project’s future analyses. 1

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thought of as a theoretical tool, but also as a corpus annotation one—as an instrument to classify datasets extracted from online platforms in terms of a yes/no label, or presence/absence of a feature. At the outset of this endeavour, it is important to provide a preliminary overview not only of extant work on definitions of hate speech, but also of existing annotation models for hate speech, against which the present model ought to be viewed. The chapter is organised as follows. The second section briefly reviews the state of the art: first, it discusses existing definitions of the phenomenon of hate speech, pointing at the shortcomings, imprecisions, and inaccuracies which the debate involves; secondly, it presents current annotation frameworks for hate speech corpora and examines their challenges and limitations. Section 3 lays out the model by presenting and discussing the five factors deemed necessary and sufficient for hate speech to occur, prior to which a superordinate term, antisocial discourse, is outlined. The fourth section applies the model to three subsets of the NETLANG corpus, organised around the social variables of ethnicity, gender, and age. Section 5 extends the analysis by looking at the linguistic features of the hate speech subsets, with a view to devising language regularities.

2 Related Work Research on hate speech definition and annotation comes from academic and non-academic realms, and from a variety of actors, perspectives, and disciplines. The literature review offered next intends to provide a general view of the state of the art.

2.1 Defining Hate Speech 2.1.1 Outside Academia Far from being an abstract idea hovering above the lives of earthly humans, online hate speech is deeply rooted in society, impacting

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individuals and communities all over the globe. Its technology-­dependent existence also makes it a rather tangible fact in daily life, subject to regulation by the digital companies that provide the Internet services. On a higher level, governments, political institutions, and non-profit organisations are also expected to keep a watchful eye on all things social, let alone oppressive, discriminatory, and possibly dangerous. Therefore, the discussion around hate speech cannot be held in academia’s ivory tower, as it were, but in direct interplay with the various political, legal, and economic agents. Outside academia, institutions responsible for protecting fundamental rights have naturally been sensitive to, and concerned about, hate speech. A forerunning, influential text dated from 1997, with the title “Recommendation No. R (97) 20 ‘hate speech’”, is a much quoted document by the Council of Europe (CoE) which, according to the institution’s website,2 is “the only text with an internationally adopted definition of hate speech”, reading as follows: “(…) the term ‘hate speech’ shall be understood as covering all forms of expression which spread, incite, promote or justify racial hatred, xenophobia, anti-Semitism or other forms of hatred based on intolerance”. The text goes on to particularise types of intolerance, namely those “expressed by aggressive nationalism and ethnocentrism, discrimination and hostility against minorities, migrants and people of immigrant origin”. The definition comes with the disclaimer that the CoE “reaffirms its profound attachment to freedom of expression”. The main limitation of the definition is its emphasis on ethnocentric and nationalist issues, disregarding all other forms of prejudice. The United Nations puts forth a broader definitional scope in terms of the targets of hate speech, but also in terms of the means through which it is conveyed: Hate speech is any kind of communication in speech, writing or behaviour, that attacks or uses pejorative or discriminatory language with reference to a person or a group on the basis of who they are, in other words,

 https://www.coe.int/en/web/no-hate-campaign/committee-of-ministers1

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based on their religion, ethnicity, nationality, race, colour, descent, gender, or other identity factor.3 [Original emphasis]

Besides the so-called “identity factors” listed above, the definition is accompanied by an even more detailed catalogue, namely “language, economic or social origin, disability, health status, or sexual orientation, among many others”. Importantly, these factors are said to be either “real or perceived” and refer to “individuals or groups”, not to “States and their offices, symbols or public officials”, nor to “religious leaders or tenets of faith”. In other words, the focus is on disadvantaged social groups, or perceived individual members of such groups, instead of on institutional entities or official representatives. Definitional issues are important for non-political organisations too. Major digital corporations, like Facebook and former Twitter, have been criticised at various points for not doing enough to protect their users from hateful discrimination,4 and even accused of profiting from it, i.e. of benefiting from the intensified participation that hate speech may fuel. Consequently, they have been keen to establish stricter, regularly updated monitoring policies. The definitions they provide coincide in several respects. Facebook writes that hate speech is “a direct attack against people—rather than concepts or institutions—on the basis of what we call protected characteristics”.5 Though the meaning of “direct” is open to debate, they list a large variety of such characteristics, namely “race, ethnicity, national origin, disability, religious affiliation, caste, sexual orientation, sex, gender identity and serious disease”, but they problematically include cursing, which sometimes functions as a bonding in-group mechanism (see below), in the range of “attacks”. Similarly, Twitter  https://www.un.org/en/hate-speech/understanding-hate-speech/what-is-hate-speech. Accessed March 2023. 4  Nobata et  al. (2016: 145) report on a particular situation involving Facebook: “[I]n 2013, Facebook came under fire for hosting pages which were hateful against women such as Violently raping your friend just for laughs and Kicking your girlfriend in the fanny because she won’t make you a sandwich. Within days, a petition was started which amassed over 200,000 supporters, and several major companies either pulled or threatened to pull their ads from Facebook since they were inadvertently placed on these pages.” 5  https://transparency.fb.com/en-gb/policies/community-standards/hate-speech/. “Current version” accessed March 2023. 3

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(renamed as X.com in April 2023) uses the adverb directly and the verb attack on their Help Centre page regarding hateful content6: “You may not directly attack other people on the basis of race, ethnicity, national origin, caste, sexual orientation, gender, gender identity, religious affiliation, age, disability, or serious disease.” An interesting plus of the Twitter/X policy is their commitment to “combating abuse (…) that seeks to silence the voices of those who have been historically marginalised”, which highlights one of the perlocutionary effects of hate speech (see Langton, 2012, also Houston & Kramarae, 1991).

2.1.2 In Academic Discourse What hate speech is, more often than what it is not, is an issue that pervades most academic contributions to the study of hate speech, either as a presupposed given, hence taken for granted and easily dismissed, or as a minor problem deserving little, generally introductory, attention. The first possibility usually gives rise to the indiscriminate use of various, but by no means synonymous, and sometimes even conflicting, terms, such as hurtful, derogatory, violent, threatening, or obscene. The second tends to produce definitions that side with at least one of the more or less consensualised set of defining elements of hate speech (Brown, 2017), leaving aside all others. Albeit inconclusively, the debate has spanned the past twenty-odd years, and it has progressively focused on the “online” nature of most hate speech nowadays. Though an exhaustive coverage of the discussion is not possible, or even desirable, to undertake, a bird’s eye view of the existing literature will illustrate a variety of contributions to defining hate speech from different disciplines and theoretical standpoints. Their subtle differences depend on whether they assume a content-­ based, an intent-based, or an effect-based perspective to hate speech (Marwick & Miller, 2014), a threefold view that echoes the speech act organisation into locutionary, illocutionary, and perlocutionary dimensions.

 https://help.twitter.com/en/rules-and-policies/hateful-conduct-policy. Dated Feb. 2023.

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In legal studies, where determining the boundaries between hate speech and hate crime is of essential importance (Jacobs & Potter, 2000), the latter, effect-based, perspective has perhaps gained the upper hand, with a range of authors focusing on the damage that hate speech produces and the destructive consequences it may inflict on its intended victims. As early as the mid-1990s, the idea that hate speech targets vulnerable social groups in a way that is potentially harmful to them is present in Walker (1994), and steadily resurfaces in legal studies well into the new millennium. Tsesis (2002), for instance, investigates how harmful social movements feed on hate speech, and examines the destructive power of hate propaganda. More recently, Gelber (2017: 625) also privileges a view in which hate speech, “directed at historically identifiable minorities”, is taken as harmful “to their involvement in processes of democratic legitimation”. She explains that hate speech may “imperil a target’s ability to participate in the political decision making that affects them”. This, she notes, allows for a distinction between discriminatory hate speech and speech that may hurt people’s feelings and cause offence. Besides leaning towards the idea of harm, most legal theorists are also concerned with whether or not the actual prohibition of hate speech is defendable, in light of the overarching right to freedom of speech (e.g. Parekh, 2006; Weinstein, 2017). As Buyse (2014: 796) puts it, “from a human rights perspective, the right to life and the prohibition of discrimination are to be balanced against the freedom of expression”. But the paradox behind the legal restraint of hate speech is that it may turn out to be “a means to strengthen the individual’s [the hater’s] right to freedom of opinion and expression” (Ward, 1997: 785). The awareness of this counterproductive effect has led a few to defend that counter-speech is generally preferable to suppression of speech (Gagliardone et al., 2015). In social sciences and media studies, many scholars conceive of hate speech in terms of the discriminatory intentions it hides—an intent-­ based approach. Boeckmann and Turpin-Petrosino (2002: 209), for example, remark that perpetrators of hate speech intend “to wound and denigrate” their “objects of prejudice”. Nielsen (2002) also points out the malevolent intent of hate speech, defining it as a mechanism of subordination for creating an atmosphere of fear and intimidation. Recent sociological studies also focus on the prejudicial intentions underlying the

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discourse of hate. Harmer and Lumsden (2019: 2), for instance, define hate speech as a process of “online othering” that “encapsulates the myriad power contestations and abusive behaviours which are manifested on/ through online spaces”. Similarly, Udupa et al. (2021) conceive of digital hate speech as a mechanism intended to imagine, enact, and brutally enforce antisocial values around the world, and remark how such destructive behaviours have smothered the euphoria around the emergence of the Internet as a liberation technology. Political philosophers and ethicists have viewed the problem from the perspective of human rights and the fundamental concepts of justice and equality, often concentrating on the morally obnoxious nature of hate speech. Waldron (2012) famously questions America’s First Amendment’s defence of freedom of thought and speech, reflected in liberal slogans like “Freedom for the thought we hate”, by concentrating on the harm that expressions of hatred (e.g. racial hatred) do to “the groups who are denounced or bestialized”, and he eloquently frames the issue by asking the following rhetorical questions: “Can their lives be led, can their children be brought up, can their hopes be maintained and their worst fears dispelled, in a social environment polluted by these materials?” (Waldron, 2012: 9). Other political theorists have also taken up the harm-based approach. Cohen-Almagor’s (2013: 43) definition, for example, very comprehensively phrases the various, intensely pernicious effects of hate speech, some of which involve the role of bystanders in continuing the cycle of hate: “Hate speech is aimed to injure, dehumanize, harass, intimidate, debase, degrade, and victimize the targeted groups and to foment insensitivity and brutality against them” (italics mine). In computer science and NLP, research into online hate speech is particularly abundant, outweighing perhaps any other academic area, especially in the past decade. Although the centrality of a precise definition is key to devising analytical algorithms, most definitions available in the literature suffer from inaccuracy, owing to the notorious fluidity of the label ‘hate speech’ (Assimakopoulos et al., 2020). Davidson et al. (2017: 512) are well aware of the pervasive conceptual confusion in the area, which leads them to undertake an attempt to distinguish hate speech from offensive speech, and offer a definition of hate speech that merges content and intent: “Language that is used to express hatred towards a

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targeted group or is intended to be derogatory, to humiliate, or to insult the members of the group” (italics mine). The next section will review NLP definitional and annotation endeavours in greater detail, but for now the definition by Fortuna and Nunes (2018: 85|5) deserves mention owing to their pointed focus on the issues of stylistic variability and indirectness, which so often characterise hate speech: “Hate speech is language that attacks or diminishes, that incites violence or hate against groups, based on specific characteristics (…), and it can occur with different linguistic styles, even in subtle forms or when humour is used” (italics mine). In linguistics—the core perspective of this book—the  researchers’ expertise in language should make it easier for them to avoid the definitional problems mentioned above. But it is not uncommon to read papers by linguists who use disparate words synonymously and recurrently mistake hate speech for aggressive, offensive, insulting, and impolite speech. A very recent, and valuable, edited volume on the grammatical features of hate (Knoblock, 2022) bears the phrase “Hateful, Aggressive, and Dehumanising Discourse” in its subtitle. But a handful of linguists also subscribe to the need for clarification, or take a clear definitional stand as a point of order. Culpeper (2021), who has recently expanded his influential focus on offensive speech (Culpeper, 2011) to a refreshing approach to hate speech, states that a comparative examination of impolite, offensive, and hateful speech aims to “promote theoretical synergies, and also greater awareness of the labels that are used”, and he ventures, perhaps over-optimistically, that it seems to be “a commonality” of hate speech definitions that “they must target an individual or group on the basis of so-called ‘protected characteristics’” (Culpeper, 2021: 4–5). Yet, the purported “commonality” is not guaranteed. Baider’s (2020: 196) definition, for instance, does not raise the issue of protected characteristics, or the identity of hate speech targets: instead, she views hate speech as “a contextualised speech act that is part of a social process of alienation”, through which it “‘others’ its targets by creating in/out groups”. Who these targets are is open to speculation. Conversely, Guillén-Nieto (2023), in her comprehensive, exciting book on linguistic approaches to hate speech, does point out that only “people identifiable by legally-protected characteristics” are targeted by hate speech (2023: ix), and she accurately highlights

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the misleading nature of the phrase as a basic obstacle to interpreting it. The semantics of its constituent parts—“hate” and “speech”—is deceptive, she holds, because it may suggest that the phrase describes “a subcategory of speech associated with the expression of hate towards people in general”: yet, its use is not limited to speech or to the expression of hatred—nor, then again, are people in general targeted by it (ibid.). Consequently, she claims, “the meaning of hate speech is not a function of the literal meanings of its constituent parts”, but a sum of its “multiple meanings” (ibid.). The importance of linguistic insight to understanding hate is evident in the fact that linguists are called upon to provide technical support to digital companies in detecting online hate speech, particularly in feeding lexical, grammatical, and pragmatic input to machine learning tools. The bridge thus established between linguistics and computer science, briefly reviewed in the next section, concentrates on the content of hateful language, that is, the information it conveys and the ideas it verbalises. In this content-based approach to hate speech, “what matters is what it says about an individual or a group” (Parekh, 2006: 214).

2.2 Annotating Hate Speech The challenge of detecting instances of online hate speech, for purposes of monitoring and control, involves annotating data in terms of the presence of defining features. In machine learning, data annotation aims at making objects—for example, hate speech texts—recognisable to computers, training the system to understand and memorise the input patterns. By feeding the machine pre-labelled training data, that is, manually annotated positive and negative examples of the feature(s) to be identified, the computer can predict and detect the label when faced with a new instance. Most work by computer scientists, NLP researchers, and computational linguists on hate speech detection revolves around deciding upon the “best features” that can be used in text classification algorithms (Khan et al., 2020). But the task of determining whether or not a textual sequence contains hate speech may be daunting, given its heavy dependence on

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interpersonal context, its challenging language nuances and, as Nobata et al. (2016: 146) put it, “the noisiness of the data in conjunction with a need for world knowledge”. The difficulty is true not only for machines, which yield a high rate of false positives (Davidson et al., 2017) and, it should be added, of undetected true positives, but also for humans, among whom inter-annotator agreement tends to be low (Waseem, 2016). Assimakopoulos et  al. (2020) provide an enlightening critical summary of attempts to annotate Web 2.0 data for hate speech. The majority of these, they hold, involve a simple binary classification into hate speech and non-hate speech (±hate speech). Included here are Kwok and Wang’s (2013) detection of racist tweets and Burnap and Williams’s (2015) detection report for policy and decision making. Other models, however, attempt to refine the analysis by introducing a hierarchical element. For instance, Warner and Hirschberg (2012) tasked annotators with classifying texts on the basis of whether they constitute hate speech or not, but additionally with distinguishing between seven different domains of hate speech (e.g. sexism, xenophobia, homophobia). Similarly, Nobata et al. (2016), in their analysis of abusive language on Yahoo, requested annotators to classify, first, if a passage is “clean” or “abusive”, and then, if abusive, whether it contains (a) hate speech, (b) derogatory language, or (c) profanity. Yet another hierarchical annotation scheme is the one by Zampieri et  al. (2019), who asked annotators to code tweets on three levels: (i) on whether they contain offensive language; (ii) on whether the insult/threat has a target, and (iii) on whether the target is an individual, a group or another entity (e.g. an organisation or event). Last but not least, Franza and Fišer (2019) put forth an annotation model for “socially unacceptable discourse”, divided into so-called “type” (acceptable, background-­violence, background-offensive speech, other-threat, other-­ offensive speech, and unacceptable) and “target” (migrants, LGBT, journalists, media, commenter, other). All these annotation schemes can be regarded as “definitional”, that is, they all aim at identifying the hatefulness of a text by checking it for the presence of its defining elements, or the components that make up the concept of hate speech: whether it has a target (and if so, whether it is individual or collective), whether it has a specific hate domain or prejudice type (and if so, whether it is sexism, racism, homophobia, etc.), and

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so on. At the same time, such definitional annotation schemes also have a differentiation purpose, that is, they intend to distinguish hate speech from non-hate speech. The annotation model offered in the next section, which does integrate hierarchical features, shares these two characteristics—identification and differentiation—which makes it definitional, i.e., conceptual.

3 A Five-Factor Annotation Model for Hate Speech The premise of the present chapter is that hate speech happens when five factors co-occur, that is, occur cumulatively. They are taken to constitute, therefore, the necessary and sufficient conditions for the identification of hate speech. The additional differentiation purpose of this model requires hate speech to be distinguished from competing terms, in a conceptual structure that is hierarchically organised as regards a superordinate category. To this category we will now turn.

3.1 Hate Speech as Part of Antisocial Discourse Hate speech must be conceptualised against the continuum of verbal (and non-verbal, including multimodal) utterances that defy the tacit norms of social interaction. For the purposes of a classification model such as the one at hand, this continuum needs an encompassing, superordinate label, covering the large variety of phenomena which, as seen in the previous section, are frequently confused with hate speech. Nobata et al. (2016) use the label “abusive language” as a hypernym covering hate speech, derogatory language, and profanity. More interestingly, Fišer et al. (2017: 46) put forth the umbrella term “socially unacceptable discourse” (SUD) to comprise the whole range of negative language practices, “such as prosecutable hate speech, threats, abuse and defamations, but also not prosecutable but still indecent and immoral insults and obscenities” (see also Pahor de Maiti et  al., this volume).

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Vehovar and Jontes (2021) also adopt the SUD label to encompass “hateful and other negative communication” in online environments. However attractive, the SUD hypernym is not without problems. The idea that offensive and aggressive language in general is “unacceptable”, though intuitively correct, overlooks that each set of interlocutors and each community of speakers has their own norms. What is unacceptable to certain speakers may be perfectly acceptable to others. Consider profanity, for example. Although most speakers will find its use unacceptable in social interactions, certain groups may, and do, deem it otherwise. Youths are a case in point (Wachs et al., 2022): they regard swearing not only as acceptable, but as a desirable, and even necessary, strategy to fit in and belong to the group, although they may be well aware that such words are “inappropriate”—and refrain from using them in other social interactions. The same may be said about the issue of appropriation of discriminatory terms by certain groups, like  Blacks, women, and gays (as discussed above—see e.g. Brontsema, 2004; Warner & Hirschberg, 2012; Davidson et al., 2017). Therefore, a more neutral comprehensive label to serve as a hypernym is “antisocial discourse”. It comprises the whole spectrum of language practices that disregard the unspoken norms of social interaction, based on the essential principle of respect for the others and what they represent. More specifically, “antisocial discourse” covers, on the one hand, all forms of aggressive speech, including confrontational, abusive, impolite, offensive, and obscene language, and, on the other, forms of speech in which hate is directed at disadvantaged  groups or at individuals’ protected characteristics. In the present model, the former dimension shall be referred to as “aggressive speech”, the latter as “hate speech”. Other binary attempts to distinguish hate speech from other forms of antisocial discourse tend to use the term “offensive language” as the second term of the hyponymous dichotomy (e.g. Davidson et  al., 2017; Zampieri et  al., 2019). The reason why I prefer the label “aggressive speech” is that the emphasis falls on the sender of the hateful message, that is, the agent of the attack, whereas the adjective “offensive” highlights the effect on the receiver, that is, the victim, whose reaction, often falling outside language, is less amenable to treatment.

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3.2 The Five Factors Next are the factors which cumulatively constitute hate speech. They may be phrased as five questions, a positive answer to which signals a hit for hate speech. In foundational communication theory (Shannon & Weaver, 1948) the communicative act involves a sender (of a message), a coder, a channel, a receiver, and a decoder. The model, criticised for its simplistic, unidirectional view of a process that is, instead, dynamic, interactive, and creative, does give groundbreaking insight into the basic components of communication. The present model reshuffles, expands on, and adapts these components to the reality of online hate speech.

3.2.1 Content: Does the Message Express Prejudice? Hate speech utterances characteristically express prejudice. The centrality of the notion of prejudice for conceptualising hate speech is patent in the fact that the term pervades most research on the topic across disciplines (e.g. Boeckmann & Turpin-Petrosino, 2002; Cohen-Almagor, 2013; Kopytowska & Baider, 2017; Schmidt & Wiegand, 2017; Fortuna & Nunes, 2018; Sanguinetti et  al., 2018; Harmer & Lumsden, 2019; Culpeper, 2021; Paasch-Colberg et al., 2021; Woods & Ruscher, 2021). What is prejudice? The general, dictionary sense of the word is that it is a (negative) preconceived opinion which is not based on reason or actual experience. The word is often used to refer to bias, discrimination, partiality, and favouritism. Allport, in his milestone 1954 book, defines prejudice as “an antipathy based upon a faulty and inflexible generalisation”, which may be felt or expressed, and may be directed toward a group as a whole or an individual that belongs to the group (1954: 10). Pettigrew et  al. (1982: 3) shift from emotions to attitudes by defining prejudice as “irrationally based, negative attitudes against certain groups and their members”, involving “prejudgment and intolerance”. And Brown (2010: 8) advances the notion of disparagement and rescues that of antipathy: prejudice means “some state of mind, feeling or behaviour that involves some disparagement of others on account of the group they

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belong to, (…) which directly or indirectly implies some negativity or antipathy towards that group”. A related term to prejudice is stereotyping (see Bolton et al., this volume). Fiske (2000: 304), in her substantial comparative study of stereotyping, prejudice, and discrimination, conceives of outgroup stereotypes as cognitive “shortcuts to category-based information”, according to which differences between categories are accentuated, whereas differences within categories are minimised (i.e. categorised groups are viewed as homogeneous). Just like prejudice, stereotyping is a recurrent term in hate speech literature. Schmidt and Wiegand (2017: 8), for instance, discuss “stereotyped prejudices” against Muslims: an online comment like “Your goat is calling”, which is apparently harmless, actually involves an accusation of bestiality. Another important concept related to prejudice is political correctness, which goes largely unacknowledged in hate speech scholarship. Prejudiced discourse—and, by extension, hate speech—is, typically, politically incorrect. This means it deliberately flouts the language policies and measures that are intended to avoid offence, exclusion, and discrimination against groups of people seen as disadvantaged and marginalised. Although the term, shortened to “PC”, has gradually gained a negative reputation, being seen as an excessive and unwarranted intrusion into freedom of speech, especially since the advent of the so-called woke culture (Cammaerts, 2022), it is still operationally valid in discussing hate speech (on the semantic and cultural history of political correctness and its impact on public life; see, e.g., Hughes, 2010). In light of the considerations above, our working definition for prejudice is the following: “Prejudice is any form of preconceived and stereotyped opinion about (members of ) a group of people, which voices politically incorrect ideas that may negatively affect them”. Two further notes are in order. The first is that prejudice is the cornerstone that allows for a distinction between hate speech and dislike, disapproval, disagreement, criticism, and other forms of negative evaluation (Parekh, 2006; Paasch-Colberg et al., 2021), no matter how aggressively phrased. To say “Cretinous ideas like yours make me homicidal”, or “I’ve always hated know-it-alls”, is not hate speech, even if the speaker literally expresses hate. The second is that prejudice in an online comment

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does not make it a hate speech comment. Speakers may refer to the concept of prejudice, or talk about other people’s prejudiced opinions, for example by accusing, criticising, disagreeing with, or condemning them, or complain about being the victim of such opinions and about the hurt and harm they suffered as a result, but these are not occurrences of hate speech. Instead, they are reported occurrences of hate speech. For hate speech detection purposes, what matters is the first-hand expression, or production, of prejudice in online comments, that is, any situation in which the speaker actively (even if indirectly or implicitly) engages in the verbalisation of prejudiced content.

3.2.2 Target: Is the Message Aimed at (a Member of ) a Disadvantaged Group? Hate speech utterances characteristically target a disadvantaged group, or a member of such a group. The first disclaimer to make is that the choice of the phrase “disadvantaged group” acknowledges a myriad of competing terms, such as “vulnerable group”, “oppressed group”, “discriminated group”, “marginalised group”, “protected group”, and “minority group”, all of which carry semantic nuances, by emphasising one, or another, of the features that qualify the groups. For the sake of choosing a somewhat more neutral, operational term, the target of hate speech is here regarded as (a member of ) a group who has experienced some sort of social, political, economic, legal, historical, physical, or symbolic disadvantage because of belonging to such a group. The second disclaimer is that this view acknowledges the existence of both  “directed” and “generalised” hate speech (ElSherief et al., 2018): the former is aimed at specific individuals on account of their perceived protected characteristics (e.g. “Your a f*cking queer f*gg*t b*tch”); the latter is aimed at a group of individuals who share said characteristics (e.g. “I will not rest until every worthless n*gger is rounded up and hung”). What groups are these? Most accounts of hate speech, as seen above, consider its targets to be protected groups, that is, groups seen by the law as needing guarded status, or  shielding, due to a history of recurrent attacks. These groups tend to be the same as the ones listed under the hate

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crime category, even though hate speech may not be considered hate crime. In America, for instance, the FBI website states that hate crimes were traditionally limited to felonies “based on a bias against the victim’s race, colour, religion, or national origin”, but with the Hate Crimes Prevention Act of 2009 the Bureau became authorised to also investigate crimes “based on biases of actual or perceived sexual orientation, gender identity, disability, or gender”.7 These identity features are common to most hate speech target definitions. But some authors contest the list as too restrictive, and enlarge it to include other identity characteristics. Silva et  al. (2016), for instance, claim that “behavioural and physical aspects” should be included as hate speech targets, since “online hate speech may not necessarily be a crime, but still harm people”. Similarly, Fortuna and Nunes (2018: 85|7) discuss the difficulty in deciding what are, and are not, protected characteristics, and advocate a more flexible view in light of one example: “boys and men receive at an early age confining and stereotypical messages”—coming from family, peers, or, crucially, the media, “instructing them how to behave, feel, relate to each other, to girls, and to women”—which can be “harmful and have short- and long-term consequences for the boys but also for women, their families, their community, and society as a whole”. In the NETLANG project, we produced a list of identity features, which we called social variables (see Chap. 1), and which the present model integrates and aims at spotting. They are ten: gender, ethnicity, age, nationality, social class, religion, sexual orientation, gender identity, physical features (including disability issues), and behaviour issues (esp. drug abuse). In turn, each of these variables relates to a type of prejudice, respectively: sexism, racism, ageism, xenophobia, classism, religious intolerance, homophobia, transphobia, body shaming (including ableism), and addiction shaming. Perhaps the most original, or least common, of these are the last two, which belong in the flexible approach to the notion of protected features just mentioned—but obese people, in particular, and drug addicts seem to form two discriminated groups of their own (on body shaming, see Chovanec, this volume).  https://www.fbi.gov/investigate/civil-rights/hate-crimes

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Admittedly, this second factor in our model seems to follow logically from the first, since the concept of prejudice entails targets (i.e. prejudice against someone). Yet, not all prejudice has disadvantaged, underprivileged targets—and such cases need to be set apart from the category of hate speech targets. Consider, for instance, forms of prejudice against certain nationalities on the basis of well-known stereotypes, from the rather bland “Brits cannot cook”, or “Germans lack a sense of humour”, to the more serious “the Dutch do drugs” and “the Swiss are tax-­ fraudsters”. Expressing these ideas, prejudiced though they may be, is hardly likely to harm people of such nationalities, perhaps because of the economic and political status of their countries, ranking presently among the richest, most powerful, and most peaceful in the world, which has saved their nationals from becoming deprived migrants or vulnerable refugees—hence potential targets of discrimination by other nationalities. Similarly, consider the prejudice against men with small hands and feet being bad lovers: it does convey a negative, prejudiced stereotype, but does it affect men as a group negatively? Hardly, one might say, as males persist in being the dominant cohort in gender power relations, be they well-endowed lovers or not. Granted, the prejudiced comment on anatomical sizes may offend or annoy the recipient of the message by attacking his manhood, but it does not harm him on account of being a man, that is, insofar as he belongs to a certain, generically considered, male group. One last example that prejudice which targets advantaged groups does not inform hate speech is the stereotype connecting beautiful people with stupidity: the potential offence is not likely to be accompanied by actual harm, as the benefits of being good-looking in our appearance-­based society far outweigh this sort of prejudice.

3.2.3 Purpose: Does the Message Intend to Cause Harm? Hate speech utterances characteristically carry a harmful purpose, intending to discriminate, stigmatise, exclude, and marginalise individuals that belong to disadvantaged groups. Intention is a central question to theorising hate speech, but a notoriously complex one, first of all due to the difficulty in establishing what the sender actually intends to do or convey.

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Hate speech scholarship is accordingly laden with discussions of intentionality, foreseeability, and accountability (see Culpeper, 2021: 48–52), informing an illocutionary view of hate speech texts. Most intent-based approaches focus on the will to denigrate (Boeckmann & Turpin-­ Petrosino, 2002), subordinate and intimidate (Nielsen, 2002), and enforce antisocial values (Udupa et  al., 2021). All such intentions are directed at the targets, that is, the victims of hate speech. Yet, other approaches look at intention as being directed at other participants in the hate speech act: the interlocutors, or bystanders, who are, in a way, another type of target—the targets of incitement. As Langton (2018: 2) puts it, “another form of hate speech is propaganda”, in which its addressee is the “recruit to hatred”: such propaganda, she explains, attempts to “justify or promote hatred”, and to “incite discrimination, or even acts of violence”. The idea of harm is closely related to intent and also permeates the literature on hate speech, constituting the core of the effect-based approaches reviewed above (e.g. Walker, 1994; Tsesis, 2002; Parekh, 2006; Waldron, 2012; Cohen-Almagor, 2013; Sellars, 2016; Gelber, 2017; Weinstein, 2017). As Benesch (2014: 21) rightly remarks, hate speech should not be taken in terms of “subjective, individual offence”, but of “harmful consequence” in societal terms, insofar as it “can prevent members of minority groups from participating fully in democracy”. By delegitimising group members in the eyes of the dominant, privileged group, hate speech decreases their social standing and acceptance within society. At the same time, by means of fear, the process leads to silencing, a form of power annihilation (Houston & Kramarae, 1991). Indeed, disseminating negative representations of disadvantaged groups and spreading harmful ideological content contributes to asserting the dominant group’s hegemony (“our” group is better than “theirs”), while further disempowering already powerless outgroups, portrayed as the “other”. It should be noted that the idea of intention cannot be dissociated from that of effect. Haters intend to cause a harmful effect. Therefore, this third factor, “purpose”, merges together the illocutionary and the perlocutionary dimensions of the hate speech act. As Assimakopoulos (2020) puts it, writing about incitement, the speakers’ intention can only be defined if we consider their “intended perlocutionary effects, that is, the

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intention of the speaker to trigger a particular kind of response from some audience”. In Grice’s (1957: 385) original view, a speaker means something by an utterance if, and only if, the speaker “intended the utterance of x to produce some effect in an audience by means of the recognition of this intention”. In online hate speech, the audience includes both  the victim(s), whose intended response is fear and self-exclusion, and the larger audience, whose intended response is to join in the hatred. Of course, as Silva et  al. (2016: 688) point out, “it is hard to infer individuals’ intentions and the extent to which a message will harm an individual”, as both may sit outside the realm of language. Yet, the mere utterance of harmful content does indicate a choice to express it, even if the sender denies aiming at the pernicious outcomes it may cause. In other words, the focus of accountability should be on the intention exposed by the speech act, instead of the speaker’s actual intention. A related question is the accidental, involuntary expression of discriminatory content. Speakers may hold they did not intend to cause harm, but simply voiced ingrained antisocial meanings (on unintentional prejudice and unconscious bias, see, e.g., Moule, 2009). In such cases, the manual analysis of context may help decide whether the text is a case of harmful intent, or of clumsy, infelicitous emotional outburst, as happens with rants and occasional prejudiced expletives. Yet, there may still be reasonable doubt as to the purported innocence, or unaccountability, of the speaker.

3.2.4 Agent: Does the Sender of the Message Identify with a Dominant Group? Hate speech messages are characteristically sent by individuals who identify with a dominant group. Tracing this factor may be harder than it seems. Haters—like trolls and cyberbullies—typically hide behind made­up nicknames to accomplish their dark agenda (Woods & Ruscher, 2021). How are we to tell who they are, and whether or not they belong to this or that group? How can we unveil what gender they are, what ethnicity, nationality, age, religion? Role-playing and deception are so common in virtual communities (Donath, 1999, was aware of it at the

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beginnings of online communication) that the identification task seems hopeless. Yet, even if under false pretences, haters do give away identity information about themselves, especially when embedded in the way they use language (after all, hate speech detection has been compared to forensic linguistics—see Culpeper, 2021). A crucial point here is to assess whether the sender of the message “identifies with” a dominant group, not whether they actually belong to such a group. The first point  to make is that “sender” (sing.), designating a single person, as phrased in the question above, may be expanded to include multiple senders of one message, or one sender writing on behalf of a group. As Del Vigna et al. (2017) note, though most hate speech attacks are carried out by a single individual, they can also be managed by groups. Capturing the activity of such groups is one way of monitoring the production of online hate speech. Many studies have indeed focused on tracking down posts of identified hate groups and radical forums (e.g. Warner & Hirschberg, 2012; Burnap & Williams, 2015; Gitari et al., 2015). Another approach to handling hate speech agents is resorting to meta-­ information, which is easily accessed via the APIs of online platforms and can prove a valuable source in hate speech detection (Schmidt & Wiegand, 2017). Actually, having some background history about the sender of a post may be rather foretelling: a user who is known to write hate speech messages may recidivate. Xiang et al. (2012) successfully engage in this rationale to anticipate future hate speech messages. And Waseem and Hovy (2016) strive to detect the gender of senders, as men are regarded as being more likely to post hate speech messages than women (on this, see Pahor de Maiti et al., this volume). Now, the crucial point for the present model is to identify what Fortuna and Nunes (2018: 85|24) call “perpetrator characteristics and superiority of the in-group”—the perceived features of the senders, that is, who they are, or, rather, construct themselves as being—since such information may be a shortcut to confirming prejudice hypotheses. For instance, if the sender is a youngster, the probability that their invectives against the elderly are meant as harmful instead of, say, playful, is higher than if the speaker were an old person—which would make the said invective a good candidate for in-group banter and playful homophily. In the latter case,

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the expression of hatred may also be an instance of flame baiting, undertaken by fake personas and fabricated agents to provoke members of a group, even if the group is one’s own (Baker, 2001). Of course, the degree of influence that speakers have over the audience, their knowledge of the audience’s complaints and fears, which they may try to exploit, also play a key role in the success of the hate agent (Benesch, 2014). In short, this factor allows us to distinguish hate speech by an in-group against an out-­ group from aggressive speech as playfully used by in-groups against each other. A separate issue is that of prejudiced and stereotyped content being expressed by members of disadvantaged groups, or by victims of hate speech in a certain discursive sequence or conversation. Such reactive forms of hateful content, however, do not belong in the present model, which differentiates hate speech from counter speech. Certain forms of counter speech (see Bick, and Ruzaitė, this volume) do engage in discriminatory and disparaging strategies similar to the ones found in hate speech, but instead targeting (representatives of ) dominant groups. The present model places this specific form of counter speech outside the realm of hate speech proper. One last, lateral point to make is that senders lack ideological creativity, but they do boast linguistic creativity. Indeed, hate speech is a typically replicated or reproduced content, rephrasing well-worn prejudiced ideas, stereotypes, and clichés, all of which are symptomatically uncreative. Therefore, we cannot strictly speak of “authors”, since what they write is not original, but of agents, that is, those who willingly assume the role of reproducing and propagating those prejudiced ideas. Conversely, hate speech senders can be rather imaginative in disguising their discriminatory content. Actually, even though the so-called tech giants have taken care to establish strict anti-discrimination policies and employ ever more fine-tuned hate detection algorithms, haters keep finding new, ingenious ways to curb them.

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3.2.5 Channel: Is the Message Publicly Transmitted? Hate speech is public speech, hence publicly transmitted. In the case of online hate speech, the channel is the Internet, which multiplies its propagation intent  exponentially, on a planetary scale. This dissemination purpose makes private, person-to-person communication ineligible for the hate speech category: for instance, hateful content transmitted on a phone call, in private emails, or in face-to-face dialogues does not fit in our definition of hate speech. Hate speech intends to spread and diffuse prejudiced, harmful representations of disadvantaged groups as widely as possible, aiming to be read, seen, or heard by as many as the World Wide Web allows. That is why social platforms, with the huge audiences they reach, have become the preferred locus, and the Internet the preferred channel, for haters to freely act. Of course, there are other channels to convey hate speech. Waldron (2012) mentions offline mediums—namely pamphlets, billboards, and talk radio—where vulnerable groups are “denounced are bestialized”. Benesch (2014) adds traditional means of disseminating hate speech, such as the soapbox and the bullhorn, but also graffiti and speeches recorded on CDs. The CoE definition of hate speech also includes a reference to the various, multimodal, forms of expression which the discourse genre makes use of, including “images, cartoons, memes, objects, gestures and symbols”, which can be disseminated offline and online. Actually, hate iconography, including swastikas, the noose, or the burning cross (Langton, 2018), has a particular synthetic capacity to mean a great deal with no words at all. Be that as it may, online, verbal hate speech is the focus of the present model, whose five questions should be answered in face of a textual instance to classify. The ordering and numbering of the five factors does imply a certain degree of hierarchical importance, with “content”/“prejudice” being the most relevant feature of hate speech, and “channel” standing on a less salient position, all the more so because non-­ hate speech online automatically tests positive for public transmission as well. Next is a diagram (Fig.  2.1) summing up the five factors, and

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Fig. 2.1  Five-factor model for distinguishing hate speech from aggressive speech

corresponding questions, showing the differentiation between hate speech and aggressive speech. In a nutshell, if the answer to all five of the questions is affirmative, it equals a positive detection of hate speech. If, however, any of the answers is negative, the comment should be classified as aggressive instead of hateful. Indeed, aggressive speech differs from hate speech in at least one of the five factors: (1) content, in that it does not express prejudice, but a range of intellectual or emotional states, such as disagreement, rejection, protest, resentment, frustration, envy, dislike, and fear, among other possibilities, or reports on the expression of prejudice by other speakers; (2) target, in that it is not directed at a disadvantaged group, or a member of such a group, but at a certain individual’s idiosyncrasies, such as a person’s uninformed opinions, vanity, or callousness; (3) purpose, in that it does not deliberately aim at causing harm to certain targets, but may be non-­ intentional (when venting aggressive emotions or accidentally expressing prejudiced expletives), discursive/phatic (when using aggressiveness to silence the hearer or emphasise an idea), or identity-based (when using

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aggressive language markers to strengthen in-group membership); (4) agent, in that it is not produced by a self-appointed representative of a dominant group, but by anyone regardless of social identity, as long as their mood, personality, or circumstances of the utterance make them aggression-prone; and (5) channel, in that it is not necessarily transmitted publicly, and may instead be conveyed through private channels, including face-to-face communication. Once the five factors have been laid out and discussed, it is high time I gave my definition for hate speech, which incorporates them: Hate speech is any form of publicly transmitted content that directly or indirectly expresses negative prejudice against disadvantaged groups, or members of such groups, which is intended to cause them harm by promoting their discrimination and marginalisation, and which is produced by agents who wish to reinforce the supremacy of their own dominant group.

4 Applying the Model The first set of texts manually annotated for the presence of affirmative hits to the five factors was taken from the NETLANG subcorpus on ethnicity. Table 2.1 lays out four examples with yes/no labels for each factor: The first two comments qualify as hate speech since they both hit positive for all five factors. Example 1, “Moany Old Blacks Wanting To Be Of Part Of A Foreign Culture Its Sad. Truth Hurts”, is a deeply patronising two-sentence text, where the speaker resorts to the adjective *moany (moaning) to reduce the target (Black people) to the status of whining, hence worthless, foreigners. The noun, which elsewhere would be completely neutral, here assumes the derogatory quality of a slur, as in aliens, outsiders  and, definitely, outgroups. The use of “old” in “Moany Old Blacks” is a misleading affective term which hides irony, just like the falsely commiserative “It’s sad”. The use of the gerund “wanting to be” presupposes an ongoing, that is, unfinished and unsuccessful, attempt at “being” something, namely “part of a foreign culture”. The quotation of a cliché (“Truth hurts”) is one further exhibition of the agent’s purported

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Table 2.1  Five-factor annotation of NETLANG corpus comments on ethnicity

superiority, an arrogant attitude of someone who knows better, and is better qualified to enlighten “them”. By signing with the nickname “Allwhite”, the agent also openly identifies with the dominant white majority. The second comment also builds on presupposition: through the use of “still”, the agent implies that “they” (the quintessential othering pronoun) have been “causing trouble” for quite some time prior to the point of speaking. The use of a rhetorical question reinforces the implication that their causing trouble is a self-evident fact that dispenses with confirmation. The second sentence is an ingeniously indirect insult, one that would easily escape automatic detection algorithms, as no explicit slurs or hateful words are uttered. It resorts to pseudo-scientific knowledge, validated by the adverb definitely, to claim that the targets suffer from a genetic disorder and are therefore defective. Hence the potential harm underlying the comment: by depicting Blacks as trouble-makers and a faulty genetic group, the hate agent contributes to marginalising them further, and incites others to do likewise. Comment 3, on the other hand, does not hit positive for all five factors, which indicates it is not, therefore, an instance of hate speech. Even though the utterance “Whites have a lot of paranoia” does express a racist generalisation and a prejudiced stereotype (hitting positive for “content”),

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it does not aim at a disadvantaged group, but at white people (thus hitting negative for “target”). At the same time, even though it does attack white people, it is not likely to cause them any harm in terms of the perception of the overall community, which is another negative hit for “purpose”. As to the “agent” factor, it is also a miss, as the speaker uses an Asian nickname, which means that they do not identify with the dominant white majority. The comment is a good contender for the category of counter speech. Finally, the fourth example (“America why do you hate someone that has black skins? They help build this country for free 400 years”) is an even clearer no-case of hate speech. None of the factors get positive hits, except for “channel” (which of course is affirmatively labelled throughout the whole subcorpus, being as it is an online public speech corpus). Again, a rhetorical question, directly addressed at a non-human, but arguably metonymical, hearer—America—explicitly includes the verb “hate”, but in a non-agentive way, that is, the speaker is not the subject of the verb. This is a typical case of reported hate speech. The comment is uttered in response to a video on American racism, and it criticises other people’s hate and prejudice against Blacks, which the sender by no means endorses. Therefore, “content”, “target”, and “purpose” all come clean: the comment does not verbalise prejudice, nor does it target a disadvantaged group. Besides, no harm is meant; on the contrary, the comment aims at integrating Blacks into American society by presenting a favourable argument: their long-standing contribution to building the country. The second set of texts is taken from the NETLANG subcorpus on “gender”. Once again, a random set of comments was annotated according to the model, as follows (Table 2.2). The fifth comment, a glaring case of hate speech, is clad as a universal truth, a fact that admits no contending: “Women exist as taxi cabs for babies”. The prejudice the statement conveys resorts to the well-known motherhood stereotype, which reduces women to their reproductive function. As such, the “content” and “target” factors indicate an obvious case of male chauvinism, the expression of which, sharp as a knife, is based on a rather creative metaphor. The “purpose” factor is also a blunt attempt at disseminating the implication that females are worthless at any other function besides giving birth to children (note: the rearing

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Table 2.2  Five-factor annotation of NETLANG corpus comments on gender

function is omitted). The agent, who boasts a male name, reinforces the hate speech rationale of identifying with a dominant group—the powerful counterpart in the gender dichotomy. Similarly, example 6 hits positive for all five factors in a rather blatant way. Once again, a well-known stereotype against women is voiced: their alleged intellectual inferiority to men. This time, the speaker creatively phrases the idea through sarcasm, or understatement: “At least they managed to find a beach... on that island”. Context knowledge is required to understand the pronoun “they”: it designates a group of female contestants on a “survival-type” TV show. The fact that islands are never short on beaches humorously hints at the assumption that the women’s achievement is not exactly a difficult feat. As to the “agent” factor, the pronominal construction usefully gives away the speaker’s identity: the use of “they” implies a male “we”. Finally, the “purpose” factor exemplifies the speaker’s underlying intention to cause harm, in that he contributes to disseminating the implication that women’s stupidity is a fact, which could arguably prevent them from occupying relevant positions in society (see the incompetence stereotype re. women in Krook, 2020). The seventh comment is among the most hateful and insulting attacks found in the analysis: “Bitches r nothing but sperm rescepticles Human

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toilets hidden behind masks of mothers/sisters/daughters/wife/girfreinds”. At a lexical level, the use of scatological vocabulary, together with an outright swearword opening the comment, signals an extremely aggressive form of speech. But it goes beyond aggressiveness by hitting positive for all the factors constitutive of hate speech. The prejudiced content which the message conveys reduces women to their sexual function, by objectifying and dehumanising them. The “toilet” and “receptacle” metaphors are stylistic aids to the vilifying semantic construction of females as objects—recipients of bodily fluids and human waste, devoid of any qualities whatsoever (“they are nothing but”). Furthermore, the verb phrase “hiding behind”, together with the noun “masks”, suggests one further, classic, stereotype, the one that views women as devious and deceitful creatures. The harm inscribed in the comment yet again derives from the anti-women propaganda that denies females equal status in society. Comment 8, the last in the “gender” set, is a counter-example of sexism. Instead of voicing male chauvinism, the speaker tries to counter it. By explicitly addressing a male interlocutor (“Brutus Tan”), and thus hitting negative for disadvantaged targets, the commenter issues a negative judgement against credulous people that believe everything they see in the media: “Brutus Tan, to trust tv is the most incompetent intelligence, if you can even call it intelligence”. This, of course, is an assessment of stupidity, hence an aggressive (verdictive) speech act, which is further issued as follows: “You should get help to your hated opinion about ‘modern’ women”. Giving advice threatens the hearer’s positive face in suggesting that something is so wrong with the way he thinks that he should seek medical help. Aggressive (and impolite) though it may be, the comment does not voice prejudice (“no” for “content”), or try to harm the male group the hearer belongs to (“no” for “purpose”). The third, and last, set of texts to annotate is extracted from the NETLANG subcorpus on “age”. Table 2.3 lays out the positive and negative hits for the five defining factors of hate speech: The ninth comment (“I work at the supermarket and have to put up with these old crones”) contains two linguistic clues that signal prejudiced content against the elderly: the slur “old crones” and the phrasal verb “put up with”, which collocates only with negative complements.

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Table 2.3  Five-factor annotation of NETLANG corpus comments on ageing

The implication, of course, is that seniors obstruct public places, like supermarkets—a typical stereotype in ageist prejudice. It is not difficult to see how such comments can cause harm, insofar as they incite others to view the no-longer productive elderly citizens as a nuisance. This may well cause them to feel unwelcome and segregated in such places, and prompt them to stay home, a dangerous attitude in an age cohort that already tends to be isolated and lonely. As the speaker identifies with people of working age, that is, the dominant group age-wise, the “agent” factor gets a yes label, like all the other factors for that matter. Comment 10 ironically conveys the stereotyped idea that old people are useless at technologies. This prejudiced, ageist content is phrased in such a way that it ridicules the target. By invoking another (positive) stereotype, that “old people have more experience”, the speaker pretends to believe they are good at fixing broken mobile phones. The irony of the comment actually conveys the opposite idea, an idea of incompetence and backwardness which, once again, may prove harmful to an age group struggling to get by in an ever-changing world. Of course, by referring to “my grandpa”, the agent automatically identifies with young people, and by extension the dominant group age-wise.

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Unlike the first two, the next two comments on this subset are not cases of hate speech, not testing positive for all the factors. The eleventh comment does contain suspicious keywords in close vicinity, namely “old people” and “stupid”. Still, it is not a case of ageism, but just a report on ageism, where seniors are actually constructed as victims: “Many old people are thought to be stupid although they are so much more intelligent than adults”. The purpose of the comment is therefore to claim that the stupidity stereotype concerning the elderly is wrong. Similarly, comment 12 expresses no prejudiced content but positive advice to all of “us”, because “we all age”. By merging into the “ageing group”, the speaker does not identify with the young, powerful majority, but with the ageing, often segregated victims, trying to spread positive meanings about growing old.

5 Extending the Analysis From the set of twelve NETLANG corpus comments under analysis, only seven tested positive for hate speech according to the five-factor model. They were comments number 1, 2, 5, 6, 7, 9, and 10. Of course, this result yields no statistical validity, given the extremely reduced size of the sample, as well as the controlled nature of the dataset, which underwent a preliminary selection and was compiled as a (potential) hate speech corpus. Yet, the sample is productive in showing the operational potential of the model. Of course, this is a definitional model, as claimed above, not a descriptive one. To expand the analysis into other descriptive elements of hate speech texts, one would have to look for the features that concern the linguistic construction of the comments, namely, the lexical choices, the structural mechanisms, and the stylistic, semantic and pragmatic strategies used to convey prejudiced content. Some of these are explicit, hence more easily detectable by monitoring algorithms; others exhibit a more devious, hidden architecture. Table 2.4 lays out the linguistic features found in the corpus subsets under focus: The lexical features spotted in the comments at hand, the ones that were deemed hate speech texts, revolve around the use of keywords and

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Table 2.4  Linguistic features of NETLANG corpus comments on ethnicity, gender, and age annotated as hate speech Linguistic features Lexical Comment 1

Comment 2

Comment 5

Comment 6

Comment 7

Grammatical

Stylistic

Pragmatic

– Sarcasm – Threat to – Gerund: – Keywords positive face signals (Blacks, continuing, foreigners) ongoing – Cliché (Truth hurts) attempt, and lack of success – Misleading in completing affective an action term (old) (wanting) – Key phrase – Third-person – Rhetorical – Presupposition: question still presupposes pronoun (causing that an action (they): creates trouble) has continued a deictic – Intensifying for a long time distance from adverbs – Threat to speaker and (definitely): positive face audience convey – Verdictive authority illocutions (e.g. diagnosing), implying authority – Keyword (Irrelevant) – Metaphor – Implication: (Women) women should be denied any other function in society besides reproduction – Sarcasm (Irrelevant) – Cancellable construction (at least is cancelled by on that island) – Metaphor – Implication: – Swearword – Restrictive women’s only (b*tches) syntactic use is as sexual construction objects (are nothing but): annuls other predications (continued)

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Table 2.4 (continued) Linguistic features Comment 9

Lexical

Grammatical

Stylistic

Pragmatic

– Slur (old crones)

– Verb (put up with): collocates only with a negative complement (Irrelevant)

(Irrelevant)

– Implication: seniors obstruct supermarkets

– Irony

– Ridicule – Denial (I don’t want to be ageist) – Implication: seniors are technologically illiterate

Comment 10 – Keywords (grandpa, ageist)

recurrent phrases, slurs, clichés, proverbs, and swearwords. Some lexical hedges intensify the meaning of the comment, as is the case of “definitely”, whereas some affective terms are used in a misleading way (e.g. “old Blacks”). Grammatically, the use of pronouns (especially the personal pronoun “they”, but also the object pronoun “them”) conveys a distancing effect from the target, both emotionally and ideologically. Certain syntactic constructions convey other types of negativity, such as the use of verbs that only collocate with negative complements (“I put up with these old crones”), whereas others teasingly cancel previous predications (“At least they found a beach…on that island”), or restrict the range of possible predications (“B*tches r nothing but human toilets”). In stylistic terms, metaphor, irony, and sarcasm are recurrent throughout the corpus, together with rhetorical questions. All such devices are rich pointers to implicit meanings—and are a notorious headache for automatic approaches and all analytical attempts outside manual, contextualised treatments like the one undertaken above. Last but not least, a pragmatic approach to hate speech texts elicits a range of strategies that elude non-contextual language processing. The

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comments above illustrate how haters resort to presuppositions, implications, face-threatening acts, verdictive and directive speech acts, and discursive mechanisms such as ridicule and denial to transmit their prejudiced meanings. Yet, more extended, larger stretches of text will no doubt elicit many other linguistic recurrences. Of course, only a qualitative treatment of such instances, case by case, will produce reliable material for machine learning uses. Nevertheless, an annotation procedure along these lines may be productive in detecting different manifestations of these language elements.

6 Conclusion This chapter has discussed the state of the art regarding the identification of hate speech, which heavily depends on definitional accuracy. It began by discussing a pervasive tendency in hate speech scholarship for dismissiveness and imprecision when it comes to defining the phenomenon, which calls for a proper circumscription of its conceptual boundaries. Then, it offered an annotation model for its detection, despite pessimistic views clouding similar attempts, according to which the context-­ dependency of hate speech practices and the ever-evolving nature of computer-­mediated communication make any such attempts useless. Admittedly, establishing a definition is but one step in understanding such an evasive and mercurial concept as hate speech. So as to fully assess how it works, it is important to know the way it is negotiated by particular speakers in particular communicative situations, meant for specific audiences at specific points in time. But the complexity of the task should not dissuade academics, especially linguists, from refining the conceptual tools that will feed the design of successful detection algorithms. An open, interactive dialogue between different academic disciplines on the one hand, and between academia and society on the other—that is, between scholarly discourse and the legal, social, and economic agents that regulate and provide online communication  services—is therefore the only fruitful way to tackle challenges as overwhelming as hate speech.

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The contribution presented in this chapter is a proposed annotation framework, with only a selection of instances tested. For it to yield statistically significant data, a much larger scrutiny of existing datasets, like the NETLANG corpus, would have to be undertaken, with the help of manual annotators. The same goes for linguistic (descriptive) annotation, a sample of which is supplied above (Table 2.4): for computers to successfully spot hate speech occurrences, in terms of not only explicit but especially implicit content, massive repositories of examples have to be fed into the systems. This is doubtless an overwhelming prospect, but one that has gradually progressed with the help of combined techniques and tools, in a concerted effort that ultimately benefits us all, users as we all are of the Internet. A key point to bear in mind is that  any detection enterprise will be useless if the very concept of hate speech is to be hurriedly, or clumsily, drafted.

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Part II Structural Patterns in Hate Speech

3 Improving NLP Techniques by Integrating Linguistic Input to Detect Hate Speech in CMC Corpora Idalete Dias and Filipa Pereira

1 Introduction Hate Speech detection research relies heavily on automatic detection models that make use of machine learning (ML), opinion mining, sentiment analysis and polarity detection (Njagi et al. 2015; Rodríguez et al. 2019; Vidgen et al. 2020; Wanjala and Kahonge 2016). Although automatic models are key to current and future hate speech research, their optimisation requires the integration of more fine-grained lexical, syntactic, semantic and discourse analysis input. This study aims to demonstrate that this can be achieved by applying a mixed methods research I. Dias (*) Department of German Studies, School of Arts and Humanities, University of Minho, Braga, Portugal e-mail: [email protected] F. Pereira Department of Informatics Engineering, School of Engineering, University of Minho, Braga, Portugal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_3

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approach that integrates both qualitative and quantitative methods. Although there has been previous work on CMC (Computer Mediated Communication) corpus compilation and annotation on various linguistic levels to aid automatic hate speech detection (Bick, 2020; Davidson et al., 2017; Yang et al., 2011), the task of applying a mixed methods approach to identify and analyse fixed discursive patterns for hate speech detection has, to the best of our knowledge, not yet been addressed and evaluated. Our proposal focuses on opinion markers that exhibit a relatively high degree of fixedness and can act as pointers to hateful content. The highly informal and speech-like nature of CMC poses many challenges for electronic processing and automatic hate speech detection methods (Beiβwenger et al., 2016; Fišer et al., 2020; Nobata et al., 2016; Pereira, 2022; Vidgen et al., 2019), but also for linguistic studies on all levels of analysis, in particular the detection, annotation and analysis of discursive-pragmatic features and strategies used to express prejudice and hate. The chapter is structured as follows: Section 2 reviews previous related work on CMC hate speech corpora, opinion mining methods, hate speech classification approaches and challenges in automatically detecting CMC hate speech. Section 3 outlines the mixed methods research approach pursued. Section 4 provides an overview of the Portuguese-­ English CMC NETLANG corpus on which the study is based: the text selection and extraction processes and text classification according to prejudice types and corresponding social variables. Section 5 focuses on specific CMC text features that constitute obstacles to the three NLP text pre-processing phases, namely tokenisation, lemmatisation and part-of-­ speech tagging. Section 6 presents a detailed description of the fixed opinion markers that can act as pointers to expressions of prejudice and hate extracted from the comment threads categorised under the prejudice types ‘Sexism’ and ‘Racism’. For the purposes of this study, our definition of hate speech is based on the five-factor model proposed by Ermida (Chap. 2, this volume), who defines hate speech as a publicly transmitted message verbalising prejudiced and discriminatory content towards a disadvantaged social group or representative of such a group based on a particular identity trait with the intention of disseminating hate.

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2 Related Work This section reviews previous related work, taking special interest on the CMC hate speech detection and classification approaches currently available for evaluation purposes.

2.1 CMC Hate Speech Corpora The main goal of CMC hate speech corpora projects is to define and classify hate speech. The simplest classification model distinguishes between hate speech (‘Abusive’) and non-hate speech (‘Clean’) (Nobata et  al., 2016). Other models are more complex, such as the two-level schema developed to annotate the bilingual Slovene-English Frenk Corpus containing hate speech toward migrants and LGBT. The first annotation level distinguishes between ‘Socially Acceptable Discourse’ and ‘Socially Unacceptable Discourse’ (SUD). Comments that fall under the latter category are further classified according to the type of SUD and the targeted individual or group: Background—violence; Background—offensive speech; Other—threat; Other—offensive speech; Inappropriate speech in the case of an unspecified target (Fišer et  al., 2020, this volume; Ljubešić et al., 2019). Davidson et al. (2017), on the other hand, defined a three-category model to classify tweets: hate speech, offensive language, or neither of the two. Despite the specific goals underlying each project, the existence of a multiplicity of classification models highlights the fact that there is still no clear definition of what constitutes hate speech. The quest to understand the difference between hate speech and other types and degrees of offensive language necessarily entails investigating the respective linguistic distinctive features. This leads us to the importance of annotating CMC hate speech corpora with different levels of linguistic information. The Frenk Corpus was annotated according to the following five categories: Orthography, Lexis, Morphology, Syntax and Word Order (Fišer et al., 2020; Ljubešić et al., 2019). As demonstrated by the results of comparing the linguistic features of SUD comments with non-SUD comments, SUD comments reveal: (i) higher vocabulary richness; (ii) more non-standard properties and (iii) a higher frequency of

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informal syntactic structures. Another noteworthy project is the XPEROHS Danish and German Twitter corpus compiled for hate speech research that has been enriched with morphological, syntactic and the much less common but valuable semantic annotation (Bick, 2020, this volume). Semantic annotation performed within the XPEROHS Corpus will enable the extraction of animal and/or disease metaphors that are often used to express hate toward minority groups. These two projects highlight that high-level linguistic annotation, namely syntactic and semantic annotation, facilitates the automatic extraction of implicit expressions of hate speech. Our own study is motivated by the importance of annotating CMC hate speech corpora on an even higher level of linguistic input, namely the pragmatic-discursive level.

2.2 Opinion Mining Opinion Mining, also referred to as Sentiment Analysis, is an NLP text-­ analysis technique that identifies the sentiment (positive, negative, neutral) expressed by someone in a written text regarding a specific topic, product, etc. (Nasukawa & Yi, 2003; Yi et al., 2003). Opinion Mining is significantly used in marketing research and business intelligence. It is mainly employed to gather customer’s reactions and feelings towards specific products and services by analysing text reviews, with the goal of improving the services provided. Nevertheless, this technique has also shown potential in extracting people’s opinions, attitudes and feelings about issues of public interest, such as politics and health care services, but also towards a particular individual, community, social phenomenon or idea. There are two fundamental approaches to perform sentiment analysis and opinion mining with the aim of automatically generating (domain-­ specific) sentiment lexicons (Darwich et  al., 2019): the lexicon-based approach and the machine learning (ML) approach. The lexicon-based approach categorises the sentiments in a specific text according to a polarity scale (positive, negative or neutral) by employing semantic orientation lexicons. These lexicons can be created manually or generated automatically with dictionary-based or corpus-based

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methods. Dictionary-based methods take a small set of words in an initial lexicon and, with the help of online lexical resources, such as dictionaries or WordNet, expand the lexicon by adding the synonyms and antonyms of those words along with the general sentiment they express. An example of a dictionary-based method to automatically generate a sentiment lexicon is the qwn-ppv (Q-WordNet as Personalised Page Ranking Vector) method that relies on the lexical and semantic relations between words in the WordNet database with an emphasis on antonymy, similarity, derived from, pertains-to and also-see relations (San Vicente et al., 2014).1 As demonstrated by the authors, this method can be applied to other WordNet languages and outperforms other automatic methods used to generate lexicons. In turn, corpus-based methods are more context-­specific and rely on the co-occurrence of word patterns to assess the word’s sentiment, using the semantic distance between a word and a specific set of positive and negative words to determine sentiment polarity (Darwich et al., 2019). Machine learning approaches are frequently applied in sentiment analysis endeavours, by employing classification techniques, such as Naive Bayes, Support Vector Machines (SVM) and Decision Trees. Most projects perform sentiment analysis by applying one of these two approaches. However, ML approaches can also be combined with lexicon-based approaches. Yang et al. (2011) combined ML techniques with semantic-­ oriented approaches in order to identify radical opinions in hate group web forums. Messages from two extremist/hate forums were collected and pre-processed. Four different types of text features were defined and extracted as classification predictors and three classification techniques were performed on the datasets: Naive Bayes, SVM and Adaboost. The four feature categories defined were syntactic, stylistic, content-specific and lexicon features. Syntactic and stylistic features, such as POS n-grams, function words and vocabulary richness, are more generic and can be used in different analyses of social media text. To represent domain-­specific knowledge, content-specific features (named entities, noun phrases) were chosen. These three types of features arise from ML  San Vicente et al. (2014: 89–90) provide an overview of relevant dictionary-based methods used to both manually and automatically build polarity lexicons. 1

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approaches while lexicon features come from a semantic-oriented approach. These lexicon features (subjective/objective term lists, hate terms, etc.) are used to capture more terms pertaining to the expression of negative emotions, such as hate and violence. Yang et al. (2011) then compared the performance of the three classifiers with different feature sets. F1-scores2 improved with the addition of more feature types and SVMs outperformed the two other classifiers regardless of the feature set chosen. The lexicon features also significantly improved the performance of the classifiers. Wanjala and Kahonge (2016) aimed to go further than only identifying radical opinions, seeking to identify and investigate hate mongers and cyber criminals. Their goal was to create a platform that extracts social media comments, classifies them as positive or negative and provides tools for cyber forensics. After a URL is crawled and the respective comments are extracted and stored in a database, the text is pre-processed (tokenisation, text normalisation and POS tagging) and a Naive Bayes classifier is used to obtain the classification of each comment. The project aims to contribute to hate speech opinion mining research via the application of cyber forensics tools.

2.3 Hate Speech Detection 2.3.1 Hate Speech Classification Approaches The detection of hate speech has become imperative in our online-­ immersed society. In recent years, considerable classification efforts have been undertaken to mitigate the propagation of hate speech in online mediums, such as social media. The lack of sufficiently accurate hate speech detection and the proliferation of disinformation in online platforms has led to real-life attacks in discriminated communities, bringing attention and outrage from the media and public to the lack of effective and timely moderation employed by social media platforms. Consequently,  F1-score is an ML metric to measure the accuracy of classification models that combines both measures of precision and recall. 2

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many projects have attempted to solve this complex problem with varied approaches. Most approaches employ machine learning and deep learning methods, creating models to classify messages as hateful, offensive or clean. Other efforts use linguistic rule-based approaches with the help of lexicons. To better moderate its platform, Facebook designed an AI dubbed XLM-R (Conneau et al., 2019), which combines two distinct models: XLM (Cross-lingual Language Model) and RoBERTa (Robustly Optimised BERT Pretraining Approach). RoBERTa (Liu et al., 2019) is an adaptation of Google’s algorithm BERT (Bidirectional Encoder Representations from Transformers). BERT (Devlin et al., 2018) is pre-­ trained with bidirectional representations of unlabelled text where a masked language model is used in order for the BERT model to learn the specific masked tokens based on its left and right contexts. Facebook improved on this model by pre-training it for a longer time and with more data, and by adopting a dynamic masking pattern for the masked language model. After pre-training, the model is fine-tuned to complete the task of classifying posts with possible hate speech with the help of previous posts already identified on the platform. By incorporating RoBERTa with the cross-lingual language model, which was trained on 100 different languages, Facebook obtains a multilingual model with state-of-the-art performance in the understanding of texts in multiple languages. In another project that applies deep learning in order to classify prejudice, Vidgen et al. (2020) focused specifically on prejudice against East Asians amidst the coronavirus pandemic, collecting and classifying a 20,000-tweet dataset in four different categories: Hostility against East Asia, Criticism of East Asia, Discussion of East Asian prejudice and Non-­ related. Different contextual embedding models were tested, and RoBERTa proved to be the one that achieved better results. Other approaches use lexicons to collect potentially offensive and hate-­ related speech, and then apply sentiment analysis to filter these candidate cases (Njagi et al., 2015; Rodríguez et al., 2019). Sentiment analysis can also be combined with ML methods (Njagi et al., 2015), providing more information that will help the model categorise the possible hateful expressions. Other types of opinion mining techniques such as emotion

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analysis (Rodríguez et  al., 2019) and objectivity analysis (Njagi et  al., 2015), are also used to complement sentiment analysis. Sentiment lexicons, such as SentiWordNet (Baccianella et  al., 2010), are usually employed to obtain the polarity of the sentences. Njagi et al. (2015) apply the Valence Aware Dictionary for sEntiment Reasoning (VADER) (Hutto & Gilbert, 2014) which goes beyond just attributing polarities ranging from −1 to 1 to a list of words as is the case with SentiWordNet. Hutto and Gilbert (2014) create a human-validated dictionary which classifies the sentiment intensity of the words on a scale ranging from slightly to extremely negative or positive, taking into account punctuation, word capitalisation, degree modifiers and other semantic features. These specific semantic rules allow for a more dynamic and context-based identification of sentiment in phrases, especially for microblog texts where these features are more commonly used to convey the tone of the opinions expressed by the users. The employment of hate lexicons, such as HateBase, can also be combined with the creation of certain lexical and/or linguistic patterns in order to identify possible phrases habitually used in the expression of prejudiced and hateful views (Pereira, 2022; Silva et al., 2016). Even if this approach doesn’t detect all occurrences of hate speech present in a certain dataset, it helps to filter large amounts of potentially hateful comments and improved performance can be achieved via the application of further hate speech detection measures on the filtered comment set (Pereira, 2022). Nonetheless, the research in automatic hate speech detection is still relatively new and faces many challenges. Fortuna and Nunes (2018) found that research projects in this field have been increasing in the last few years but that the field is still in an early stage, with research scopes often being broader than just hate speech, focusing on cyberbullying or general abuse.

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2.3.2 Challenges for Automatic Detection of Hate Speech in CMC Automatic detection and annotation tools have mostly been optimised for well-formed written production. The highly informal and speech-like nature of CMC text productions have significantly contributed to major shortcomings in automatic hate speech detection methods and classification models (Beißwenger et al., 2016; Vidgen et al., 2019). Main challenges for automatic detection and electronic processing tools include word and sentence boundary detection due to non-standard use of punctuation, not clearly identifiable word boundaries and contracted forms. Analysing and describing linguistic irregularities, lexico-semantic features and discourse strategies that are characteristic of social media content generated by users is of paramount importance to improve the performance of NLP and ML tools. A phenomenon worth highlighting is the large amount of orthographic variation found in CMC texts that may be categorised as typographic errors, spelling errors due to ignorance or intentional deviations from the norm to obscure the true meaning of the expression or for emphatic purposes: the replacement of characters by asterisks or other typographical symbols, as in ‘YOU NPC’S seriously need to get your heads out of your @$$e$!’3; the repetition of characters in a word or phrase used as a means of emphasising the intensity of a specific emotion, as is the case of the interjection ‘argh’ in Example (1). Another important aspect to mention is the rapid evolution of language in social media that involves the creation of new words by employing different word-formation processes to convey a specific standpoint, opinion or attitude, as shown by the ad hoc formed words ‘Liebour’ (lie + Labour) and ‘Con-swerve-atives’ (a word play with the noun ‘Conservatives’ and the verb ‘to swerve’) in Example (1). Both these ad hoc derived lexical units are context-dependent and intricately linked to the user’s opinion of the Labour and Conservative Parties, respectively. As a result, prolific lexical creativity on social media networks poses problems for automatic

 All examples in the chapter are taken from our NetLang corpus and are reproduced as they appeared, including typographical errors, misspellings, and non-standard grammar. 3

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models that were trained on older datasets which don’t account for these new language characteristics (Vidgen et al., 2019). (1) Lack of representation and being governed by incompetent aspholes do my head in. Failure to report real news, removal of freedom of speech, aaaaarrrrrgggggghhhhhh! What choice do we have at election time? Do you want Liebour to hit you with a piece of 4 × 2 or the Conswerve-atives to belt you with a lump of 2 × 4? If you are an OAP commit to hate speach each and everyday and save on nursing home fees. In Example (2), the commenter, who is clearly anti-feminist, uses the word ‘wimmins’ that was adopted by feminists to avoid the use of the word ‘men’ at the end of the commonly used word ‘women’. Since the word ‘wimmin’ is not lexicalised and is not found in the dictionary resources, it is not recognised by NLP pre-processing tools. (2) Women are STRONG and capable....but they lack confidence?!?!?! So we’ve got to engineer society to satisfy every whim of the wimmins. Pathetic! The use of irony and sarcasm is an additional challenge for automatic methods as sarcastic comments and humorous remarks introduce ambiguity in the automatic models. It is often necessary to be aware of the context about the community and the user in order to understand the irony present in written text (Nobata et al., 2016). In some cases, even humans have difficulties in determining if a specific comment in written discourse is meant sarcastically or not.

3 Research Approach Our mixed methods research approach is focused on combining linguistic knowledge and qualitative analysis with the quantitative results obtained from NLP techniques (see our joint paper, Ermida et al., 2023). We started by carrying out a qualitative fine-grained linguistic and pragmatic analysis of comment threads categorised under the prejudice types ‘Sexism’ and ‘Racism’ in the NETLANG English subcorpus in order to

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identify specific pragmatic patterns that seem to act as pointers to hateful content. Having identified a number of fixed pragmatic cues that exhibit possible anaphoric and cataphoric relations to expressions of prejudiced opinion within the dialogic nature of user-generated CMC content, we had a look at the frequency of occurrence of each pattern in the selected subcorpora and in the entire English corpus. We then proceeded to the qualitative analysis of the behaviour of the most frequent patterns in their dialogic context, including anaphoric and cataphoric phenomena that may contribute to optimising automatic methods of hate or prejudiced speech detection.

4 Corpus Compilation The NETLANG Corpus is a collection of English and Portuguese CMC texts, more specifically of comments present in the comment boards of online newspaper websites and YouTube. The English subcorpus is composed of articles and comment threads originating from the newspapers Metro, Daily Express and Daily Mail, while the Portuguese subcorpus includes articles and comment threads from the newspapers Sol, O Público and Observador. NETLANG’s corpus contains 50.5 million words of which around 43 million pertain to the English subcorpus. YouTube is the biggest contributor to our corpus with close to 48 million words extracted. The selection of texts was performed by targeting news articles and videos that might incite hateful responses, increasing, in that way, the probability of capturing potential occurrences of hate speech. Prior to the text extraction process, the NETLANG project compiled a table of keywords and expressions, matching them to specific social variables and the respective prejudice type associated with the word or expression. The web scraping process resulted in a dataset in JSON output format composed of (i) the full newspaper article texts and the YouTube post texts that originated the comments and (ii) the multiple posts that constituted the entire threads available on the date of extraction. In addition, an array of metadata related to the newspaper article, YouTube post text and individual comments was also retrieved, including article or video

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Table 3.1  Total number of occurrences of opinion markers in the racism and sexism subcorpora

Patterns

Occurrences (English Corpus)

(a | you) 4537 bunch of ((ADJ) + NN) if you think 2562 (x) people 2313 like you ((if | whether) 552 you) like it or not | like it or not

Occurrences (racism only subcorpus)

Occurrences (sexism only subcorpus)

Occurrences (sexism and racism subcorpus)

1059

740

1465

490 524

526 427

919 972

145

107

208

title, date of publication of the article, post text and comments, extraction date, number of likes, article URL and the most frequently occurring keywords in the comment thread (Henriques et  al., 2019). The extracted comment threads were then automatically classified according to the social variables defined using the keyword analysis tool NetAC (Elias et  al., 2021), a statistical framework which applies the keyword table to assess the most frequently occurring keywords in the text and consequently assign the corresponding social variables. It is important to point out that comment threads may be categorised as belonging to more than one prejudice type, as is often the case with racism and sexism (Table 3.1).

5 Pre-Processing The corpus compilation phase was followed by a fundamental set of text pre-processing operations that facilitate annotation and analysis of linguistic phenomena in electronic texts, namely tokenisation, lemmatisation and part-of-speech tagging.

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5.1 Tokenisation The pre-processing pipeline starts with recognising sentence boundaries and performing tokenisation that refers to the division of the input string, in this case, the full comment text, into tokens based on whitespaces, punctuation at the beginning or end of words or specified delimiters to be further analysed individually. A token can represent a word, a punctuation mark or an emoji. Example (3) displays language-specific and user-generated content features that pose a problem for tokenisation: (3) Trump hasn’t caused a nuclear war yet so id say hes doing okay. Aoc wants america to be socialist so i dont like her. Whos gonna foot the bill for free healthcare. The united state has a ton of debt, if the governed cant pay off the debt how are they gonna pay for free healthcare. The Somalian politician said 911 was some people doing stuff. She hates isreal. Screw her. Dont know much about the others other then the African american one is using her race as a shield. Obama never did those things when he was president. He used his talents which had nothing to do with race. In example (3), standard contracted forms ‘I’d’ (I would), ‘he’s’ (he is), ‘don’t’ (do not), ‘who’s’ (who is) and ‘can’t’ (can not) lack the apostrophe and will be interpreted by the tokeniser as one token when in fact these forms represent two tokens. As a result, common non-apostrophe variants in user-generated CMC content constitute an obstacle for higher-­ level NLP tools, such as lemmatisers and POS taggers that are not always able to effectively deal with ambiguous tokens, as is the case with ‘id’ that can be recognised as the contracted form ‘I’d’ or the noun ‘ID’ referring to a form of personal identification. Additional problems arise with informal contracted forms such as ‘gonna’ that is a widely used variant for ‘going to’. For specific linguistic analyses this token is required to be treated as two tokens. Another important aspect to mention is that defining punctuation marks as sentence and word delimiters may lead to tokenisation errors due to the multiple functions they perform. For example, periods also appear in abbreviations and ellipses that should both be interpreted as

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one token. Ideally the tokeniser should identify the periods in the abbreviated form ‘P.C.’ (politically correct) in example (4a) as part of the abbreviation. Example (4b) illustrates that in CMC communication ellipses may also be represented by commas and be composed of more than the three standard punctuation marks. (4) a. well stated brother. Well said! These P.C. warriors are outta control and gonna cost some lives. Glad I’m at the tail end of an over twenty year career. Transgendered is the latest change coming. And coming quickly. b. ISRAEL,,,,,,,,,,,,,,,,,,,,,,,,,why? bec GOD almighty is on there side no one can argue period Multiple adjacent punctuation symbols as represented by the shrug emoticon in example (5a) should be rendered as one token so as to preserve the meaning behind the emotion icon. Emojis are also a common non-verbal communication feature of CMC that may take various forms to convey affective states, intent, make opinion statements, as in example (5b), etc. Since the repetition of the same emoji in a sequence reinforces the opinion expressed, the entire sequence should be considered one token, instead of the seven tokens rendered by the tokeniser. (5) a. 2:09 Funny how the girl who prefers women in EVERY possible way, cuts her hair like a man \\_(Д)_/¯ b. shes an ugly peice of At this point, it is important to emphasise that the quality of the output of the tokenisation process will have a direct influence on the results of the subsequent processing steps.

5.2 Lemmatisation The second phase of the pre-processing pipeline is lemmatisation. After dividing each sentence in its individual parts, each token is then lemmatised. This dictionary-based process that takes into account the

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morphological features of a word consists in removing the inflectional endings of the input word, returning it to its lemma or base form. A lemmatised corpus is of considerable use in linguistic studies as it allows users to: (i) search for the base form of a word and obtain as a result all its inflected and derived forms and (ii) carry out frequency and distribution counts. Performing a search query in our corpus to search for occurrences of lemma ‘kill’ used as a verb retrieves instances containing inflected forms of the verb as shown in Example (6). (6) a. Of corse the muslim women don’t have “hate”....they would be beaten up or killed if they had an opinion or spoke up, like most oppressed people do. b. I would pay a hit man £1000 for every feminist he kills c. It’s pretty obvious why we prefer older women. This third wave feminism has killed the chances of getting with any younger girls. d. Always kills me when gay dudes talk about how hetero sexual men treat women. If we are so bad at the job why don’t you all step in? In examples (6a) and (6b) the verb ‘to kill’ is used in its literal sense, as opposed to its metaphorical sense in Examples (6c) and (6d). The verb-­ noun collocation in (6c) ‘kill the chances’ followed by the phrasal verb ‘to get with’ in the gerund, meaning ‘to eliminate the possibility of something happening’, exhibits a fixed lexical and syntactic pattern that can be used to extract all the instances of this verb-noun construction. This is also the case of the hyperbolic expression in (6d), ‘(It) always kills me’, normally followed by the conjunction ‘when’, meaning that the user finds the situation described preposterous. The lexical and syntactic fixedness of expressions can be exploited by NLP resources to perform higher-level linguistic annotation, including metaphor annotation. Given the prevalence of misspellings in CMC texts, it is important to point out that this feature results in the incorrect lemmatisation of the word in question, as is the case in Examples (5b) and (6a), with the incorrect spelling of the noun ‘piece’ (‘peice’) and the second element of the fixed expression ‘of course’ (‘corse’). When it comes to intentional misspellings, as is the case of the word ‘Liebour’ in example (1), in which the

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first part of the word is strategically replaced by another to convey the user’s opinion, running a spellchecker that compares ‘Liebour’ with correctly spelled words in a large dictionary and uses algorithms, such as approximate string matching algorithms, to correct its spelling to ‘Labour’ will distort the intended meaning the user wishes to express.

5.3 Part-of-Speech Tagging The third phase entails automatically assigning each token with a part-of-­ speech tag according to its morpho-syntactic properties: noun, verb, adjective, adverb, etc. Example (7) shows the result of POS tagging the tokens in the comment ‘@[USERNAME] i dont think you can compare pregnancy to lung cancer tbh’ using the FreeLing POS tagger and respective tagset.4 (7) @ (punctuation) [USERNAME] (common noun) i (personal pronoun) dont (common noun) think (verb, present tense) you (personal pronoun) can (modal verb) compare (verb) pregnancy (common noun) to (particle) lung (common noun) cancer (common noun) tbh (common noun). (punctuation) The tagger was not able to classify the non-standard contracted form of ‘dont’ (do not) and the informal abbreviation ‘tbh’ (to be honest) correctly. As clearly demonstrated, non-canonical spelling and non-­lexicalised abbreviations complicate the POS tagging task. The POS tags are assigned based on the probability of a particular tag occurring. In the case of ‘tbh’ (Fig. 3.1), the first POS analysis carried out indicates that there is a low probability of the token ‘tbh’ being a noun (NN). In a second iteration, the tagger classified the token as an adjective (JJ) with an even lower likelihood of occurrence. The results of these automatic processes add a layer of linguistic input to the text of each comment which allows the search of specific combinations of words/lemmas and POS tags. In fact, the annotated comment  http://nlp.lsi.upc.edu/freeling/

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Fig. 3.1  Pre-processing result for tokens ‘dont’ and ‘tbh’

threads that resulted from this pre-processing enabled the creation of a query engine, SAQL (Pereira, 2022), in which one can search for lexical and morpho-syntactic patterns in the corpus.5

6 Using Linguistic-Pragmatic Patterns to Detect HS Although there are many studies on CMC corpus compilation and annotation at various linguistic levels to automatically detect hate speech (Bick, 2020; Davidson et al., 2017; Yang et al., 2011, to mention just a few), little mixed methods research has focused on the identification of pragmatic patterns to detect hate speech in user-generated content. We address this gap by qualitatively identifying and analysing fixed pragmatic patterns that constitute potential pointers to hate speech. In this process, we took a subset of comment threads categorised under the prejudice types ‘Sexism’ and ‘Racism’ and manually identified a number of opinion markers that seemed to point to prejudiced and discriminatory content.  The query engine is freely available for research purposes at http://netlang-corpus.ilch.uminho. pt:10400/ 5

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We then carried out an absolute frequency count of these markers in these specific subcorpora and extracted the candidate comment texts for further analysis. We focused on linguistic and contextual aspects of a subset of those instances that express hateful and discriminatory opinions. The results of this qualitative analysis were used to aid the sentiment polarity analysis that was run on the candidate comment texts containing instances matching the regular expression (a | you) bunch of ((ADJ) + NN).

6.1 Linguistic Pattern Identification Whilst analysing the subset of comment threads categorised under the prejudice types ‘Sexism’ and ‘Racism’, we picked up on the following opinion markers that exhibit a certain degree of fixedness and seem to behave as potential pointers to hate speech: –– Expressions containing the noun ‘bunch’ in the pattern (a | you) bunch of ((ADJ) + NN), as in ‘a bunch of primates’. –– The expression ‘If you think’, as in ‘If you think millennials are bad,wait till they raise their kids 😂😂 kill it before it breeds.’ –– The expression ‘(x) people like you’, as in ‘Black people like you are racist. Stop blaming white people because you are racist’. –– Fixed phrases ‘((if | whether) you) like it or not’, as in ‘Amongst our specie,Men are meant to lead ,like it or not’. Table 3.1 shows the total number of occurrences of the above-listed patterns in the ‘Racism Only’, ‘Sexism Only’ and ‘Sexism and Racism’ categorised subcorpora6 and in the entire English corpus. The pattern matching the regular expression (a | you) bunch of ((ADJ) + NN) is the most frequently occurring pattern with a total of 4537 instances in the corpus. It should be pointed out that 71.9% of the pattern occurrences are found in the racism and sexism categories. The patterns ‘if you think’ and ‘(x) people like you’ are relatively comparable in terms of their  The ‘Sexism and Racism’ category refers to files that have been identified as containing both prejudice types. It is important to point out that the files categorised as ‘Sexism Only’ and ‘Racism Only’ in our study may also contain other prejudice types. 6

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distribution in the categories ‘Racism’ and ‘Sexism’ representing 75.5% and 83% of the total occurrences, respectively. The pattern ‘((if | whether) you) like it or not’ appears a total of 552 times in the corpus with 83.3% of instances in the subcorpora. In the following section, we provide a detailed analysis of instances matching the patterns in Table 3.1 based on the mixed methods approach.

6.2 Analysis of Results 6.2.1 Pattern 1: (a | you) bunch of ((ADJ) + NN) A qualitative analysis of a subset of the total instances containing the word ‘bunch’ highlights the importance of taking into account the surrounding context when evaluating expressions of hate speech. Consider the following examples taken from the subset: (8) a. By allowing the Muslims to march, pray wherever they want and slaughter animals in our streets like the bunch of Neanderthals they are the police are failing OUR community! b. Even a century after the UK first gave female citizens the vote, feminists are still holding each other back by jealousy and attacking each other like a bunch of alley cats. c. Well, you know, if your video is just a bunch of stats bundled up and not especially interesting, at least make sure to not assume that correlation implies causality... d. You just said a bunch of nothing. Don’t preach your ancient fairy tales to me. Having identified instances matching the regular expression (a | you) bunch of ((ADJ) + NN) that target a specific group in a prejudiced and hateful form, as in Examples (8a) and (8b), and other instances that are not considered hate-related speech, such as (8c) and (8d), we conducted an automatic sentiment polarity analysis using SentiWordNet on the total occurrences of Pattern 1 in the entire corpus with the two-fold aim of (i) obtaining an overview of the number of these instances that are

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Fig. 3.2  Polarity D=distribution for instances of Pattern 1

assigned a negative connotation and (ii) understanding the behaviour of sentiment polarity analysis tools. As will be demonstrated by the sentiment polarity analysis carried out with Pattern 1, the automatic assignment of polarity based on polarity lexicons presents some shortcomings. The results in Fig. 3.2 show that 87.7% of the total instances of Pattern 1 are assigned a negative polarity, in contrast to the number of instances assigned a neutral polarity (5.5%) and a positive polarity (6.7%). The very large difference between the negative and the other polarity values seems to indicate that Pattern 1 can function as a pointer to prejudiced and discriminatory content. A closer qualitative analysis of the results reveal that there are instances that were assigned a negative polarity that should be classified as neutral or positive. There are also cases of neutral comments that bear negative connotations and positive comments that are in effect negative. SentiWordNet operates on the basis of annotated WordNet synonym sets (synsets) that are classified according to the three degrees of sentiment polarity (positive, negative and objective) of the terms that form each synset (Baccianella et al., 2010). Words with different senses, as is the case with the noun ‘bunch’, appear in more than one synset, each with its own polarity scores. The assignment of a polarity score for an instance matching the regular expression (a | you) bunch of ((ADJ) + NN)

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results from the sum of the polarity scores of the words that compose the pattern. This process is exemplified in Example (9): (9) Anyone else think that the parents in this show don’t give a shit about their kids and are using them to make money like a bunch of degenerate pieces of fucking garbage? The noun ‘bunch’ has been assigned a negative polarity score of 0.125. Since the word ‘degenerate’, which was wrongly classified as a noun by the POS tagger, has a positive polarity of 0.125, these two values cancel each other out, resulting in a neutral polarity assignment. As can be seen, errors in the pre-processing phases have implications for higher-level operations. A qualitative analysis of Example (9) clearly identifies the instance as bearing a negative sentiment. This analysis also demonstrates the importance of sentiment analysis systems taking into account larger units of meaning and consequently larger chunks of discourse in order to improve their performance. Example (10) illustrates the difficulty sentiment analysis tools have when dealing with words that are not lexicalised or words that are not contained in the dictionaries used to train them, as is the case of the word ‘conspiritards’, a blend composed of the words ‘conspirator’ and ‘retard’. (10) I’m looking in and I see a bunch of schizophreniec conspiritards thinking the cops are making black people look like criminals. Given the above, SentiWordNet is not able to assign the words ‘schizophreniec’ and ‘conspiritards’ a polarity score. Due to the misspelling of the first word and the absence of the second in the dictionaries, SentiWordNet does not take them into account when calculating the sentiment polarity of the sentence(s). This is a shortcoming of sentiment analysis, since these unrecognised words combined with ‘bunch’ convey the main opinion of the user. Once again, a qualitative analysis makes clear that this comment expresses a negative prejudiced opinion. Moving on to analyse the other three patterns in Table 3.1, it is important to highlight that the pattern (a | you) bunch of ((ADJ) + NN): (i) contains the collective noun ‘bunch’ that functions as an anchor in a WordNet structure that facilitates automatic hate speech detection and

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(ii) has a specific syntactic construction that can be easily queried in a POS-tagged corpus. As will be demonstrated in Sect. 6.2.2, automatic hate speech detection becomes a very difficult task when the above conditions are not satisfied.

6.2.2 Remaining Patterns: if you think, (x) people like you, ((if | whether) you) like it or not In contrast to Pattern 1, the fixed expressions if you think, (x) people like you, ((if | whether) you) like it or not do not contain a content word that can aid in automatic hate speech detection. As demonstrated by Examples (11) to (13), separating each of the words in the expressions into tokens destroys the potential of these opinion markers operating as pragmatic cues to hateful content. For this reason, these fixed expressions should be treated as a unit and labelled accordingly. Automatic hate speech detection models, which normally make use of sentiment analysis and polarity detection, should consider incorporating pragmatic-based features, such as fixed opinion markers in their approach. Another key factor that poses problems for automatic hate speech detection, specifically when handling opinion cues, is the very fragmented nature of CMC interaction due to sequential incoherence (Herring, 1999). The opinion cues in examples (11) to (13) serve as anaphoric and/ or cataphoric anchors to stereotypical, toxic and racist opinions towards black people. (11) I hate how blacks think we owe them something because we enslaved there ancestors over a 100 years ago. I see this all of the time!! I think of it as where would blacks be if we hadn’t enslaved them and brought them to America. They would be in Africa swating flies off of there face. If you think we are so racist here you can go back to Africa to be with your own race!!!! (12) i love being white. i’m proud of being white and i hate anyone who hates white people... including white people like you who hate white people. anyone who’s against whites is my enemy. that means YOU are my enemy. you’re too stupid to realise that negroes hate

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white people. that stupidity may end up getting you badly hurt one day. don’t be looking for any sympathy from whites when it happens. (13) Whether you like it or not, BLM is just a sorry excuse to get black supremacy and sadly you’re buying into it. Seriously if the word “hypocrisy” could take a physical form it would be BLM. The expressions ‘if you think’, ‘(x) people like you’, ‘((if | whether) you) like it or not’ in the examples refer back and forth to the comment(s) of (a) previous user(s) and refer back and forth to the commenter’s own opinion. NLP models are not able to effectively deal with complex behaviour of anaphoric and cataphoric referencing in CMC texts to capture hateful content. A linguistic analysis of the content and formal syntactic properties of the utterances initiated by the opinion markers ‘if you think’ (11) and ‘Whether you like it or not’ (13) reveals a certain degree of fixedness that can be formalised: ‘If you think (that) x, (then) y’ and ‘(Whether you) like it or not, x’. Notwithstanding the drawbacks associated with non-­standard features of CMC content (e.g., lack of punctuation), it would certainly be interesting to explore how automatic detection models handle fixed opinion cues whose content structure can be formalised and their contribution to hate speech detection in conjunction with other resources. Detecting hateful content automatically in comments characterised by an extensive argumentative mode, as is the case of Example (12), is an extremely difficult task. A more fine-grained qualitative analysis of the lexical, syntactic, contextual and pragmatic features of the comment is required.

7 Conclusion In our attempt to demonstrate how linguistic input can improve NLP techniques applied to CMC Corpora in order to detect Hate Speech, we started by conducting a qualitative analysis of a subset of the texts present in the NETLANG Corpus categorised according to the prejudice types ‘Racism’ and ‘Sexism’ with the aim of identifying pragmatic patterns that can serve as cues to prejudiced, discriminatory and hateful content. In

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this process, we observed the occurrence of fixed opinion markers, namely (a | you) bunch of ((ADJ) + NN), if you think, (x) people like you, ((if | whether) you) like it or not, that seemed to operate as anaphoric and cataphoric cues to hateful speech. Subsequently, we extracted all instances of the identified opinion markers in the above-mentioned subcorpora and analysed a small set of the instances qualitatively focusing on their potential to effectively point to hateful, prejudiced and discriminatory opinions in the comment. As demonstrated by the results of our study, the selected opinion markers do serve as pointers to prejudiced or toxic opinions. The lexical and/or syntactic fixedness of the marker facilitates automatic hate speech detection. Identifying hate speech using opinion markers found in long argumentative comments has proven to be a difficult task. As shown in various examples from the corpus, certain characteristics of CMC texts pose challenges to automatic methods and result in errors that have consequences for the entire NLP pre-processing pipeline. Identifying and understanding these characteristics is crucial to fine-tune pre-processing tools and improve the results for higher-level processing tasks. Enriching the NLP tools with linguistic input, such as new terms, new variants and neologisms, is necessary for a more accurate attribution of POS tags, and consequently lead to a more precise extraction of the fixed patterns that will later be analysed and submitted to other automatic NLP methods. Our study highlights the importance of applying a mixed methods research approach that integrates both qualitative and quantitative methods. The fixed patterns analysed in this study would have been difficult to identify in the corpus if we had not started exploring the data from a qualitative perspective. The identification of fixed patterns that function as opinion markers and a pragmatic analysis of their anaphoric and cataphoric behaviour in CMC content constituted a valid approach to understand aggressive, hateful and prejudiced opinion in CMC, thus demonstrating the importance of integrating pragmatic-discursive knowledge in hate speech detection and extraction. ML techniques and NLP methods, such as content-specific lexicons and semantic corpus-­ based approaches can greatly benefit from this knowledge.

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Silva, L., Mondal, M., Correa, D., Benevenuto, F., & Weber, I. (2016) Analyzing the targets of hate in online social media. In Proceedings of the tenth international AAAI conference on web and social media (pp. 687–690). The AAAI Press. Vidgen, B., Harris, A., Nguyen, D., Tromble, R., Hale, S., & Margetts, H. (2019). Challenges and frontiers in abusive content detection. In Proceedings of the third workshop on abusive language online (pp.  80–93). Association for Computational Linguistics. Vidgen, B., Hale, S., Guest, E., Margetts, H., Broniatowski, D., Waseem, Z., Botelho, A., Hall, M., & Tromble, R. (2020). Detecting east Asian prejudice on social media. In Proceedings of the fourth workshop on online abuse and harms (pp. 162–172). Association for Computational Linguistics. Wanjala, G.  W., & Kahonge, A.  M. (2016). Social media forensics for hate speech opinion mining. International Journal of Computer Applications, 155(1), 39–47. Yang, M., Kiang, M., Ku, Y., Chiu, C., & Li, Y. (2011). Social media analytics for radical opinion mining in hate group web Forums. Journal of Homeland and Emergency Management, 8(1). https://doi.org/10.2202/1547-­7355.1801 Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Third IEEE international conference on data mining (pp. 427–434). IEEE.

4 First-Person Verbal Aggression in YouTube Comments Ylva Biri, Laura Hekanaho , and Minna Palander-­Collin

1 Introduction Verbal aggression is regarded as one of the downsides of the participatory culture of online social media platforms as it has both individual and societal repercussions and may contribute to harmful and criminal phenomena such as cyberbullying, hate speech, cyberterrorism and even war (e.g., Bilewicz & Soral, 2020; Chetty & Alathur, 2018; Hamilton, 2012). Especially sites allowing users to comment on posts by other users anonymously, such as YouTube, are often described as environments prone to aggressive and derogatory communication (e.g. Brown, 2018). Earlier

Y. Biri (*) • M. Palander-Collin University of Helsinki, Helsinki, Finland e-mail: [email protected]; [email protected] L. Hekanaho University of Helsinki, Helsinki, Finland University of Tampere, Tampere, Finland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_4

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research on hateful language on these sites has focused, for instance, on individual words and lexical co-occurrence patterns that could be used to detect hate speech automatically (see, e.g., Tontodimamma et al., 2021), but our interest in online verbal aggression stems from a somewhat different research perspective. We are interested in how the commenters position themselves in this type of online interaction and ask whether and how they attribute aggression to themselves in the first person. The first person in general is an important locus of interpersonal and indexical work and it often establishes the starting point of the interaction (e.g., Agha, 2007: 280; Mühlhäusler & Harré, 1990). The first-person singular attributes maximum overt responsibility for what is being said to the self, and how we thus position ourselves in interaction may vary according to many situational factors and the types of roles we may assume in different social contexts. Verbal or physical aggression is not socially acceptable on most occasions, but forms of verbal aggression seem to occur regularly online (Hamilton, 2012). Our approach combines exploratory corpus-based investigation of patterns of ‘I + aggression verb’ with inductive qualitative close-reading of aggressive comments in context. Rather than explore turn-taking patterns in specific comment chains and the commenters’ local positions in such discussions, we make use of the big data nature of the corpus to identify a more general taxonomy of first-person singular aggression online. To achieve this, we investigate the textual functions of aggressive comments (cf. Mahlberg, 2007). The data include YouTube comment sections relating to sexism collected in the NETLANG corpus. YouTube is the biggest global online video platform where videos are uploaded both by organisations and private individuals. The data source of this study is the YouTube comment section: any logged-in user can leave a comment on a video, and the comments will be visible to all other users. (For an overview of YouTube, see, e.g., Androutsopoulos & Tereick, 2015). In this chapter, the extracts from the data are quoted verbatim; while we have avoided including instances of highly graphic descriptions or threats, the analysis will by necessity include quotes containing aggressive, sexist and misogynistic language, including references to sexual assault.

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2 Background The volume of research on hate speech has increased rapidly during the past 15 years in tandem with the development of online participatory media, especially in the fields of automatic hate speech detection, legal scholarship and social sciences (Tontodimamma et al., 2021). Language scholars have been slower to adopt the hate speech framework, possibly due to its restrictive definition. In many studies exploring some form of derogatory or offensive language use, scholars are not thus restricted but deal, for instance, with online bullying (McCambridge, 2022), or they approach hate speech with a pragma-linguistic framework such as impoliteness (Culpeper, 2021; Culpeper et al., 2017). In our analysis, we adopt the broader, less limiting term verbal aggression to refer to “the act of using aggressive language on a target” (Hamilton, 2012: 6). In this way, we cover a broader territory of first-person aggressive verbal behaviour than concentrating strictly on hate speech would allow us to do. As far as we know, there are no previous studies that would specifically target verbal aggression in first-person singular verbal constructions. While there is no universal definition of hate speech, it is generally defined as “bias-motivated, hostile and malicious language targeted at a person or group based on their actual or perceived characteristics”, such as ethnicity, political orientation, or gender (Siegel, 2020: 57). That is, hate speech is directed at an individual due to their (assumed) membership in a group (Sellars, 2016: 25). In legal definitions “incitement to discriminatory hatred” is another key factor in addition to the vulnerable characteristics (Culpeper, 2021: 5, see also Baider, 2022, for a discussion of definitions, and Ermida, Chap. 2 this volume). For example, YouTube’s hate speech policy describes hate speech as promoting violence or hatred against individuals or groups, based on their age; caste; disability; nationality; race or ethnicity; sex, gender identity, or gender expression; or sexual orientation, among other features (YouTube Help, n.d.-a). Some of the examples in the dataset analysed here match this strict definition of hate speech. Yet, verbal aggression, threats and incitements to violence can also be attributed to the victim’s personal or situational factors, such as their appearance, perceived authenticity or likeability (McCambridge,

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2022), or behaviour, such as Verbal Trigger Events (see Wigley, 2010). In addition, the danger posed by antagonistic language ranges from offense and harassment to calls for discrimination and even coordinated extremism and attacks (Gagliardone et al., 2016: 18–19). The linguistic forms of verbal aggression cover many types of hostility, rudeness and incivility (cf. types of conventionalised impoliteness, Culpeper, 2011: 135–136). Based on research on discrimination and racism, we know that such discourse may employ an array of discursive strategies including metaphors and derogatory naming practices to construct the self/in-group as good and the other/out-group as bad (Reisigl & Wodak, 2001). For example, recent studies using machine learning methods have identified words such as fuck, ass, shit, faggot and little among the top words in both offensive and hateful tweets, and additionally words like hate and kill in hateful tweets (Watanabe et  al., 2018). However, since our analysis focuses on the first-person I, further tied to the presence of an aggression verb, many common types of verbal aggression are excluded from consideration, including various types of insults, such as personalised negative vocatives, assertions and references expressed in the second person (e.g., you idiot, you are X), as well as silencers (e.g., shut the fuck up) and negative expressives (e.g., go to hell, fuck you) (Culpeper, 2011: 135–136). Online spaces may be particularly prone to induce hateful content. The anonymity or pseudonymity of many social media discussions, including YouTube comments, has been associated with hostile behaviour for self and social identities. Users’ sense of anonymity in online discussions may disinhibit individuals to engage in behaviour they would avoid in offline interaction (Suler, 2004). Despite YouTube’s policy prohibiting threats and “implied calls for violence” on the platform (YouTube Help, n.d.-a, c), we found plenty of aggressive comments in the data, especially different types of threats, which we view as a particular type of verbal aggression. Threats can be formally defined as illocutionary speech acts that express the speaker’s intention to commit an act that will affect the addressee negatively (Fraser, 1998). However, the speech act does not entail an actual commitment to the act, only the intention is expressed (Fraser, 1998, see also discussion of intentionality by Culpeper, 2011: 48–52). Since we found many other types of aggressive comments, which

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do not seem to serve specific speech acts, we focus our analysis broadly on textual functions of verbal aggression instead (cf. Mahlberg, 2007). Hate speech and verbal aggression may serve many general functions. For example, applying the analytic framework of conventionalised impoliteness formulae, Culpeper et al. (2017: 14) found out that the language of religiously aggravated hate crimes is often coercive, seeking to force a realignment of values between the producer and the target by means of insulting and threatening the target. Online hate may also function to discourage targeted groups from participation (e.g., Nadim & Fladmoe, 2021; Richardson-Self, 2021) by inciting negative emotional reactions among the targets (e.g., Staude-Müller et al., 2012) and thus creating an unsafe atmosphere. Online hate is even known to incite extremism and real-life violence (e.g., Siegel, 2020; Richardson-Self, 2021). For example, members of the online “incel” community, known for their hateful orientation towards women, have committed several violent acts in recent years (Pelzer et al., 2021). From the perspective of the instigator, there may be a number of different motivations to engage in online hate. For example, some individuals engage in hate speech for a political cause or as a part of a cultural struggle, others use it as a way to draw attention to social problems (Erjavec & Kovačič, 2012). While online hate can be purposeful and ideological, it can also be affectively motivated, spontaneous and related to the individual’s subjective emotions (Saresma et  al., 2022: 89). For example, online aggression may be triggered by the speaker’s sense of being threatened or being put in a weaker position by an out-group (Saresma et al., 2022: 93–94). A sense of anonymity in a crowd of other users can further encourage users to see others as part of stereotyped outgroups (Postmes et al., 1998; Spears & Postmes, 2015). Impolite or hostile comments may boost in-group identity by creating affiliation among commenters. For example, criticising an out-group or a member thereof may strengthen a sense of in-group homophily among YouTube commenters (Andersson, 2021), with the in-group judgement co-constructed as a mass social judgement attributed to “everyone” (e.g., McCambridge, 2022). Similarly, although hate speech and online hate are often linked with societal issues and political ideology, for many, producing hateful content

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is simply “just some game in the online community” (Erjavec & Kovačič, 2012: 912). So-called performatively motivated hate speakers may share opinions or ideas that they do not truly support in order to provoke or escalate an argument and get a reaction (Saresma et al., 2022: 90). This type of action can be described as trolling, that is, “deliberate, deceptive and mischievous attempts that are engineered to elicit a reaction from the target(s) [and] are performed for the benefit of the troll(s) and their followers” (Golf-Papez & Veer, 2017: 1339). For example, some Twitter posts containing sexual aggression may contain threats and harassment to cause fear in the recipient, whereas others contain no linguistically evident intent to harm and are instead posted as part of jocular discourse of spam or ridicule (Hardaker & McGlashan, 2016). Nevertheless, since the motivation is not known to the target, even online hate with performative or jocular function can cause emotional distress for the affected individuals and groups. Indeed, while some definitions of hate speech consider whether hate speech causes negative effects beyond the speech itself, these are problematic precisely because of the challenges involved in measuring the harm to the victim(s) or the spread of hatred among readers (Sellars, 2016: 27, Hietanen & Eddebo, 2022). Considering that online hate may often be motivated by ideological forces, it is not surprising that YouTube videos on controversial topics tend to attract more spam-like or offensive comments, emotional shout-­ outs and irrelevant content than do videos on more neutral topics such as animals (Schultes et al., 2013: 666). Because of the above reasons and because the original corpus compilation was based on the presence of words of verbal aggression, we assume that the data on a debatable topic such as gender contain offensive comments relating to (but not necessarily limited to) sexism. Indeed, our analysis demonstrates that YouTube tends to contain a noticeable amount of insulting, threatening and aggressive comments, despite YouTube’s hate speech policy, user’s ability to report hate speech and comment sections, and YouTube’s comment moderation targeting hate speech and cyberbullying (YouTube Help, n.d.-a, b, c).

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3 Methods We looked at the most common aggression verbs occurring in the NETLANG Sexism subcorpus, hypothesising that some verbs would be used to express hate in constructions with the first-person singular. The corpus contains 21.8 million words representing over 762,000 comments posted in response to 172 different YouTube videos. The data includes comment sections of gender-related YouTube videos. The data was scraped automatically by the NETLANG team using specific key words selected by the team to identify verbal aggression. The keywords used to identify comment sections containing sexism include the following: “Male chauvinism”, “Chauvinist”, “Gender”, “Sex”, “Sexual”, “Sexism”, “Misogyn-” (-ny/-nous/-nist) “Patriarchy”, “Pussy pass”, “Misandry”, “Woman”, “Chick”, “Dame”, “Old hag”, “Hag”, “Crone”, “Witch”, “Minger”, “B*tch”, “Froggie”, “Harlot”, “Ho”, “Hooker”, “Promiscuous”, “Sharmoota”, “Slag”, “Slapper”, “Slattern”, “Sl*t”, “Sloot”, “Tart”, “THOT”, “Trollop”, “Tramp”, “Wh*re”, “Dumb blonde”, “Becky”, “Make me a sandwich”, “Bimbo”, “Feminazi” (see Henriques et  al., 2019). As the corpus comprises the identified comment sections in their entirety, it includes also comments without any verbal aggression or with verbal aggression relating to issues other than sexism. First, the 500 most frequent verbs in the corpus were manually scanned to identify potentially “hateful” verbs: the list was reviewed several times after which concordances of each verb were carefully checked to ensure they were used for verbal aggression at least some of the time. The following verbs were chosen for further qualitative inspection: die, kill, fuck, shut, destroy, beat, break, throw, rape, slap, kick, punch, shoot, burn, smack and rip.1 In an exploratory pre-analysis, we used keyword analysis (e.g., Scott, 2010) to compare the frequencies of words in the study corpus and in the US-subcorpus of the GloWbE corpus (Davies & Fuchs, 2015); this confirmed that most of the chosen verbs are particularly frequent in

 Verbs that were initially inspected but were left out of the analysis are: fight, blame, hit, hurt, fall, force, abuse, cut, attack, harm, punish, screw, suck, murder and bang. With some of these there were isolated instances of hateful use, excluded from the analysis due to insufficient frequency. 1

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the study corpus and might thus reflect language characteristic to (sexism-­ related) verbal aggression in particular. For each verb selected for further analysis, we extracted concordances where the pronoun I appeared within five words left of the present tense form of the verb. This “5L” window span allowed for modal verbs as well as intensifiers or other adverbials to appear between the verb and the pronoun, which was commonly the case. In the concordance searches, we included the base form of the verb, the third-person present tense -s form and present progressive -ing form but excluded past tense (see below). This initial selection resulted in 6363 concordances (see Table 4.1).2 Next, we proceeded with a manual inspection of the concordances to ensure that they fit our final selection criteria. The first criterion was that the concordance needed to include clear verbal aggression, for example, expressing violent actions towards the target as a function of the verb. Certainly, this part of the process hinged on the researchers’ interpretation, as understandings of what is considered aggressive or threatening may differ between cultures and individuals (cf. Culpeper, 2011: 14–15). Second, we manually confirmed that the aggression verb was directly attributed to the imagined actions or thoughts of the agent behind I. Third, we only included instances where the aggression targeted people, people-related abstract entities (e.g., humanity, feminism) and in some rare cases inanimate objects (such as computer screens). Following these criteria, we excluded concordances with non-­ aggressive meanings of the verbs as well as concordances with unfitting targets (e.g., dying of laughter, beating someone in a game, destroying someone’s arguments).3 While we made a note of the type of target of the verbal aggression, we did not specify the gender of the target or whether the aggression was triggered by sexism. Since we were interested in verbal aggression that occurred in the communicative event itself, we also excluded instances where the writer was describing a past event and/or someone else’s actions (e.g., I’ve seen a woman punch a complete stranger),  Due to technical difficulties working with POS-tagged data, the searches were carried out on non-­ tagged data, hence, non-verb forms were included in the initial selection, but excluded during the manual inspection of the concordances. 3  Similarly, with one relevant exception (throw up, see Sect. 4), we excluded phrasal verbs such as fuck up and kick out. 2

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Table 4.1  Distribution of initial items and verbs included in the analysis

cases of reported speech (e.g., I’d be like “ok I’d punch you” ),4 and meta-­ level discussion of violence (e.g., I deserve to punch women as hard as I punch men). Similarly, we excluded humorously intended or sarcastic comments (e.g., I use to beat my four wives just for fun everyday), since such comments do not represent the writer’s aggressive position in the same way as our other examples. This process resulted in a final selection of 1591 concordances (25% of the initial selection, see Table 4.1), with great variation between the different verbs. Whereas 70% or more of the instances with punch, slap, and smack were identified as verbal aggression, included in the analysis, only  Instances of past tense are thus mostly excluded. A rare exception are cases where the writer clearly talks about their reaction to the video in past tense; these are equivalent to other writers expressing their reactions in present tense, e.g., I wanted to punch my screen. 4

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a small fraction of concordances with rape, break and fuck were included. For example, many instances of rape turned out to be cases of reported speech, whereas break and fuck were utilised in many non-aggressive expressions; fuck mostly appeared in other roles than a verb (Table 4.1). With an inductive approach (see, e.g., Thomas, 2006), the analysis aimed at identifying the targets and functions of verbal aggression. With functions, we refer broadly to often-localised textual functions (cf. Mahlberg, 2007). During the first rounds of analysis, the concordances of each verb were analysed separately. This allowed us to pay particular attention to verb-specific uses and functions. Approaching the data with a close reading, we coded the type of target and function of the verbal aggression for each concordance. The task was carried out by the first and second authors, who each annotated the concordances of about half of the selected verbs, discussing coding procedures and initial codes regularly. After the first round of coding, the authors decided on a final coding scheme. Each author then revised the coding of their share of the concordances. To further improve inter-annotator agreement, as the next step, each author inspected the concordances initially analysed by the other author, making note of any disagreement in the coding procedure. Deviant cases were discussed together, reaching a mutual decision in all cases. Last, the concordances of different verbs were organised in one file, allowing us to carry out further analyses and easily compare different verbs, targets and functions. The targets of verbal aggression were categorised based on the specificity of reference. Specific targets include individual persons, groups and the self, whereas unspecific targets include generic references (e.g., anyone, a woman) and abstract references (e.g., sexism, Islam, humanity). While the analysis of functions was largely inductive and we approached the data with no particular framework in mind, once we had finalised the coding scheme, it was evident that threats were a common nominator for many of the identified functions (cf. Fraser, 1998). As such, at the first level of our taxonomy, we categorise functions as threats of physical aggression and as expressions of mental aggression. Threats were further classified as either simple or conditional threats, whereas expressions of mental aggression were grouped into boulomaic and emotive expressions. Third-level categories reflecting our coding scheme are summarised in Fig. 4.1 and are further presented in Sect. 4.1.

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Fig. 4.1  Taxonomy of first-person verbal aggression

Importantly, in our analysis of functions, we only consider the immediate textual context of the verbal aggression (in a window span of L10– R09). At times, a broader context including the YouTube video might, for example, reveal relevant information about the nature of the comment, but multimodal analysis of this type falls beyond the scope of this chapter. In addition, while some overlap occurs between the function categories, we have opted to categorise the threats only based on their most salient function. In Sect. 4, we present verbatim examples from the data. The YouTube comment section is available to anyone without registration, and thus, there is no reasonable expectation of privacy for the commentators (European Commission, 2021: 13–14). Nevertheless, following Scott (2022: 157), we considered whether the data excerpts could lead to the identification of the commentators. However, the examples have low searchability and thus prevent identification of the original contributions.

4 Analysis We begin this section by introducing the functions represented by our taxonomy in Sect. 4.1, followed by a quantitative consideration of the association between verbs and functions in Sect. 4.2. In Sect. 4.3, we then examine the targets of verbal aggression, before discussing the results in more depth in Sect. 5.

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4.1 Functions of First-Person Verbal Aggression 4.1.1 Threats of Physical Aggression This main category includes both simple threats (direct and indirect threats) and conditional threats (prototypical, punitory, disciplinary, and retaliatory), the former lacking the specificity and detail as to why or under which condition the threat ought to be carried out, present in the latter.

Simple Threats Direct Threats: I’d kill that shrimp As the most common type of threats, with 355 instances, direct threats express the writer’s (alleged) intention to harm the target of the threat, typically in a straightforward fashion. Yet, while many direct threats are succinct (1–2), some include additional details concerning the way the threat might be delivered (3); detailed accounts were particularly frequent with kill (4). (1) I hate you I will kill you (2) I’ll fucking destroy you (3) I will bitch slap you with a hammer until you WAKE UP (4) I would fucking kill her I would shoot her in the legs beat the shit out of her with a crow bar Direct threats were often expressed with future orientation, for example, with the auxiliary verb will (1–3). However, over half of direct threats (196 out of 355 instances, or 55%) appeared in would constructions (4–6). While the modal verb would suggests conditional mood, we categorised these constructions as simple (and direct) threats when would appeared alone and the condition under which the threat would be acted upon was not specified, in contrast to conditional threats (see below).

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(5) I would kill that racist bitch (6) I’d beat tf out that bitch like dead ass

Indirect Threats: Good thing I know how to break a wrist with my bare hands Indirect threats are a function category distinct from other types of threats. The category was not frequent in the data (with only 22 instances) and thus no salient constructions were identified with indirect threats. Instead, indirectness emerges at the level of meaning. Indirect threats do not declare the writer’s intention to act against the target, but they suggest the possibility of violence, for example, by stating that the writer has the ability to carry out an act of aggression (7) (see Yamanaka, 1995: 52). Other times, indirectness rose from the vague level of intent (8) or in the form of a question, for example, (9). (7) oi mate bet I could fuckin beat your ass (8) I had the thought of kicking those 3 girls (9) do i punch you or punch you??

Conditional Threats Conditional threats contain the writer’s threat of committing an act as well as a specification of a condition: a reason for the act to occur, a prerequisite or hypothetical situation in which the act would be carried out. Based on the type of conditional, we categorise threats as prototypical, punitory, retaliatory and disciplinary. As indicated by the category label, we classify typical cases as prototypical conditional threats, which lack further qualities found in punitory, retaliatory and disciplinary conditional threats.

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Prototypical: If I was there, I would slap her Prototypical conditional threats were nearly always expressed with the construction if X, then I would Y. The conditional if-clause suggests that the threat would be carried out under some hypothetical circumstances. However, prototypical conditional threats resemble the simple threat function category in that the writer does not specify an obvious reason as to why the act ought to be carried out, as instead the condition is given along the lines of “if I was given the chance” or “if it were me” (10–12). (10) If I see this girl out in public I will slap the living daylight out of her (11) If I was god I would first destroy the feminists, wouldn’t think a second (12) If I were you I would throw her across street and leave her there Details on the reason or condition may be evident to readers if the actions of the addressee are given in the YouTube video, as is the case in example (10), where the target, “this girl”, is a person featured on the video.

Retaliatory: If someone grabs me, I’ll kick their ass A special type of a conditional situation occurring in the data was that of retaliatory conditional threats. Similar to prototypical conditional threats, retaliatory threats were often expressed with the construction if A does X to me, then I would Y, hence specifying the action that would lead to the retaliatory threat being carried out. For example, retaliation was imagined to happen if someone attacked the writer first (13), if someone misbehaved towards the writer (14) or made racist remarks, for example (15). (13) if they punch i [sic] punch them back (14) If she ever talked to me like that I would beat her ass (15) If somebody white call me a [n-word] I would kill then [sic] Retaliatory threats seem to be utilised to present the aggression as “reasonable” or “justified”. This way, the writer takes a stance towards actions

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they see as unacceptable while perhaps also giving a warning to any addressee that might initiate conflict.

Punitory: I still punch a toxic woman in their face Punitory conditional threats were usually also expressed in similar ways to direct threats, with the distinction that with punishments, the writer clearly indicated a reason for the violent act to occur. While retaliatory threats indicate that aggression would occur in response to a potential action of an oftentimes generic target, punitory threats generally occur in reaction to the target’s alleged behaviour (16–17), prejudiced attitude (18–19), perceived stupidity or intellectual capacity (20) or ideological positioning (21). At times punishments were explicitly framed as conditional, with the modal verb would (16–17, 20). (16) I would slap if she gave me that fuckin attitude (17) This girl is so cringy I would beat her into a 3year coma (18) I’ll punch her so badly stupid racist ugly bitch (19) I won’t hesitate to punch that sexist son of a bitch (20) I’d end up slapping some idiots with the good lords bible (21) I’ll freaking kill u because ur a feminist u offend me Commonly, the punishable behaviour or characteristic of the target on the video was referred to implicitly. By threatening punishment, the writer judges the target’s behaviour or attitude as unfavourable or unacceptable some way or another.

Disciplinary: I would have to beat her ass if I were her parents Sharing characteristics with both prototypical conditional threats and punitory threats, disciplinary conditional threats include specific threats that suggest corporal punishment in order to teach the target some kind of a lesson. In these cases, the hypothetical condition is used by the writer

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to situate themselves in an authoritative role, typically as the target’s parent (22–23) but sometimes also as a sibling (24). (22) If she was my daughter I will smack the shit out of her (23) I f I was her mama ohh I swear I’d slap and spank her everyday until she learns her lesson (24) If I was her sister I would rip her head off Because of the prevalence of parental punishment, we included in the disciplinary category instances where an image of a parent punishing a child is otherwise evoked, with mentions of lashing someone with a slipper or a belt (25), for example. (25) Her bitchy ass needs the belt and a good slap of reality. On the other hand, implications of punishing someone physically in order to teach them a lesson were also attested in uses of the verb fuck (26), where the writer situates themself in a position of power and control over the target but not as an imagined family member. (26) I wanna fuck the racism out of her

4.1.2 Expressions of Mental Aggression The data also includes types of verbal aggression that lack the indication of the writer’s intention to act, which is associated with threats (see Fraser, 1998). Instead, they convey internal mental states that the first-person writer ascribes to themselves. This main category includes boulomaic expressions conveying a wish and emotive expressions evaluating the target.

Boulomaic Expressions Boulomaic expressions were used to express what the writer hopes will happen to the target of verbal aggression; in this sense, these expressions

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are characterised by a threatening quality. Boulomaic expressions were further distinguished based on whether the writer wishes the action were to be carried out by themselves (agentive wish) or by someone else (nonagentive wish). Particularly agentive wishes resemble threats in that an aggression verb indicates violence towards a target, and the I functions as an imagined agent. However, framing the act of aggression as something the writer allegedly merely desires to do or hopes to happen mitigates the writer’s intention to carry out the act of aggression.

Agentive Wishes: I wanna slap her so hard Agentive wishes were most typically expressed with constructions using boulomaic verbs, for example, I hope, I want to (or / wanna) and I wish (27–29). Notably, the construction I want to [beat/kick/slap/smack] the [shit/crap/fuck] out of [target] was utilised fairly frequently (examples 29–30). A less frequent but salient construction was asking for permission to do something, sometimes even followed with a polite please (30–31).5 (27) I wanna punch this bitch in the throat so badly (28) My head hurts I just wanna kill feminists that are like this (29) I wanna slap the shit out of her (30) Can I kick the shit out of her soul? (31) Can I just slap her, please Importantly, agentive boulomaic expressions imply that the writer does not intend to carry out an act of violence. Although this may, of course, be because the writer is unable to do so, we see this category as mitigating the intention of the writer compared to the intention entailed by a direct threat.

 While these constructions were common for wishes, they were not exclusively used in boulomaic expressions, for example, I want to throw up was classified as a contemptuous reaction (see below). 5

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Nonagentive Wishes: I hope she dies Nonagentive wishes represent the writer’s wishes, but the writer is not positioned as the agent of the desired action.6 Most commonly, there was no named agent for the action (32–34), but sometimes there was an unspecific agent, for example, someone (35). Only rarely were agents of these wishes specific people (36). Apart from missing an agent, nonagentive wishes were formulated in similar ways as agentive wishes (e.g., I hope, I wish). (32) i hope she kills herself and saves the world a lot of trouble (33) I think he should burn in hell (34) I wish all racists would die not just white (35) […] and i hope someone kills your husband (36) I hope he fucks you till your [sic] dead The verb die was frequently used in this category, given that this intransitive verb cannot have an active agent who would carry out an act towards a target. On the other hand, nonagentive wishes of the target’s death were attested with the verb kill (32, 35) and other lexico-grammatical detailing circumstances, such as until you’re dead (36).

Emotive Expressions The last category of emotive expressions differs from all other categories in that the (imagined) physical violence or act of aggression does not directly target the cause of the aggression. Instead, the physical aggression verb is directed towards the writer’s self or towards inanimate objects (violent reactions), or without directing the act at anyone or anything (contemptuous reactions). However, we chose to include emotive expressions in our analysis, since they express hostility and intense dislike towards a target, which qualifies as verbal aggression. That is, the verbal  Since our selection criteria excluded past tenses of the selected verbs, many common types of nonagentive wishes expressed in passive constructions are thus excluded, e.g., I hope she gets punched. Hence, this category does not represent all nonagentive wishes. 6

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aggression is typically triggered by someone or something in the video. This trigger of aggression is the ultimate target of the negative evaluation by the writer, thus tying the emotive expressions indirectly to similar targets as we have included above.

Violent Reactions: I wanna kill myself after this video Violent reactions were typically explicit reactions to the video: the video, someone or something in the video, makes the person want to act aggressively (37–40). The verbs die, kill and punch were most frequently employed in this function. Die and kill, used in a hyperbolic way, typically directed the action towards oneself (37–38), while inanimate objects such as the wall or a computer screen were found with punch (40). (37) I just want to die because I watched this video (38) I will kill myself before I go into a nursing home (39) I’d probably rather shoot myself than spend 5 second with them. (40) I really wanted to punch my monitor as soon as this discussing woman opened her mouth

Contemptuous Reactions: I would not fuck her for gold bars We also identified specific uses with the verbs fuck, throw and throw up that functioned as manifestations of verbal aggression, yet without the threat or wish of violence present in the other functions. These contemptuous reactions were used to express extreme contempt towards the target. Similar to violent reactions, throw up was employed for negative evaluation, to express that the video or something in the video was revolting enough to cause the reaction (41–42). Much less frequently, similar contempt was expressed by indicating one would throw something concretely, for example, in the trash (43). (41) I was about to throw up while watching this (42) Ewww when they made out I throw up (43) I will throw Quran in the recycle bin […]

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Meanwhile, with fuck, contemptuous reactions were mainly expressed by stating that one would not fuck someone, often coupled with additional evaluative language (44) or circumstances (45). What makes this construction distinct from most others included in the analysis is that instead of threatening to do something, the commentators here are conveying the opposite, with the negated threat qualifying as verbal aggression given the contemptuous evaluation intended. As such, female worthiness is tied to male desire, and contempt is expressed by considering a female unworthy of male attention. (44) I wouldn’t fuck one of these nasty slags either (45) I wouldn’t fuck her with a stolen dick

4.2 Functions Associated with Verbs To explore which verbs resemble each other in terms of their functions in the data, we clustered the verbs based on the percentual frequency of their functions as verbal aggression (dendrogram in Table 4.2).7 The frequencies of the functions are used to assess the typical functions of verbal aggression in our data and the dispersion of verbs across different functions. Five concordances from the initial selection (n  =  1591) did not match our taxonomy and are here excluded from the analysis. The most frequent functions are direct threats and agentive wishes, which suggests that most of the verbal aggression in the data does not specify the reason for why the act of physical violence should be carried out. On the other hand, conditional threats outweigh simple threats, and retaliatory conditional threats are the third most frequent function category. Together, retaliatory, punitory and disciplinary threats account for  The clustering was done using the “stats” and “hclust” packages in R. The distance between the verbs was measured as Euclidean distance and the distances were clustered using UPGMA clustering method. 7

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Table 4.2  Clustering of verbs and proportional frequencies of functions

485 instances out of the total of 1586, meaning that 31% of the instances of verbal aggression are asserted or implied to be justified because of the unacceptable action, demeanour or characteristic of the target. Table 4.2 shows that there is a lot of variation in the distribution of functions among the selected verbs. The most frequent verbs—slap, beat, punch—are dispersed across several functions. Despite semantic similarity of these verbs as expressing concrete violence inflicted by hands, beat is associated more closely with direct threats and retaliatory threats, whereas punch and slap as well as smack occur most commonly with the boulomaic agentive wish function, where threat towards the target is mitigated. Slap and smack are also used for disciplinary threats, further indicating that the physical threat of these two verbs is relatively mild compared to punch, for which only 2% of instances are disciplinary threats. Meanwhile, kill and shoot occur at highly different frequencies in the data but bear semantic resemblance and are associated with direct

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threats, agentive wishes and violent reactions. In summary, although the most frequent verbs are distributed across almost all the functions, they are associated more strongly with certain functions of verbal aggression. This may in turn partly explain the frequencies of the functions: for example, the frequency of punch may explain the frequency of agentive wishes, as punch accounts for 98 out of the 335 instances (29%) of this function category. On the other hand, some of the studied verbs are limited to specific functions. While burn and die occur at different frequencies, both are used primarily in the boulomaic nonagentive wish function. As noted above, the intransitivity of die explains why as many as 82% of the instances of this verb are categorised as nonagentive, and why 103 out of 142 (73%) instances of nonagentive wishes are indeed wishing for the target’s death. Fuck and throw up are in a cluster distinct from the other verbs in that they are used for contemptuous reactions; in fact, throw up is used exclusively to convey contempt. The remaining verbs are generally too infrequent or too widely dispersed in our data for any reliable generalisation.

4.3 Targets of First-Person Verbal Aggression While individuals were rarely named in the data, verbal aggression was by far most frequently targeted at specific persons (Table 4.3). As illustrated by many of the examples in Sect. 4.1, these targets were typically represented by a pronoun, for example, her, him or you, interpreted to be a person in the video. Similarly, groups represent people appearing in the video, referenced to with the pronouns they and you, and with various types of noun phrases (NPs), such as these idiots, these motherfuckers. Groups also often refer to categories such as women and feminists (example 28) or racists (34). While we did not quantify this aspect, the majority of the targets in the data seem to be women (and/or feminists); this is likely explained by the corpus compilation procedure. Some verbs target the writer’s self (Table 4.4), typically expressed either with intransitive (I + verb) or with transitive constructions (I + verb + myself  ). However, as discussed above and shown in Table 4.4, this target

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4  First-Person Verbal Aggression in YouTube Comments  Table 4.3  Verbs associated with targets of first-person verbal aggression

person group destroy 11 4 break 10 1 kick 54 2 beat 238 13 rip 10 0 throw 19 1 shoot 17 6 kill 70 20 smack 88 6 slap 298 23 punch 150 17 rape 9 3 die 73 20 burn 12 5 throw up 0 0 fuck 40 3 total 1099 124

self 0 0 1 1 0 0 8 27 0 2 1 0 23 2 38 0 103

generic abstract other total 1 2 0 18 9 0 1 21 16 0 0 73 69 0 1 322 3 0 0 13 4 1 1 26 0 1 0 32 3 130 9 1 0 122 28 0 20 0 0 343 9 236 59 0 1 0 0 13 0 126 0 10 1 3 0 23 0 38 0 0 3 50 3 1 223 19 18 1586

Table 4.4  Functions associated with targets of first-person verbal aggression

person direct threat indirect threat prototypical punitory disciplinary retaliatory agenve wish nonagenve wish violent reacon contemptuous r. total

group self generic abstract other SIMPLE THREATS 295 18 0 39 2 1 1 10 6 0 5 0 CONDITIONAL THREATS 76 6 0 13 0 0 75 11 0 22 0 0 0 146 5 0 1 0 83 9 0 130 1 2 BOULOMAIC EXPRESSIONS 285 42 0 6 1 1 106 24 0 1 11 0 EMOTIVE EXPRESSIONS 1 2 64 2 2 10 22 1 39 4 2 3 1099 124 103 223 19 18

total 355 22 95 108 152 225 335 142 81 71 1586

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category was restricted to the violent and contemptuous reactions, which suggest that the writer is prepared to harm themselves, yet entail negative emotive reactions triggered by some other individual or object. The “other” target category is also mostly covered by the violent reaction function, as “other” targets mainly include animals or inanimate objects in expressions of anger, such as punch my monitor (example 40). Meanwhile, generic targets were often represented by NPs, such as a girl, that bitch, but also by personal pronouns (you, they) and indefinite pronouns, such as anyone, someone. In contrast, abstract targets such as feminism, sexism, racism, religion or humanity at large were infrequent. Table 4.2 shows that there are some tendencies for specific functions to be directed at certain targets. Overall, specific persons were the most common target of verbal aggression in the data, but they stand out as particularly common for various types of threats (direct threats, prototypical and disciplinary conditional threats) and for boulomaic expressions (both agentive and nonagentive). Other types of targets are less frequent for these functions, although generic targets did appear with direct threats, and abstract targets (e.g., religion, feminism) almost exclusively occurred with nonagentive wishes. This may reflect how it is not feasible for an individual to (physically) harm an abstract target, leading writers to wish for a more abstract nature of damage instead with die or burn, which were commonly associated with nonagentive wishes. What is more, while group threats do not seem to be associated particularly strongly with any functions, they do appear somewhat more frequently with boulomaic expressions. This may also reflect the more abstract nature of wishes; concrete acts of violence are (imagined to be) directed at specific targets, whereas an unspecified death is easier to wish upon a group. What is interesting is that while many of the conditional threats (prototypical, retaliatory, punitory, disciplinary) could have imagined, generic targets, only retaliatory threats stand out in this respect. Indeed, retaliatory threats were often built by imagining the premises in which someone else’s behaviour would cause oneself to act violently (e.g., example 15). In contrast, disciplinary threats are almost exclusively directed at specific people, that is, used to criticise the actions of a specific individual on the video.

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5 Discussion and Conclusion In sum, our analysis demonstrated that first-person verbal aggression takes many forms, and that despite the strict hate speech policy, YouTube comments include a plethora of “implied calls for violence” and threats of physical violence (YouTube Help, n.d.-a). We specifically explored a selection of verbs that we identified as being utilised at least some of the time in verbal aggression: die, kill, fuck, shut, destroy, beat, break, throw, rape, slap, kick, punch, shoot, burn, smack and rip. Our qualitative analysis revealed that these verbs are indeed employed in verbal aggression, but verbal aggression is not their only possible function. Such multifunctionality poses difficulties for automatic hate speech detection, but our findings and the taxonomy introduced may potentially be used to improve the accuracy of automatic recognition. While previous research (e.g., Culpeper, 2011: 135–136) has proposed various frameworks and taxonomies for what we call verbal aggression, our specific focus on the first-person and on aggression verbs necessitated an inductive approach to the analysis. We categorised instances of verbal aggression as different types of threats of physical aggression and threatening expressions of mental aggression, the latter category including some specific types of comments expressing negative emotive evaluation. Overall, direct threats (e.g., I will kill you) were the most common type of verbal aggression in the data, followed closely by threatening boulomaic agentive wishes (e.g., I want to punch her) and less frequent but distinct nonagentive wishes (e.g., I hope she dies); different types of conditional threats were also frequently employed, specifying the condition under which the threat would be carried out (e.g., if you do X, I will punch you). The analysis confirmed that aggression expressed from the point of view of the writer in the first person occurs regularly online and writers do not shy away from taking responsibility for the most explicit threats of violence. The analysis also revealed that the targets of verbal aggression were most commonly specific people (in 69% of instances), typically either other commentators or people appearing in the video. While hate speech specifically targets people based on their salient group membership such as (assumed) gender, ethnic background or political orientation (e.g.,

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Sellars, 2016: 25, Siegel, 2020: 57, see also Silva et al., 2016), we found that at least on the surface level, verbal aggression was often triggered by someone’s behaviour. Nevertheless, we also found comments where the target’s background was specified (e.g., I’d slap that Asian bitch). The data moreover included repeated references to women and feminists, but we must attribute this to the data collection method and thus cannot regard it as a finding. Furthermore, while we found many instances in which verbal aggression at least seemingly occurred in reaction to behaviour (e.g., punitory threats targeting individuals who are perceived to have broken social norms), this is not to say that the individual’s assumed membership in a group does not play a role in triggering verbal aggression. Indeed, a multimodal exploration including the videos might have revealed more information about the targets. A future study could consider how the person(s) and the content of the video predict the types of aggression in the comments, for example, whether the threats or ideological views expressed in the video are reflected in the comment section. Certainly, the content of the video plays some part, as in the current study, we observed threats directed at women featured in the videos and several discussions on the topic of equality, where threats were used in meta-level discussions on whether it is justified (for a man) to punch a woman (e.g., If someone slaps me, girl or boy, I’m going to punch them.). What we did find surprising is that while hate speech in particular is directed at disadvantaged groups, verbal aggression did not target only disadvantaged groups but also the assumed aggressors, since targets were at times explicitly described as sexists or racists (e.g., Shut the fuck up before i slap white racist ass). Indeed, commentators regularly attempted to justify the aggression by various means—most notably occurring in threats that we labelled as disciplinary threats, in which the writer takes an authoritative, often parental role, to teach a lesson to the target for breaking a social norm (cf. Culpeper et al., 2017: 16–17, who found the correcting of past injustice and setting things right as a frequent perceived moral justification for harm). Our focus on verbal aggression by the first person already frames the communicative acts as personal in a sense. However, we found variation in the severity of verbal aggression. Direct threats imply an intention to

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carry out physical violence, representing severe cases, while boulomaic expressions, indicating wishes, can be justified to simply express the writer’s desires. Emotive expressions of aggression do not direct violent behaviour towards others, yet they carry out what seems to be one of the base functions of verbal aggression: an indication that someone or something is detestable to the extent that they “deserve” to be (verbally) abused. Furthermore, while the focus of this chapter did not allow us to delve deeper into the matter, we found that the commentators were using various ways to either (somewhat) soften the threat of violence—or intensify it. For example, the frequent use of would in threats might function to downtone the meaning (I will kill her vs. I would kill her), and sometimes verbal aggression was expressed as a question or even as a “polite” request (e.g., Can I just slap her, please). In contrast, meanings were regularly intensified with cursing and profanities (e.g., I would fucking kill her, I hope this b*itch dies in the worst way possible), as well as by adding gruesome details of the intended violence (e.g., I hope you die slowly in a train wreck). The frequency of instances of writers expressing threats or wishes of serious physical harm could suggest the normalisation of aggression and to some extent the semantic bleaching of aggressive verbs such as die, kill and shoot. Last, we found threats and aggression directed at groups or at abstract societal concepts to be rare in the data. Instead, at least on a surface level, verbal aggression triggered by the target’s behaviour or personal characteristics often seemed to be spontaneous and motivated by the writer’s emotional reaction rather than by ideological goals (see Saresma et al., 2022). While the verbal aggression identified here does not necessarily match definitions of hate speech as incitement to violence or discrimination against a group, it may nevertheless contribute to or reflect broader ideological debates. Gendered, sexist and especially misogynistic stereotypes are a normalised part of harassment and trolling in internet culture (e.g., Condis, 2018; Lumsden & Morgan, 2018), which means that many instances of verbal aggression found in our data may to some extent draw on the broader discourse and contribute to normalising the sexist discourse in online interactions.

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Reisigl, M., & Wodak, R. (2001). Discourse and discrimination: Rhetoric of racism and anti-semitism. Routledge. Richardson-Self, L. (2021). Hate speech against women online: Concepts and countermeasures. Rowman & Littlefield. Saresma, T., Pöyhtäri, R., Knuutila, A., Kosonen, H., Juutinen, M., Haara, P., Tulonen, U., Nikunen, K. & Rauta, J. (2022). Verkkoviha: Vihapuheen tuottajien ja levittäjien verkostot, toimintamuodot ja motiivit [Online Hate: The networks, practices and motivations of the producers and distributors of hate speech]. Valtioneuvoston selvitys- ja tutkimustoiminnan julkaisusarja [Publications of the Government’s analysis, assessment and research activities] (p. 48). Schultes, P., Dorner, V., & Lehner, F. (2013). Leave a comment! An in-depth analysis of user comments on YouTube. Wirtschaftsinformatik Proceedings, 2013, 42. Scott, M. (2010). Problems in investigating keyness, or clearing the undergrowth and marking out trails…. In M. Bondi & M. Scott (Eds.), Keyness in texts (pp. 43–58). John Benjamins. Scott, K. (2022). Pragmatics online. Routledge. Sellars, A.  F. (2016). Defining hate speech. Berkman Klein Center Research Publication No. 2016-20. Boston University School of Law. Siegel, A. (2020). Online hate speech. In N. Persily & J. Tucker (Eds.), Social media and democracy: The state of the field, prospects and reform (pp. 56–88). Cambridge University Press. Silva, L., Mondal, M., Correa, D., Benevenuto, F. & Weber, I. (2016). Analyzing the targets of hate in online social media. In Proceedings of the tenth international AAAI conference on web and social media, pp. 687–690. Spears, R., & Postmes, T. (2015). Group identity, social influence, and collective action online: Extensions and applications of the SIDE model. In S. S. Sundar (Ed.), The handbook of the psychology of communication technology (pp. 23–46). Staude-Müller, F., Hansen, B., & Voss, M. (2012). How stressful is online victimization? Effects of victim’s personality and properties of the incident. European Journal of Developmental Psychology, 9(2), 260–274. Suler, J. (2004). The online disinhibition effect. Cyberpsychology & Behavior, 7(3), 321–326. Thomas, D. (2006). A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation, 27(2), 237–246.

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Tontodimamma, A., Nissi, E., Sarra, A., & Fontanella, L. (2021). Thirty years of research into hate speech: Topics of interest and their evolution. Scientometrics, 126, 157–179. Watanabe, H., Bouazizi, M., & Ohtsuki, T. (2018). Hate speech on Twitter: A pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access, 6, 13825–13835. Wigley, C. (2010). Verbal trigger events—Other catalysts and precursors of aggression. In T. Avtgis & A. S. Rancer (Eds.), Arguments, aggression and conflict: New direction in theory and research (pp. 388–400). Routledge. Yamanaka, N. (1995). On indirect threats. International Journal for the Semiotics of Law, 8, 37–52. YouTube Help. (n.d.-a). Hate speech policy. Accessed from https://support. google.com/youtube/answer/2801939?hl=en YouTube Help. (n.d.-b). Potentially inappropriate comments now automatically held for creators to review. Accessed from https://support.google.com/youtube/ thread/8830320/potentially-­inappropriate-­comments-­now-­automatically-­ held-­for-­creators-­to-­review?hl=en YouTube Help. (n.d.-c). Harassment & cyberbullying policies. Accessed from https://support.google.com/youtube/answer/2802268?hl=en

5 Emotional Deixis in Online Hate Speech Joana Aguiar and Pilar Barbosa

1 Introduction In recent years, the debate around hate speech in online settings has flourished. Automatic tools have been developed not only to detect hate speech but also to distinguish it from offensive and non-offensive language. Most of these automatic tools use a lexicon-based approach or a combination of it with sentiment analysis (Davidson et al., 2017; Martins et al., 2018 and references therein). Nonetheless, content words alone are not the only linguistic elements that may convey expressiveness or

J. Aguiar Polytechnic Institute of Bragança, Bragança, Portugal Department of Portuguese Studies, University of Minho, Braga, Portugal e-mail: [email protected] P. Barbosa (*) Department of Portuguese Studies, School of Arts and Humanities, University of Minho, Braga, Portugal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_5

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heightened emotional states. As observed by Potts and Schwarz (2010), the sources of expressive content in language use are not always transparent and often arise from the way apparently innocuous functional elements are used. One such case is the use of demonstratives. Besides their function of indicating spatial and temporal relations or establishing anaphoric reference, demonstratives are often used to convey the speaker’s emotional involvement—emotional proximity or distance—in the subject matter (Lakoff, 1974). In addition, research on demonstrative use has highlighted the role of demonstratives in establishing a common ground or shared perspective between speaker and addressee, thus potentially being used as tools to express complex social meanings and attitudes (Lakoff, 1974; Chen, 1990; Acton & Potts, 2014). In this chapter,1 we explore the use of demonstrative determiners in Portuguese, in a subset of the NETLANG Corpus, an online hate speech database that is composed of comments on pieces of news from Youtube and from Portuguese and English mainstream online newspapers, a type of Computer-Mediated Discourse (CMD) that is characterised by social interaction and user-generated content (Herring, 2013).2 We show that demonstrative determiners are used in the corpus to convey expressiveness and create solidary effects. Our quantitative analysis reveals that, even though there is no significant difference in the distribution of the different demonstrative determiners according to sentiment analysis, the presence of exclamativity reveals a statistically significant pattern: the negative sentiment value is higher when the demonstrative is followed by a proper noun and is inserted in an exclamatory sentence. On the contrary, positively rated comments are more frequent when exclamativity is not present. This entails that the interaction between exclamativity markers and the affective use of demonstrative determiners is a possible feature of hate speech in CMD. This chapter is organised as follows: first, the notion of emotional deixis is discussed in the context of previous work on the topic; the  A special thanks to Filipa Pereira, who ran the sentiment analysis and assisted in many other technical issues, and to Isabel Ermida for commenting on an early draft. We are also grateful to two anonymous reviewers whose critical comments have helped to improve this chapter. Any remaining issues are our own responsibility. 2  Herring (2013) labels this subtype of CMD Convergent Computer Mediated Discourse (CCMD). 1

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following section is dedicated to demonstrative determiners in Portuguese. The methodology, corpus description and corpus analysis tools used are described in Sect. 4. The results of the analysis and the discussion of the main findings are presented in Sect. 5. In Sect. 5.2, we focus on the distribution of demonstrative determiners between proper and common nouns. Finally, Sect. 6 is dedicated to conclusions.

2 Emotional Deixis Lakoff (1974) discusses three uses of demonstratives in English: spatio-­ temporal deixis, anaphora and emotional deixis. The latter use, which involves the proximal (this, these) and distal (that, those) demonstratives, is described as follows3: Under this rubric I place a host of problematical uses, generally linked to the speaker’s emotional involvement in the subject-matter of his utterance. Since emotional closeness often creates in the hearer a sense of participation, these forms are frequently described as used for ‘vividness.’ And since expressing emotion is—as I noted last year—a means of achieving camaraderie, very often these forms will be colloquial as well.

Affective demonstratives (to borrow a term from Potts & Schwarz, 2008) indicate that the speaker wishes to involve the addressee in the subject matter thus conveying a sense of solidarity and shared emotions. As pointed out by Lakoff (1974), in (1), below, the demonstrative points to an attribute of the referent that is assumed to be shared by the speaker and addressee: (1) I see there’s going to be peace in the Mideast. This Henry Kissinger is really something! In (2) the use of the demonstrative instead of the determiner also has the effect of involving the speaker more vividly:  Levinson (2004) uses the term “empathetic deixis” in regard to the extra-text, non-deicitc use of demonstrative determiners (see also Argaman, 2007). 3

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(2) There was this/a traveling salesman, and he … Curiously, the notion of emotional closeness can also be detected in uses of the distal demonstrative ‘that’. As noted by Lakoff, even though the spatio-temporal uses of ‘this’ and ‘that’ are almost the opposite of each other, their emotional uses are close. In (3), the use of ‘that’ conveys empathy and in (4) it implies that the participants in the conversation share the same views on the individual referred to: ( 3) How is that throat? (4) That Henry Kissinger sure knows his way around Hollywood! Lakoff (1974) suggests that the reason why the distal demonstrative is used to convey solidarity is that it establishes a link between speaker and addressee and enables them to relate to one another. The idea that demonstratives bring up a sense of common ground and shared perspective is explored by Acton and Potts (2014) in their study on the potential social meaning of demonstratives. Acton and Potts (2014) attribute this presumption of shared perspective to the basic semantics of demonstratives. In order to identify the referent of an expression introduced by a demonstrative, the addressee needs to consider the speaker’s relation to entities in the shared discourse context. This relation may be spatial, attitudinal or epistemic. Thus, demonstratives can be used by the speaker as a tool to create a sense of ‘perspectival alignment’, which reinforces feelings of solidarity or empathy, on the one hand, or may be taken as “socially repellent if they clash with the addressee’s conception of her relation with the speaker” (Acton & Potts, 2014: 5). Therefore, demonstratives have potentially complex social meanings that are worth exploring in the context of the characterisation of hate speech in CMD. If indeed both the proximal and the distal demonstratives are markers of interpersonal involvement, the question arises whether there is a neutralisation of the proximal–distal distinction in contexts in which the spatial interpretation does not apply (Levinson, 1983). According to Chen (1990), emotional uses of demonstratives and their basic semantic contents are closely related, in such a way that emotional deixis is an extension of the semantic content primarily conveyed by each type of

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demonstrative. In his perspective, the contrast between the proximal and distal demonstratives is maintained in their affective use. Cheshire (1996) addresses this issue in an extensive corpus study and concludes that there are indeed differences in the affective uses of the distal and proximal forms: “this tends to encode the speaker’s personal involvement in what he or she is saying, whereas that tends to encode the speaker’s desire to ensure interpersonal involvement between themselves and their addressee” (Cheshire, 1996: 375–376). Moreover, using large corpora and applying corpus tools, Potts and Schwarz (2008) find contrasting results on the distributional profiles of ‘this’ and ‘that’. While affective ‘this’ correlates strongly with positive evaluativity, the use of ‘that’ has a bias towards the negative end. This issue will be taken up in our examination of the samples of hate speech drawn from comments on the web.

3 Demonstrative Determiners in Portuguese In the context of the present discussion, Portuguese is a particularly interesting language to study, given that it has a three-way system of demonstratives (Lopes, 2019), with three forms that express proximity in relation to the speaker, proximity in relation to the hearer/interlocutor; and distance in relation both to the speaker and the hearer. In total, there are twelve demonstrative determiners in the Portuguese system, classified according to distal relation, gender and number (Table 5.1). As in English, demonstratives in European Portuguese (EP) may be used to convey solidarity and emotional involvedness: (5) Anda por aí muita gente na política bem pior do que essa senhora. [2413] (‘There are many people in politics a lot worse than that [fem. sg] lady’) (6) Esta Joacir continua a pensar que é muito importante. [2339] (‘This [fem.sg] Joacir still thinks she is very important’) (7) Pobre animal ter de conviver com aqueles escroques. [5966] (‘Poor animal having to coexist with those [masc.pl] crooks’)

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Table 5.1  Demonstrative determiners in Portuguese Gender and number Distal relations

Masculine Singular

Feminine Singular

Masculine Plural

Feminine Plural

Proximity in relation to the speaker

este [This—masc sg] esse [That—masc sg] aquele [That one– masc sg]

esta [This—fem sg] essa [That—fem sg] aquela [That one—fem sg]

estes [These— masc pl] esses [Those— masc pl] aqueles [Those— masc pl]

estas [These— fem pl] essas [Those— fem pl] aquelas [Those— fem pl]

Proximity in relation to the hearer/interlocutor Distance in relation both to the speaker and the interlocutor

According to Lopes (2019), the affective proximity or distance conveyed by demonstrative determiners is a “metaphorical extension of the spatial deixis” (Lopes, 2019: 226) primarily conveyed by these elements. Considering the spatial information the demonstrative determiners in Portuguese express (cf. Table  5.1), it is expected that similar distance/ proximity affective meanings are established. Therefore, este, esta, estes, and estas would be used to convey the speaker’s personal involvement whereas esse, essa, esses and essas would express foregrounding of the speaker–hearer relation. The demonstrative determiners aquele, aquela, aqueles, aquelas would be used to convey affective distance as well as solidarity between speaker and hearer. In this chapter, we propose to test these hypotheses by conducting an analysis of the distribution profiles of the three types of demonstrative determiners in the Portuguese NETLANG Corpus. Online comments are dialogic and informal (Herring, 2013), and frequently embed emoticons as well as non-linguistic contextual information (Benamara et al., 2018). It is thus expected to find vernacular and even non-standard lexical forms associated with the use of affective determiners (and exclamativity) to express heightened emotional states and vividness (Lakoff, 1974). Although online comments are dynamic, they do not constitute a form of oral interaction; rather, according to their linguistic and communicative properties, they are classified as a written register. More specifically, online comments may be described as instances of “informally

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written, argumentative evaluative language” (Ehret & Taboada, 2020: 22). Furthermore, these conversational interactions do not necessarily follow a time (or even a topic) sequence, as comments and replies may be intertwined (Benamara et al., 2018).

4 Method In this section, we describe the methodology adopted, from the corpus design to the main tools used to extract and compile data.

4.1 The NETLANG Corpus This research is one of the outputs of the project The Language of Cyberbullying: Forms and Mechanisms of Online Prejudice and Discrimination in Annotated Comparable Corpora of Portuguese and English. Among others, the objective of the project was to build, annotate and analyse a comparable corpus of online texts in Portuguese and English. The Portuguese language subset of the NETLANG Corpus4 gathers comments on 828 texts and/or videos from YouTube and two Portuguese (online) newspapers. In total, the corpus is composed of 57,195,058 tokens. This corpus is organised according to: (i) language (Portuguese or English); (ii) Platform (YouTube or newspaper sites); (iii) Type of Prejudice, Social Variable and corresponding Type of Prejudice (Sexism, Racism, Nationalism, Body shaming, among others). In order to limit the scope of analysis, this study is based on a sample of the NETLANG Corpus. We thus extracted a subcorpus composed only of comments on news labelled with two types of prejudice: sexism and racism, as overall these texts triggered more comments. The decision to select texts labelled as containing sexist and racist comments was also motivated by the results of previous research that focuses on racist and  This project was funded by the Portuguese Foundation for Science and Technology (PTDC/LLT-­ LIN/29304/2017). Corpus and Project description are available online at: https://netlang-corpus. ilch.uminho.pt/index.html 4

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sexist comments analysis: racist tweets are likely to be classified as hate speech and sexist tweets as offensive language (Davidson et al., 2017). To this we shall return below. Quantitatively, the sample extracted from the NETLANG Corpus gathers comments on 206 pieces of news: 43 from Youtube, and 163 from newspapers (41 from Público, a Portuguese daily newspaper, and 122 from Sol, a Portuguese weekly newspaper. This sample is composed of 660,231 tokens (56.3%) from Youtube; and 513,085 (43.7%) from newspapers: 413,285 (35.2%) from Sol, and 99,800 tokens (8.5%) from Público.

4.2 Corpus Tools After selecting the sample, it was necessary to automatically extract the syntactic pattern under analysis. Using the NETLANG Corpora Query Engine,5 it was possible to search for syntactic patterns in NETLANG Corpus, namely [pos=“DETDem”] [pos=“NCommon”] and [pos=“DETDem”] [pos=“NProper”]. This automatic extraction was possible as all NETLANG comments were initially pre-processed and annotated using FreeLing, “an open-source language analysis tool suite”.6 Further details on the NETLANG Corpora Query Engine and the annotation process are described in Henriques et  al. (2019) and Elias et al. (2021). The sample converted in .xml files was then uploaded to Sketch Engine,7 which provided metalinguistic, and frequency data, as well as KWIC (Key Word in Context) information. Using Concordance Tool from Sketch Engine, we ran a frequency analysis to retrieve all comments in which the structure demonstrative determiner + noun was used, as

 This tool, developed to query comments from the NETLang corpus, is available online at: http:// netlang-corpus.ilch.uminho.pt:10400/ 6  https://nlp.lsi.upc.edu/freeling/ 7  Available online at: http://www.sketchengine.eu 5

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well as the contexts of use of ess*, est* and aquel*.8 In total, after manual verification, 6077 comments were analysed.9 Considering that affective demonstratives are often related to evaluative predications (Lakoff, 1974; Potts & Schwarz, 2010) and marked messages (Davis & Potts, 2010), a sentiment analysis was run using SentiLex-PT,10 a sentiment lexicon for the extraction of sentiment and opinion in texts written in Portuguese (Carvalho & Silva, 2015). The presence of exclamativity in the online comments was a linguistic variable that needed to be taken into account. According to Lakoff (1974), Potts and Schwarz (2010) and Lopes (2019), there is a correlation between the affective uses of the demonstratives and exclamativity. Also, exclamatives are described as being inherently evaluative (Potts & Schwarz, 2010). Therefore, the sentences with demonstratives were encoded for exclamativity. In order to minimise subjectivity, we decided to use the presence of an exclamation mark as the criterion for exclamativity. Each comment was annotated according to the following variables: (i) source; (ii) demonstrative determiner (cf. Table 5.1); (iii) noun classification: proper noun or common noun; (iv) context to the right (pause, VP, AP, NP, PP, or conjunction); (v) presence of exclamation mark; (vi) sentiment analysis (negative, neuter and positive); and (vii) sentiment value (values range between −1 and 1, the neutral score being 0). Finally, using the same statistical analysis, results were compared with a subcorpus extracted from a comparable reference corpus, the Portuguese Web 2018 Corpus (ptTenTen). This corpus is made available by Sketch Engine and is composed of texts collected from the Internet, containing

 The asterisk (*) is used to retrieve any number of unspecified characters. This way, it was possible to extract the contexts in which the demonstrative determiners (esse, essa, esses, essas, este, esta, estes, estas, aquele, aquela, aqueles, aquelas) occurred. Because the tool may also extract lemmas that do not correspond to a demonstrative determiner, a manual verification was necessary. 9  Some examples had to be ruled out, as in Portuguese the verb form está (‘is’) when written without the diacritic has exactly the same form as the determiner esta. 10  Further information on the sentiment lexicon for Portuguese—SentiLex-PT 02  (Carvalho & Silva, 2017)—is available at: https://b2share.eudat.eu/records/93ab120efdaa4662baec6a dee8e7585f 8

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more than 7.4 billion words.11 The subcorpus extracted is composed of 3000 sentences containing the structure Demonstrative Determiner followed by Noun (1000 sentences for each lemma).

5 Results and Discussion In this section, we present the overall results of the analysis for the subcorpus NETLANG.  Section 5.2 is dedicated to the type of noun that follows the demonstrative determiner.

5.1 Overall Results Considering the sample of comments on 206 pieces of news, extracted from the NETLANG Corpus, we automatically retrieved a total of 6077 comments in which Demonstrative Determiners occurred. Although the sample was balanced (56.3% of the tokens from YouTube; and 43.7% from newspapers), the majority of the occurrences of demonstrative determiners appears in comments taken from YouTube (4400 comments, 72.4%), and only 27.6% in comments taken from online newspapers (1371 occurrences (22.6%) in Sol and 306 occurrences (5%) in Público). This may be explained by the fact that the affective use of demonstratives is colloquial (Lakoff, 1974) and thus more prone to show up in more dynamic platforms. Table 5.2 below shows the frequency distribution of the demonstrative determiners in our sample. The most common demonstratives are the ones that establish proximity in relation to the hearer/interlocutor. From those, essa ‘this.fem.sg‘is the most frequent demonstrative determiner (23.5%). In view of the types of prejudice of the comments under analysis (sexism and racism), it is actually not surprising that the feminine form of the distal demonstrative should be more frequently used than the masculine form.  Detailed information on the Portuguese Web 2018 Corpus (ptTenTen) is available online at: https://www.sketchengine.eu/pttenten-portuguese-corpus/ 11

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Table 5.2  Demonstrative determiners in Portuguese: distribution in our sample Gender and number Distal relations Proximity in relation to the speaker

Masculine Singular

este 848 14.0% Proximity in relation esse to the hearer/ 1175 interlocutor 19.3% Distance in relation aquele both to the speaker 90 and the interlocutor 1.5% Total 2113 34.8%

Feminine Singular

Masculine Plural

Feminine Plural

esta 1080 17.8% essa 1431 23.5% aquela 96 1.6% 2607 42.9%

estes 257 4.2% esses 485 8.0% aqueles 41 0.7% 783 12.9%

estas 181 3.0% essas 362 6.0% aquelas 31 0.5% 574 9.4%

Total 2366 39.0% 3453 56.8% 258 4.2%

Most determiners are followed by a common noun (5964 occurrences, corresponding to 98.1% of the cases) rather than a proper noun (113 occurrences, corresponding to 1.9%). Proper nouns occur frequently with singular determiners esse (31 occurrences), essa (32 occurrences), este (24 occurrences) and esta (20 occurrences). The only determiner that conveys distance in relation both to the speaker and the interlocutor that occurs with a proper noun is aquele (two occurrences in the examples below): (8) Afinal todos se esqueceram rapidamente da festa de arromba que foi por aquele Médio-Oriente fora! (‘After all, everyone quickly forgot the huge party that went on all over that [masc.sg] Middle East!’) (9) E os virologistas a dizerem bla bla bla como aquele Simas. (‘And the virologists saying blah-blah-blah like that [masc.sg] Simas’) In relation to the right-hand-side context of the string DET + NOUN, we limited the scope of analysis to proper nouns. In the majority of the cases, the proper noun was followed by a Verb Phrase (VP) (61.1%) as in (10); a pause/sentence limit (19.5%) as in (11); and to a lesser extent a Conjunction (7.1%) as in (12): (10) Esta Cristina é a senhora certa para nos apelar ao pensamento (‘This [fem.sg] Cristina is the right lady to appeal to our thinking’)

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(11) Provocadora esta Joaquina! (‘Provocative this [fem.sg] Joaquina!’) (12) este Manoel e este Mike têm um problema muito difícil de se descrever e são de facto racistas e incongruentes (‘This [masc.sg] Manoel and this [masc.sg] Mike have a very difficult problem to describe, and are in fact racist and incongruous’) In terms of sentiment analysis, as predicted, the comments extracted from the NETLANG Corpus tend to be more negative (64.2%) than positive (16.6%) or neuter (19.2%) (Table 5.3). In contrast, sentences extracted from the Portuguese Web 2018 Corpus are mostly classified as neuter (39.1%) even though no sharp differences are evident in terms of sentiment analysis. Negative sentences represent only 29.5% of the overall sample. Consequently, the fact that the majority of the NETLANG comments are classified as negative should be interpreted as a consequence of the characteristics of the corpus under analysis. Figure 5.1 shows the distribution of sentiment analysis according to value (ranging from the most negative (−1000) to the most positive (1000). The value 0 signals neuter comments. According to the results, negative comments tend to be more negative, scoring mostly between −0.500 and −1.00, with a medium value of −0.538. In contrast, the medium score for positive comments is 0.375. According to Davidson et  al. (2017), racist terms are considered by human coders to be hateful whereas sexist terms are encoded as only offensive. To verify this hypothesis, sentiment analysis was also run according to the most salient type of prejudice of the pieces of news/ videos (classified as racist, classified as sexist, classified both as racist and

Table 5.3  Distribution of sentiment analysis (negative, neuter and positive) NETLANG subcorpus

Portuguese Web 2018 Subcorpus

Sentiment

Frequency

Percentage

Frequency

Percentage

Negative Neuter Positive Total

3901 1168 1008 6077

64.2% 19.2% 16.6% 100.0

886 1172 947 3000

29.5% 39.1% 31.4% 100%

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Fig. 5.1  Distribution of sentiment analysis according to value (ranging from −1000 to 1000)

Fig. 5.2  Sentiment analysis according to type of prejudice (Sexism, Racism, and both)

sexist). Figure 5.2 shows that although comments classified as sexist are slightly more negative, no statistically significant difference was found. The second hypothesis to be verified was the correlation between positive evaluativity and the use of proximal demonstrative determiners, and,

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Fig. 5.3  Distribution of determiners according to sentiment analysis

on the other hand, negative evaluativity and the use of distal demonstrative determiners. This effect is described by Potts and Schwarz (2008), who analysed online product reviews. Figure 5.3 shows that there are no significant differences in the distribution of the demonstrative determiners12 according to sentiment analysis. This result seems to contradict the prediction that demonstrative determiners which convey distal relations also convey negative sentiments. Considering the data above, and the variables ‘lemma’ and ‘sentiment analysis’ alone, there is no evidence to establish a correlation between distal relations primarily conveyed by demonstratives and sentiment values (negative or positive) associated with them. In this regard, our results seem to corroborate Lakoff’s findings in the sense that the “emotive uses” of English determiners this and that are “surprisingly close” (Lakoff, 1974: 349). The results make clear that the characteristics of the corpus influence the percentage of negative versus positive comments. Regardless of the lemma used, the percentage of positive comments always represents a minority of the overall comments. Nonetheless, the crosstab between sentiment and demonstrative determiner (cf. Table  5.4) indicates that aquelas and aqueles do not pattern alike: 80.6% of the comments with  Lemmas were used in order to group the determiners.

12

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Table 5.4 Distribution of sentiment analysis (negative, neuter and positive) according to determiner Sentiment analysis Aquela Aquelas Aquele Aqueles Essa Essas Esse Esses Esta Estas Este Estes Total

Count % Count % Count % Count % Count % Count % Count % Count % Count % Count % Count % Count % Count %

Negative

Neuter

Positive

Total

61 63.5% 25 80.6% 62 68.9% 22 53.7% 954 66.7% 218 60.2% 741 63.1% 297 61.2% 711 65.8% 118 65.2% 526 62.0% 166 64.6% 3901 64.2%

15 15.6% 2 6.5% 13 14.4% 10 24.4% 292 20.4% 62 17.1% 258 22.0% 100 20.6% 185 17.1% 26 14.4% 158 18.6% 47 18.3% 1168 19.2%

20 20.8% 4 12.9% 15 16.7% 9 22.0% 185 12.9% 82 22.7% 176 15.0% 88 18.1% 184 17.0% 37 20.4% 164 19.3% 44 17.1% 1008 16.6%

96 100.0% 31 100.0% 90 100.0% 41 100.0% 1431 100.0% 362 100.0% 1175 100.0% 485 100.0% 1080 100.0% 181 100.0% 848 100.0% 257 100.0% 6077 100.0%

aquelas (25 occurrences) are classified as negative. A refined analysis of the examples revealed that 5 out of the 25 occurrences with aquelas are followed by the noun pessoas (‘people’). In Portuguese this noun is feminine plural even though it makes reference to both male and female persons indistinctively. If we rule out these occurrences and recalculate the percentage of comments with a negative sentiment value, we conclude that 64.5% of the comments with aquelas are evaluated as negative. On the other hand, only 53.7% of the comments with aqueles (22 occurrences) are negative (p-value = 0.000).

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This result is clearly due to the types of prejudice that are predominant in these texts—sexism and racism. The fact that the gender asymmetry is higher in the use of aquel* is also suggestive of the solidarity effect that the author of the comment seeks to establish with the (presumably male) reader while establishing an affective distance from the female entity mentioned. Thus, in this context, there is a correlation between negativity and choice of the determiner that is used to mark proximity from speaker and reader. Figures 5.4 and 5.5, respectively, show the distribution of exclamatory (and non-exclamatory) comments according to lemma and sentiment analysis. It is possible to see the effect of exclamativity in comments with ess*: 70.8% of the comments are negative as opposed to 62% when the comments are non-exclamatory. The absence of an exclamation mark has also an effect on the rise of neuter comments except when the determiner is ess*. Thus, in comments with aquel* and ess*, exclamativity seems to trigger negative comments. On the other hand, the frequency of est* in exclamatory comments when conveying a negative sentiment is lower (62.6%) than in non-exclamatory comments (65.1%). These results may be explained if we consider that determiners that convey distal (affective) relations “establish emotional closeness between speaker and addressee”

Fig. 5.4  Distribution of exclamatory comments according to lemma and sentiment analysis

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Fig. 5.5  Distribution of non-exclamatory comments according to lemma and sentiment analysis

(Lakoff, 1974: 351). By using ess* or aquel*, the author of the comment seeks to entail agreement with the reader, and, at the same time, to establish an affective distance from the entity mentioned.

5.2 Proper Nouns versus Common Nouns When a demonstrative is used with a proper noun, it is not discriminating an entity from a larger set of entities. In fact, its use is referentially superfluous (Acton & Potts, 2014). Affective demonstratives are commonly used with proper names that the speaker presumes the addressee to be familiar with (Lakoff, 1974: 347). This entails shared information, thereby contributing to establishing a common ground and a sense of shared perspective with the addressee. As Acton and Potts (2014: 9) underline, this structure provides “an opportunity to develop or strengthen a sense of perspectival alignment”. The following comment exemplifies the use of Determiner + Proper Noun with the purpose of creating affective proximity between the speaker and the referent.

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(13) Cada vez admiro mais este André! Grande homem. (‘I admire this [masc.sg] André more and more! Great man.’) The examples below show that demonstrative determiners followed by a proper noun are also used to create detachment between the one who writes/speaks and the referent, and, at the same time, between the referent and the audience/reader. (14) Que nojo que dá essa Tvi. Despache-se lá a mandar os espanhóis fecharem a loja. (‘What disgust this [fem.sg] TVI causes. Hurry up and tell the Spaniards to close the shop.’) (15) Ah ahahahah esse André Ventura também parece um ciganito. Com esse bronze e de fatinho (‘Ha hahahaha this [masc.sg] André Ventura also looks like a little gypsy. With that tan and a little suit [derogatory diminutive]’ As shown by the examples in (13–15) above, sentences with demonstrative determiners combined with a proper noun are often evaluative. Figure 5.6 shows the distribution of determiners according to the type of noun that follows and the sentiment analysis labels. It reveals that the string Det + Common Noun retrieves both negative and positive sentiment more frequently than Det + Proper Noun constructions, which, in comparison, are more neuter.

Fig. 5.6  Distribution of determiners according to the type of noun and sentiment analysis (NETLANG subcorpus)

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Online comments containing demonstrative determiners followed by proper nouns classified as positive and even neuter may, in fact, be ironic or pejorative (Carvalho et  al., 2009). This means that the string Demonstrative Determiner + Proper Noun is likely to be classified as positive, especially when deprived of lexical cues tagged as negative, while the underlying message of the comment is negative or aims at triggering negative comments, as illustrated in example (16): (16) Esta Cristina é a senhora certa para nos apelar ao pensamento (‘This Cristina is the right lady to appeal to our thinking’) Even though comments classified as neuter in the NETLANG subcorpus may be interpreted as negative, the distribution of the demonstrative determiners according to the type of noun and sentiment contrasts with the one found in the reference corpus (Fig. 5.7): The results show that, in the Portuguese Web 2018 subcorpus, the sentiment value is not altered by the type of noun that follows the demonstrative determiner. The distribution of sentiment analysis according to the type of noun (common or proper) and the presence of exclamativity (Figs. 5.8 and 5.9) reveals a statistically significant pattern (p-value = 0.008): the negative sentiment value is higher when the determiner is followed by a proper noun and is inserted in an exclamatory sentence. On the contrary,

Fig. 5.7  Distribution of determiners according to the type of noun and sentiment analysis (Portuguese Web 2018 Subcorpus)

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Fig. 5.8  Distribution of Demonstrative Determiner + Proper Noun according to sentiment analysis and exclamativity

Fig. 5.9  Distribution of Demonstrative Determiner + Common Noun according to sentiment analysis and exclamativity

positive rated comments are higher when exclamativity is not present (cf. Figs. 5.8 and 5.9). The effect of exclamativity on the distribution of the string Demonstrative Determiner + Common Noun according to sentiment analysis revealed no significant differences.

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Concerning common nouns, the analysis of KWIC (Key Word in Context) revealed that the most frequent noun occurring after ess* is mulher ‘woman’ (251 hits), followed by tipo ‘guy’(171 hits), pessoa ‘person’ (143 hits), and país ‘country’ (110). The same analysis applied to est* revealed that the most frequent words are senhor ‘sir’ (143 hits), país ‘country’ (139 hits), tipo ‘guy’ (110 hits) and gente ‘people/folk’ (88 hits). Finally, the most frequent lemmas to the right of aquel* are país ‘country’ (11 hits), pessoa ‘person’ (10 hits), mulher ‘woman’(8 hits), m*rda ‘sh*t’ (6 hits). Looking at the data, there is a clear division between the demonstrative determiners that precede male and female addressees: before mulher ‘woman’, essa and aquela are more frequent, whereas este is more frequent before senhor ‘sir’. In Portuguese the masculine counterpart of mulher ‘woman’ is homem ‘man’. However, in our corpus, the most frequent term to name a male person is senhor ‘sir, mister’ (rather than homem ‘man’, while the most frequent term to name a female person is mulher ‘woman’ (rather than senhora ‘Mrs’). Since the terms ‘senhor’ and ‘senhora’ are more respectful than ‘homem’ and ‘mulher’, respectively, it seems clear that there is a bias towards using the most respectful term to refer to men rather than women. This correlates with choice of demonstrative: the form that indicates proximity with the speaker appears more often with the noun senhor ‘sir’, whereas the forms that indicate distance from speaker (ess*) and speaker and reader (aquel*) occur more often with the noun mulher ‘woman’. In this context, however, the use of senhor may be interpreted as ironic, considering that the commenter/writer intends to denigrate their point of view or actions. In fact, ironic (or sarcastic) messages in social media may reverse the polarity, as utterances evaluated as positive in terms of sentiment analysis may contain a negative message (Hernández Farías & Rosso, 2017). Another relevant aspect of this frequency analysis is the preference for general nouns (tipo, pessoa, gente, and m*rda) regardless of the demonstrative determiner—see examples (17) through (20). Although general nouns are more frequent in spoken discourse than in written texts (Brinton, 1996; Biber & Leech, 1999), the dynamic and dialogic aspect of online comments gives rise to the presence of features of spoken discourse. General nouns imply a “loose use of language” (Jucker et  al.,

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2003) and function as placeholders (Jucker et al., 2003; Andersen, 2010), as they fill in a syntactic slot that could have been occupied by the target noun. In the context of online written comments based on specific news, general nouns do not fill the syntactic slot of a delayed constituent due to retrieval problems (Andersen, 2010), as happens in oral speech, and they do not carry a sense of indeterminacy, because the target word may be easily retrieved from the text/video; rather, they are used to purposely convey contextual vagueness and inexact rendering. (17) Assassinos, terroristas e ladrões são heróis para estes tipos (‘Murderers, terrorists and thieves are heroes for these [masc.pl] guys’ (18) esta pessoa não fala por todos os portugueses (‘This [fem.sg] person does not speak for all Portuguese’) (19) Um[a] baixeza de gente de muito má pinta... Essa gente é mesmo de baixo nível. (‘A lowness of very trash looking people... That [fem.sg] crowd is really low level.’) (Folk, just like people, is never used in the singular. Therefore, the problem I pointed out in the previous version remains. I propose, to solve the issue, “That crowd”) (20) aquela pita a incentivar aquela merda é que merecia um belo par de estalos (‘that [fem.sg] chick encouraging that shit would deserve a nice couple of slaps’)

6 Conclusion The quantitative analysis of our sample of hate speech has revealed the impact of exclamativity on emotional deixis. Even though, overall, there is no significant difference in the distribution of the different demonstrative determiners according to sentiment analysis, the presence of exclamativity reveals a statistically significant pattern: the negative sentiment value is higher when the demonstrative is followed by a proper noun and is inserted in an exclamatory sentence. On the other hand, positively rated comments are more frequent when exclamativity is not present. Moreover, in comments with aquel* and ess*, exclamativity appears to trigger negative comments. By contrast, the frequency of est* in exclamatory comments when conveying a negative sentiment is lower (62.6%)

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than in non-exclamatory comments (62%). We therefore conclude that the interaction between exclamativity markers and the affective use of demonstrative determiners is a possible feature of hate speech in CMD. Furthermore, we found suggestive asymmetries concerning gender: 80.6% of the comments with aquelas are classified as negative against 53.7% of the comments with aqueles. The proximal form est* appears more often with the noun senhor ‘sir’ and the forms that indicate distance from speaker (ess*) and speaker and reader (aquel*) appear more often with the noun mulher ‘woman’. Most likely, this is an effect of the fact that ‘sexism’ was one of the two types of prejudice used to label the selection of comments examined here. Further research is needed to establish whether the bias detected can also be found in other types of hate speech samples. Although it is not possible to establish a direct correlation between the spatial deixis primarily conveyed by each demonstrative determiner and the affective values (positive and negative values retrieved from sentiment analysis) manifested in relation to the referent, demonstrative determiners are used to convey expressive meaning. More specifically, they establish a common ground between speaker/writer and hearer/reader from which assertions established as true arise. This is particularly evident when the demonstrative determiner is used in combination with a proper noun. Before concluding, a few comments are in order regarding the limitations of the present study. First, our criterion for exclamativity is probably too narrow. Search for other markers of exclamativity would most certainly allow for a more rigorous assessment of exclamativity. Second, considering that sentiment analysis and its value were obtained automatically, it is possible that some comments classified as neuter may be, in fact, ironic or sarcastic. As Pozzi et al. (2017: 9) emphasise, “[t]he difficulty in recognizing irony and sarcasm causes misunderstanding in everyday communication and poses problems to many natural language processing system”, including sentiment analysis. To sum up, although the results presented in this chapter are preliminary, as further research on the gender bias needs to be conducted, the results are promising and revealing of the complexity in the use of

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demonstrative determiners to convey expressive content and social meaning. As such, they are of potential interest to an understanding of the features of hate speech in CMD.

References Acton, E. K., & Potts, C. (2014). That straight talk: Sarah Palin and the sociolinguistics of demonstratives. Journal of Sociolinguistics, 18, 3–31. https://doi. org/10.1111/josl.12062 Andersen, G. (2010). A contrastive approach to vague nouns. In G. Kaltenböck, W. Mihatsch, & S. Schneider (Eds.), New approaches to hedging (pp. 35–47). Emerald Group Publishing. Argaman, E. (2007). With or without “it”: The role of empathetic deixis in mediating educational change. Journal of Pragmatics, 39, 1591–1607. Benamara, F., Inkpen, D., & Taboada, M. (2018). Introduction to the special issue on language in social media: Exploiting discourse and other contextual information. Computational Linguistics, 44(4), 663–681. Biber, D., & Leech, G. (1999). Longman grammar of written and spoken language. Pearson Education. Brinton, L. (1996). Pragmatic markers in English: Grammaticalization and discourse functions. Mouton de Gruyter. Carvalho, P., & Silva, M. (2015). SentiLex-PT: Principais características e potencialidades. Oslo Studies in Language, 7, 425–438. Carvalho, P., & Silva, M. (2017). SentiLex-PT 02. Accessed from ­https:// b2share.eudat.eu/records/93ab120efdaa4662baec6adee8e7585f Carvalho, P., Sarmento, L., Silva, M., & Oliveira, E. (2009). Clues for detecting irony in user-generated contents: Oh...!! it’s “so easy” ;-). In Proceedings of the international conference on information and knowledge management. https:// doi.org/10.1145/1651461.1651471 Chen, R. (1990). English demonstratives: A case of semantic expansion. Language Sciences, 12(2–3), 139–153. Cheshire, J. (1996). That jacksprat: An international perspective on English that. Journal of Pragmatics, 25, 369–393. Davidson T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated hate speech detection and the problem of offensive language. In Proceedings of the eleventh international AAAI conference on web and social media ICWSM, pp. 512–515.

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Davis, C., & Potts, C. (2010). Affective demonstratives and the division of pragmatic labor. In M. Aloni, B. Harald, H. Bastiaanse, T. de Jager, & K. Schulz (Eds.), Logic, language, and meaning: 17th Amsterdam colloquium revised selected papers (pp. 42–52). Springer. Ehret, K., & Taboada, M. (2020). Are online news comments like face-to-face conversation? A multi-dimensional analysis of an emerging register. Register Studies, 2, 1–36. Elias, C., Gonçalves, J., Araújo, M., Pinheiro, P., Araújo, C., & Henriques, P. (2021). NetAC, an automatic classifier of online hate speech comments. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & A. M. Ramalho Correia (Eds.), Trends and applications in information systems and technologies. WorldCIST 2021. Advances in intelligent systems and Computing, 1367. Springer. Henriques, P., Araújo, C., Ermida, I., & Dias, I. (2019). Scraping news sites and social networks for prejudice term analysis. In H. Weghorn & L. Rodrigues (Eds.), Proceedings of the 16th international conference on applied computing 2019 (pp. 179–189). Hernández Farías, D.  I., & Rosso, P. (2017). Irony, sarcasm, and sentiment analysis. In F. A. Pozzi, E. Fersini, E. Messina, & B. Liu (Eds.), Sentiment analysis in social networks (pp. 113–128). Elsevier. Herring, S. (2013). Discourse in web 2.0: Familiar, reconfigured, and emergent. In D. Tannen & A. M. Tester (Eds.), Georgetown University round table on languages and linguistics 2011: Discourse 2.0: Language and new media (pp. 1–26). Georgetown University Press. Jucker, A. H., Smith, S. W., & Lüdge, T. (2003). Interactive aspects of vagueness in conversation. Journal of Pragmatics, 35(12), 1737–1769. Lakoff, R. (1974). Remarks on ‘this’ and ‘that’. Proceedings of the Chicago Linguistics Society, 10, 345–356. Levinson, S. (1983). Pragmatics. Cambridge University Press. Levinson, S. (2004). Deixis. In L. R. Horn & G. Ward (Eds.), The handbook of pragmatics (pp. 97–121). Blackwell. Lopes, A. C. M. (2019). A deixis emocional em português europeu contemporâneo: alguns contributos. Revista da Associação Portuguesa de Linguística, 5, 225–235. Martins, R., Gomes, M., Almeida, J.  J., Novais, P., & Henriques, P. (2018). Hate speech classification in social media using emotional analysis. In 7th Brazilian conference on intelligent systems (BRACIS), pp. 61–66. Potts, C. & Schwarz, F. (2008). Exclamatives and heightened emotion: Extracting pragmatic generalizations from large corpora.

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Potts, C., & Schwarz, F. (2010). Affective ‘this’. Linguistic Issues in Language Technology, 3, 1–30. Pozzi, F. A., Fersini, E., Messina, E., & Liu, B. (2017). Challenges of sentiment analysis in social networks: An overview. In F. A. Pozzi, E. Fersini, E. Messina, & B. Liu (Eds.), Sentiment analysis in social networks (pp. 1–11). Elsevier.

6 Derogatory Linguistic Mechanisms in Danish Online Hate Speech Eckhard Bick

1 Introduction Discriminatory, negative stereotypes facilitate the vilification of the cultural other (Gümüş & Dural, 2012) and can be found in all modes of communication. Linguistically, they translate into hate speech (HS), when targeting people on the basis of religious, ethnic, physical or sexual characteristics,1 through discoursive mechanisms such as exclusion/othering, dehumanisation and generalisations, employing lexical vehicles such as derogatory metaphors and slurs (Meibauer, 2022), all of which are mentioned in Facebook’s HS policy (Facebook, 2022). In addition, some languages, such as Danish, use morphological means, employing derogatory prefixes and productive compounding to evoke a hateful  Discrimination of groups and group members based on “protected characteristics” such as these is a shared trait of most or all definitions of HS (Culpeper, 2021). 1

E. Bick (*) University of Southern Denmark, Odense M, Denmark e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_6

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narrative. In written form, HS is particularly prevalent and powerful in online social media with their less restrained, and more immediate form of communication, which is easy to share and access (Cervone et  al., 2020). Based on the Danish section of a large, annotated bilingual (German and Danish) corpus, this article examines the use of derogatory linguistic vehicles in the current Danish online discourse about refugees and immigrants. Given the target group and the ensuing focus on religion, culture and ethnicity, generalised HS, involving religious terms and quantity words, plays a larger role in our discussion and examples than (personal) directed HS marked by action words and sexual slurs, as predicted in ElSherief et al. (2018). In our analysis, we exploit linguistic annotation for a frequency-based inspection of the corpus, including a lexical snapshot of productive compounds and the extraction of syntactic attributions of derogatory content. The extracted examples2 are used to discuss and quantify various linguistic aspects of HS, both morphological and syntactic, showing, for instance, how (human) target lexemes are linked to (negative) sentiment carriers through processes such as stereotype-based word formation or metaphor projection, be it in the creation of slurs or the encoding of hateful narratives. Motivated by the morphological similarities between our two corpus languages and similar work ongoing for German (Bick, 2023), we place particular emphasis on productive compounding and its evocative use in HS (Jaki & De Smedt, 2019:14), a mechanism that is largely equivalent to how head-dependent patterns in English noun phrases are used for HS detection by Njagi et al. (2015).

2 The Corpus All examples and statistics presented here were drawn from the Danish section of a large bilingual social-media corpus compiled for linguistic research on the expression and perception of hate speech (XPEROHS, Baumgarten et  al., 2019), with a special focus on the refugee crisis,  All examples are authentic, albeit anonymous, XPEROHS corpus excerpts, quoted literatim without orthographial normalisation. 2

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immigration, Islam and nationalist discourse. The corpus contains German and Danish posts from both Facebook (FB) and Twitter (TW), compiled continually over a five-year period from late 2017 to mid 2022.3 The Twitter section is unabridged in a lexical sense, because high-­ frequency and function words were used as search terms for Twitter’s automatic query API, resulting in very large sub-corpora, 3 billion words for German and 360 million words for Danish. The FB section has a bias towards political discourse, because harvesting requests have to be linked to specific pages, and XPEROHS chose to focus on mass media and political parties. For this reason, and because FB stopped large-scale access to its API in July 2018, the concerning sub-corpora are smaller—200 million words for German and 40 million for Danish. The entire corpus was preprocessed for orthographical variation, tokenised and annotated linguistically (Bick, 2020a). The parsers used, GerGram (visl.sdu.dk/de/) for German and DanGram (visl.sdu.dk/da/) for Danish, are comparable in terms of annotation granularity, lexical coverage and performance. Both use the Constraint Grammar framework (Bick & Didriksen, 2015), and both provide full morphosyntactic annotation such as POS, inflection, compound analysis, etc. as well as functional and structural information (dependency trees). In addition, the annotation contains a semantic layer, with semantic ontologies for content words (~ 200 categories for nouns, ~120 for adjectives), named-­ entity recognition (20 classes), framenet structures (500 different frames) and semantic roles (50 categories). Both parsers underwent genre-­ adaptation, taking up various challenges related to computer-mediated communication (CMC), such as the recognition and classification of both (typographical) emoticons and emoji-pictograms,4 which are grouped in ten sentiment classes. Other adaptations concerned jargon abbreviations (lol, omg, etc.) and the lexical normalisation of misspelled  Both FB and TW claim to have greatly reduced HS content over this time span. Thus, FB claims to remove 95% of HS before it gets reported, and a fivefold reduction of HS violaons from 0.1% to 0.02% between 2020 and 2022 (Facebook, 2022). However, their prevalence metric is per number of views (HS posts/viewings), which can be affected by re-ranking or the relative growth and popularity of the proverbial cat video. In any case, reducing the visibility of HS does not necessarily educate the public to use it less. 4  For lack of a better hypernym, the two types could be lumped as “emoglyphs”. For a detailed discussion, see (Bick, 2020). 3

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13→10 dependency link hættemåger wordform: [hooded gulls] [hættemåge] lemma semantic ontology: bird-animal

conjunct (of 'kludemænd') N UTR P NOM IDF noun, common gender, plural, nominative, indefinite @P< argument of preposition ('til' [to]) §REC semantic role: recipient (of [pay])

17→13 dependency link tilbeder wordform: [worships] [tilbede] lemma

main verb

lexicalised V PR AKT verb, present tense, active

verb frame: like @FS-N< relative clause (of token 13) §ATR semantic role: attribute

Fig. 6.1  Parse tree with sample annotation fields

or intentionally altered orthography (all-lower-case, phonetic “spoken” spelling, upper-casing/letter-repetition for emphasis). Figure 6.1 illustrates the structural and morphosyntactic annotation levels for two content words in the following sentence: (1) ... vi danskere betaler 100 millioner kroner årligt til shariaskæggede kludemænd og overmalede hættemåger, som oven i købet tilbeder en terrorideologi, der har som mål at overtage vores land, Danmark... [we Danes pay 100 million kroner a year to sharia-bearded paltry-men and painted hooded gulls, which—to make matters worse—worship a terror ideology that aims at taking over our country, Denmark] Given the data harvesting method, the corpus only contains public posts that are (or were at the time of harvesting) freely accessible on the Internet. Nevertheless, in order to protect possible GDPR-sensitive information contained in the corpus, user names were pseudonomised (“@ twittername”) and corpus access restricted. Contextualisation links to the original TW and FB comment threads are stored in separate files and accessed through ID codes. The quantitative and qualitative analyses presented here were carried out using CorpusEye (corp.hum.sdu.dk), a graphical corpus search interface5 that allows the modular composition of complex searches on all  Internally, CorpusEye uses the CQP engine (http://cwb.sourceforge.net/) to create and access its search databases. 5

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linguistic levels. Search hits and concordance contexts can be statistically evaluated with both absolute and relative frequencies, n-grams, etc. An iterative quantitative-qualitative work flow is supported by enabling to re-enter concordance mode from individual lexical elements in the statistics list. Using measures such as relative frequency and the mutual information-­ measure it is possible to linguistically map stereotypical or derogatory features associated with HS minority targets, for instance as adnominal or predicative collocates. Also, the perception of minority members can be approximated linguistically through typical subject/agent or object/ patient relations, and the generalising use of pronouns (all, we/you, our/ your) may help to identify stereotypes and identification/othering constructions, associating speaker-assigned attributes with either an in-group or an out-group. It should be noted that the corpus-linguistic method advocated here is one of iterative quantitative-qualitative inspection, where a quantitative evaluation of a search pattern will allow an informed qualitative inspection of a corpus too large to “read through”. Concrete concordance examples will then help to improve or constrain the search pattern in question, giving rise to a new round of (better) statistics and (more focused) inspection. The process can be likened to the way a camera zoom works (Kalwa, 2019) and helps to delineate linguistically constituted concepts better than either method on its own. For instance, we can look for conceptual links between vold (violence) and various minority groups by performing a sequential search with vold* (includes inflection and compounds) as the first step, and ‘N & ’ as a second step. Relative frequency statistics for the group nouns in the concordance will yield the following ranking: dansker>muslim>statsborger>svensker>russer>udlænding>amerikaner This does not mean, however, that Danes are most violence-prone and Russians, foreigners and Americans less so. The sequential search only provides a “bag-of-words” co-occurrence link, and while the relative-­ frequency measure suggests a real relation and greatly facilitates corpus inspection, it does not tell us the type or polarity of the relation. Thus, a qualitative assessment of the results reveals that Danes and Swedes mostly occur as victims, while foreigners almost always are the perpetrators. On

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the backdrop of the Ukraine war, this is true of most hits on ‘Russian’, as well, while the picture for Americans is mixed. Statsborger (citizen) mostly occurs as “Danish citizen” and depicts a victim.

3 Morphology: Compounding and Affixation Like other Germanic languages with the exception of English, Danish is morphologically productive in hateful discourse, employing derogatory prefixes and evocative compounding. The compounds found in hate speech can be interpreted as condensed versions of constructions that would otherwise be found at higher linguistic levels (predications, metaphors, implicatures). Also, as Jaki and De Smedt (2019:14) point out, compounds, like adjective–noun combinations, can implement and expose dehumanisation and stereotyping mechanisms in hate speech by linguistically linking a (human) target to a derogatory word. This qualifies compounds for lexically based HS detection on par with, for example, the NP head-dependent patterns used for English by Njagi et  al. (2015), where the NPs had to contain both (a) an opinion- or metaphor-­ bearing element and (b) a theme relation to a HS target (religion, race or nation). That safe linguistic links between (a) and (b), syntactic or morphological (e.g. compounding), are important for automatic HS detection also follows from Malmasi and Zampieri’s (2017) finding that unigrams work better than bigrams, trigrams and skipgrams, arguably because the latter do not guarantee a direct word relation and get “diluted” by accidental adjacency.

3.1 Pejorative Word Formation Finkbeiner et al. (2016) list three core mechanisms for pejorative word formation for German, all of which can also be found in the Danish corpus: (a) pejorative modifiers, which in Danish consist of pejorative nouns used either as a kind of prefix (e.g. skide/lorte- ‘shitty’), or as regular

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compound first parts (e.g. taberreligion—‘loser religion’, den store luderbog Koranen—‘the Koran, the big hooker book) (b) pejorative noun heads (e.g. -pak ‘rabble’,—kælling ‘bitch’, halal-­ hundæde—‘halal-dog grub’), with a special sub-group of name heads (e.g. skvatmikkel—‘weakling’, tudemikkel—‘whining Mikkel’, sladdermikkel ‘slander Mikkel’) (c) pejorative derivation, for example—eri (hykleri—‘hypocrisy’, tuderi—‘whining’, dyr(e)plageri—‘animal mistreatment’) and—ling (svækling—‘weakling’, fedling—‘fat person’, usling—‘mean person’, gal(n)ing—‘rabid person’). The two suffixes are also found in many neutral words, but productive usage tends to have a negative connotation. A further mechanism that should be mentioned here is: (d) co-pejoration, where a derogatory word is built from otherwise neutral, or even positive, parts (e.g. hudfarveordfører—‘skin colour speaker’, rygklapper—‘back patter’). Also, first parts are not always modifiers, but can also be: (e) “syntactic” complements of the second part (e.g. islamkær—‘Islam-­ friendly’, gedeknepper—‘goat fucker’). Obviously, compound parts can also all be negative (e.g. terrorsvin— ‘terror pig’, perkerkælling—‘perker bitch’).

3.2 Compound Slurs For the discussion of HS against minorities, the pejoration subfield of ethnic, racial or religious slurs is prototypical and of special interest (Cepollaro & Zeman, 2020). While many types of derogatory words can be used to verbally attack individuals, such slurs fulfil the HS criterion already at the word level by combining pejorativity with group membership. In his expressivist account of slurs, Jeshion (2016) suggests that the personal contempt expressed in slur creation has a dehumanising effect

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and that usage leads to a crystallisation of derogatory context on the term itself (established slurs). Jeshion’s expressivist take aligns well with our corpus data, where relatively few slurs are established simplex words, while most are ad hoc compound creations combining an expressive component with a group-targeting component. In Technau’s (2021) four-way classification of “insult words” (Beleidigungswörter), based on whether or not they target a person group (±PG) and whether or not they have a non-pejorative correlate (±NPC), slurs proper belong to the +PG + NPC category. Surprisingly, the most established ethnicity-targeting slur lexeme (ethnopaulism) in Danish is a word without a clear NPC: perker (foreigner, especially Middle East). The word occurs in many compound variations, for example, perkerkælling (perker bitch) or præmieperker (premium perker). The term neger (black, nigger) has formally moved from NPC to ethnopaulism slur, but the picture is somewhat mixed in the corpus, with speakers disagreeing about its derogatory content. In other, less lexicalised words, the group relation is based on body features (skævøje—‘squint eye’/Chinese), associated animals (gedeknepper—goat fucker, kamelknepper—camel fucker, for Arabs) or religious/cultural references (alahsvin—‘Allah pig’, Muslim, hvidløg perker—garlic perker). In addition, ad hoc slurs can be constructed by adding a group reference modifier to a -PG derogation element (e.g. body parts or animals): muslimpik (‘Muslim dick’), muslimsvin (‘Muslim pig’), arabersvans (‘Arab dick’), araberrotte (‘Arab rat’). A common insulting trope involves sexual deviation (homosexuality, pedophilia): bøssesvin (‘homo pig’), børneboller (‘child molester’), røvskubber (‘ass pusher’). These words are +PG, but used to denigrate another PG (immigrants/Muslims), and for some of the words, even non-slang equivalents have a pejorative connotation. Unlike ethnic and religious slurs, the group relation has to be ensured through the context, often with an adjective, or by coordinating the word with another slur. Finally, -PG insult words are also used, some with an NPC (svagpisser—‘weak pee’er’/weakling, åndsamøbe—‘intellectual amoeba’/stupid, skvatrøv—‘weak ass’/weakling), some with only a negative reference (voldtægtssvin—‘rapist pig’, mordersvin—‘murderer pig’, terrorsvin—‘terrorist pig’). These terms are not only used to insult

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individuals belonging to a minority group (directed HS), but also for the group as a whole (generalised HS), either in the plural or by adding an adjective denoting ethnicity or religion.

4 Counter Hate and Secondary Hate Speech Derogatory word formations occur on both sides of the minority HS discourse, though immigrant HS against the Danish majority is harder to find in the corpus, arguably because of the limited Danish-language social-media participation of Arabic speakers. Examples of such “counter hate speech” (CHS) are—svin compounds (danskersvin—‘Dane-pig’, racistsvin—‘racist pig’, nazistsvin—‘nazi pig’) and the above-mentioned præmieperker, that is used for what is perceived as a cultural traitor who lets himself be “integrated” and used as a showcase excuse on the backdrop of general discrimination. Many posts claim that luder (slut) is often used by immigrants as a derogatory label for Danish women, but the actual use is difficult to document in Danish TW and FB. What can be found is a picture of Danes as unprincipled and morally degenerated: (2a) H  vad ved I danskere overhovedet om moral, de fleste af jer er født udenfor ægteskabet, I accepterer alle mulige perversiterer som homoseksualitet, dyresex og promiskuøs adfærd. (What do you Danes even know about morals, most of you were born bastards. You accept all kinds of perversities such as homosexualism, sodomy and promiscuous behaviour) The concept of counter speech (CS) was originally linked to the idea of counter-balancing pejorative discourse by using positive terms instead (Hudson, 2017) and is here defined in a wider sense as posts by Danes defending immigrants against false, demeaning and generalising claims (Buerger & Wright, 2019). Such CS typically attacks individuals or political movements, and rarely qualifies as HS, but it still uses some of the same linguistic and narrative tools, such as generalisation (2c), othering (2b) and dehumanising metaphors (e.g. sump [swamp], 2d).

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(2b) S ig mig, har I nazispader ikke fundet ud af, at danskerne ikke gider høre på jeres ævl?! (Tell me, haven’t you Nazi idiots figured out that Danes don’t want to listen to your rubbish) (2c) H  ey Pernille, Jeg kaster op over alle de racister i dit parti som udgiver sig for at være politikker. (Hi Pernille, I am sick of all those racists in your party who call themselves politicians) (2d) h øjreekstreme partier der fisker i sumpen af islamfrygt (right-wing extremist parties fishing in the swamp of Islam angst) Often, entire parties or media are labelled as racist, especially Dansk Folkeparti (DF). A typical construction is ‘NPneg + i + party’, where NPneg is a derogative, generalised in the definite plural: (2e) hyggeracisterne (those arm chair racists), fremmedhaderidioterne (those xenophobic idiots), de galninge (those insane people), tumperne/tosserne (those idiots), mørkemændene (the evil forces), taberne (those losers) etc. Statistically speaking, though, rather than slurs and outright HS, CS simply uses certain negative-sentiment keywords as code markers, for example, ‘hater’, ‘hetz’, ‘angst’: islam-/muslim-/flygtningehader (Islam/ Muslim/refugee hater), flygtninge-/fremmedhetz (refugee/foreigner bullying), islamangst/-forskrækkelse (Islam anxiousness). In its reaction to CS, we find a great deal of HS in what we will here, with a newly coined term, call “secondary hate speech” or counter CS (CCS). This kind of HS is “secondary” in the sense that it is reactive to CS, levied by the right-wing targets of CS at what they perceive as a leftist 5th column (islamelsker—‘Islam lover’, islamapologet—‘Islam excuser’, perkerelsker—‘foreigner lover’, islamliderlig—‘Islam horny’, islamomfavnelse—‘Islam hugging’, islamluder—‘Islam hore’, kryptoislamist— ‘crypto-islamist’), as an establishment prone to “opinion censorship” (sindelagskontrol) and lopsided media coverage (løgnepresse—‘lying media’, mikrofonholderi—‘microphone providing’) or—in the very

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least—naive “softies” (pladderhumanist—‘hogwash humanist’, venligboer—‘friendling’, muslimnaiv—‘Muslim-naive’, rygklapper—‘back-­ patter’). The term venligboer refers to Venligboerne (= ‘friendly locals’),6 a refugee support group now active in several countries, but is used as a HS term in CCS, with variations such as venligtosse (softie idiot) or the pun væmmeligboer (abominable softie). It is part of the CCS censorship narrative that there is a double standard for the very definition of HS, victimising what is perceived as the true perpetrators (offerkortet/−fortælling—victim trump card/narrative), but ignoring abuse directed at Danes: (2f ) M  en når indvandrere kalder os for fucking ludere, danskersvin og rådne bacon-svin, så beder vi naturligvis selv om det. (But when immigrants call us fucking hores, Dane-pigs or rotten bacon pigs, it must naturally be our own fault) Some word creations and spelling variation in the use of slurs are arguably connected to real rather than perceived censorship, and functions to circumvent automatic HS detection algorithms used by social media networks. Examples are the phonetic variations perle (pearl) instead of perker and muslinger (mussles) instead of Muslim: (2g) S kyd muslingerne når de laver ballade på sygehuse. (Shoot the mussles when they make a row at a hospital) (2h) S å en klaphat, send det skide perler hjem (What an idiot, sent the fucking pearl’er home)

5 Compounds as Narrative and Stereotype Carriers in the Immigrant/ Refugee Discourse In our corpus, both the minority-targeting HS and the HS found in CCS can be linked to a number of well-defined stereotypes and narratives, at both the utterance and the lexical levels. For instance, the compound  This is a pun linked to the term vendelbo (plural: venligboer), denoting the inhabitants of the province in which the movement was founded. 6

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skægbørn (beard children) refers to young immigrant men lying about their age to trigger more lenient asylum rules. In the example, this is combined with the rape culture stereotype, all wrapped up in a HS rant against an entirely different target group (“me-too hippies”). (3) skide spelthippier så kan islamister og skægbørn voldtage løs, dem nævner metootosserne ikke. (fucking dinkel hippies ... so islamists and beard children can rape away, the me-too idiots don’t mention them) Other examples of narrative-carrying word coinings are vinkedansker (‘waving Dane’, welcomer), referring to do-gooders welcoming refugees at train stations and border crossings, or asylindvandrer (asylum immigrant), about (economically motivated) immigrants posing as asylum seeking refugees. But also mainstream Danish has its share of compounds that can only be understood on against the backdrop of the narrative they trigger, for example, ghettopakke (law package to prevent neighbourhoods and schools to become immigrant-only), tvangsvuggestue/ vuggestuetvang (forced creche, to Danify immigrant children), raceprofilering (race profiling, euphemism for police discrimination).

5.1 Stereotype: Culture of Violence This stereotype suggests that violence is inherent in Muslim culture. It is expressed in compounds like voldskultur (culture of violence), voldsparat/−beredt or terrorparat (violence/terror-prone), stammekrigskultur (tribe warfare culture), underkastelseskultur (submission culture), etc. On the backdrop of the ISIS caliphate, terms like halsoverskæringer (beheadings), dødsregime (death regime) and koransoldat (Koran soldier) are used to reinforce and “prove” the violence stereotype, as does the related narrative of a rape-sanctioning, misogynous patriarchate (voldspartriarkat), where HS posts can cite immigrant-laden rape statistics and cases of gang rape. (4a) d it parti @Enhedslisten i lighed med @larsloekke tækkes en voldsforherligende og udansk religion som islam (Your party [Socialists] and [prime minister] bow to a violence-worshipping and un-Danish religion such as Islam)

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Many posts peddle this stereotype by pointing to a lack of freedom enforced on religious grounds: tvangsreligion (forced religion), tvangskonvertere (to force-convert), tørklædetvang (forced head scarf ) and shariavagter (sharia police, a ghetto phenomenon). We exploited the framenet and semantic role annotation of the corpus to examine whether Muslims are framed more as victims or more as perpetrators. For the ‘attack’ frame, with non-passive verb constructions, the lexeme ‘Muslim’ occurred 30% more frequently as subject/agent than as object/patient. A further difference was that, as subjects/agents, Muslims were portrayed as physically aggressive (4b), while many of the object/ patient instances were about verbal attacks against Muslims, not physical violence (4c). Also, the latter were often CS. (4b) Muslimer fra Gellerup angriber brandbiler med fyrværkeri. (Muslims from Gellerup [ghetto] attack fire trucks with fire crackers) (4c) højrefløjen angriber muslimer med at de er homofober alt imens de selv er det (Right-wingers attack Muslims as being homophobic, while they themselves are it)

5.2 Stereotype: Muslim Culture Is Primitive Depicting a target group as primitive is a very effective HS trope. Key morphemes in the involved compounds make reference to desert tribes (stammekultur, ørkenreligion), the middle ages (middelalderkultur) or even the Stone Age (stenalderideologi). Some posts conjure up a threatening, uncivilised mob: muslim-horde, pøbelvælde (rabble reign). (5a) D  ette kan kun stoppes ved at fordrive disse muslimer tilbage til de stenhuler hvor de høre til og hvor man slås hele dagen!! (This can only be stopped by banishing these Muslims back to the stone caves they belong in, and where you fight all day) This stereotype mashes with the misogyny stereotype (4.3.), with claims that archaic traditions like ritual circumcision and honour killings (æresdræb) are supported by all Islam and Muslims in general.

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5.3 Stereotype: Islam Is Misogynous and Paedophilic There is a relatively broad consensus in Denmark, extending into the establishment, that the world’s Muslim women enjoy fewer freedoms than Danish women. HS discourse goes beyond this and claims that the oppression of Muslim women is enshrined in Islam itself and attributes it malice and misogyny (voldtægts/overgrebs-kultur—culture of rape, voldspatriarki—patriarchy of violence, Islams kvindeslaveri—Islam’s enslaving of women). Word creations supporting this stereotype also address marriage practices illegal in Denmark, for example, barnebrud (child bride), flerkoneri (“polygamy’ing”) and tvangsgifteri (forced “marriageing”), the two latter ones with the pejoration suffix—eri. The compound fødemaskine (birthing machine) dehumanises Muslim women, but at the same time touches on the narrative that Muslim immigrants consciously exploit demography, intending to outbirth the Danish population. By extension, this stereotype regards Muslim men as potential rapists of Danish women: (6a) A  t nogle kvinder klæder sig tiltrækkende, er ikke ensbetydende med, at de er «gramsningsklare» selv om mange muslimer påstår det... (That some women dress provocatively, that doesn’t mean they are “ready for groping”, even though many Muslims say so) The low age limit for (arranged) marriages is used to depict Muslim men—or even the prophet or Allah—as paedophiles, a very serious insult in Danish (børnesexslave—child sex slave, pædofilikultur, tvangspædofiliægteskab—forced paedophilia marriage, pædofil himmelnisse—paedophile god gnome). (6b) D  e bliver hjernevasket om omskåret og babysex er mainstream islam. (They are brain-washed and circumcised, and baby sex is mainstream Islam)

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5.4 HS Narrative: Immigration by Cheating and Welfare Tourism It is a common HS trope in our corpus that Middle-Eastern and African immigrants cheat the Danish system by posing as refugees (asylsvindleri/ asylsnyd—asylum cheating, flygtningesvindel—refugee scam) or through false marriages (fupægteskab), with the sole intention of siphoning Danish welfare. Relevant pejorative word creations incorporate the word nasse (exploit) and the parasite metaphor (snylter): islam−/velfærdsnasser (Islam/ welfare “exploitist”), nasseflygtning (“exploitist” refugee), samfundssnylter (social parasite). Refugees are seen as fake and not in need (bekvemmelighedsmigrant/-flygtning—migrant/refugee of convenience, luksusflygtning—luxury refugee, velfærdsturist—welfare tourist), with Denmark as a kind of “promised land of welfare” (bistandsparadis—welfare paradise) with a naive refugee policy (asylhul—asylum loophole, asylmagnet—asylum magnet) and a tendency to spoil immigrants (curling-­ Denmark, inspired by curling-forældre—parents that constantly sweep the ice for their children). The high social and economic cost of Danish asylum and immigration policy is also a common trope, centred around the concepts of ‘burden’ (byrde, belastning, pres), ‘pressure’ (pres, skrue) and ‘weight’ (vægt), combined with target-group terms, for example, muslim-/ udlændinge-/flygtninge-byrde, flygtningepres, flygtningeskrue (refugee vice), muslim-/indvandrertung (Muslim-heavy, immigrant-heavy [e.g. neighbourhood]).

5.5 HS Narrative: Islamisation Master Plan Another HS narrative pictures Muslim immigration as a planned, organised process (migrantindustri—migration industry, muslimimport— import of Muslims, flygtningerykind—refugee “moving in”, folkevandring—tribal migration), with the goal of subverting and ultimately Islamising Denmark (islamiseringskampagne—Islamisation campaign, folkeudskiftning—population substitution. The human rights principle of keeping families together is interpreted as an immigration vehicle by design (familiesammenføringsmaskine—family fetching

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machine, kædeindvandring—immigration chain). It is also often suggested that the Islamisation master plan has help from the Danish establishment criticised as über-åben (too welcoming): migrantlobby, masseimport af flygtninge (mass import of refugees). Even the war metaphor is used in this narrative, for example, masseinvasion (mass invasion), immigranthær (immigration army), erobringsideologi (conquest ideology): (7) d en muslimske invasionshær nogle kalder for flygtninge, men som vi andre kalder berigelsestiggere (the Muslim invasion army some call refugees, but which we others call ‘enrichment beggars’7)

6 Dehumanising Metaphors Like generalisation/depersonalisation, stripping a (verbal) victim of his/ her humanity facilitates HS (Haslam & Murphy, 2020) and changes the threshold for incitement to (or even possible enacting of ) violence. Not least in social media, such a dehumanising effect can be achieved by employing non-human vehicles of metaphor (Demjén & Hardaker, 2017), for instance, by likening people to trash: (8a) A  t perkere er affald er svært at argumentere imod (That foreigners-slur are trash is difficult to argue against) (8b) [ Danmark]... er om få år en muslimsk kloark, fyldt med indavlet affald. (In a few years, [Denmark] will be a Muslim sewer, filled with in-­ bred trash) The three most common dehumanisation tropes in our Danish corpus were ‘Islam as a disease’, ‘Muslims as animals’ and ‘immigration as a natural disaster’.

 This term is a narrative in its own right: ‘berigelse’, a legal term used in connection with embezzlement and other economic crime, here clashes with tigger (beggar), insinuating that refugees are fake and not in need. 7

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6.1 Metaphor: Immigration as a Natural Disaster In this narrative, mass immigration or the arrival of refugees in force is depicted as an unstoppable natural force overwhelming the country, the main trope being flooding: flygtningetsunami (refugee tsunami), migrantbølge (migrant wave), flygtningestorm (refugee storm8) The metaphor was used even though Denmark had a smaller influx than neighbouring countries, and very strict immigration laws to begin with. (9) v i er ved at oversvømmes af muslimer og deres afkom (We are flooded with Muslims and their offspring)

6.2 Metaphor: Islam Is a Disease The disease metaphor is a variant of the natural disaster metaphor. It, too, raises the spectre of a blind threat arising of its own accord, but it suggests more a process than a one-time event: A disease progresses, gets worse and can be cured. A common variant is depicting Islam as an infection: islamitis, muslimitis, islamiserings-virus (Islamisation virus), islam-/musliminficere (Islam/Muslim-infect), islam syndrom. The scope of the problem is emphasised by words like ‘spread’ and ‘epidemic’, for instance by alluding to the black death (koranpest, den sorte syge—the black disease). Another image involving spread is that of a cancer slowly erasing Danish/ European culture. (10a) Islam er en kræftsvulst ude af kontrol. (Islam is a cancer out of control) (10b) I slams ideoligier breder sig som en pest over vores skrøbelige demokrati. (Islam’s ideologies spread across our fragile democracy like a pest) (10c) D  e muslimer er blevet en byld i røven på os alle... (These Muslims have become a boil in all our asses) (10d) m  oskeerne, hvor imamer spreder sine metastaser ud i samfundet (The mosques where imams spread their metastases out across society)

 The word can also be translated as ‘onslaught’, serving the war metaphor at the same time.

8

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6.3 Animal Metaphors Animal metaphors are, of course, common in ordinary language. Thus, politicians “failing” in the handling of the refugee crisis are likened to ostriches sticking their head in the sand (strudsemetode) or passing apes in a game of blame (sidde med aben). When they give up on a principle, they ‘swallow a camel’ (sluge en kamel). Unlike these lexicalised expressions, dehumanising animal metaphors target victims based on group membership, often using animals that are perceived as unclean, pests or parasites (e.g. perker rotter—perker rats, muslimsk utøj—Muslim vermin). A common trope is that Muslims multiply like vermin, using animal specific words and compound parts like yngle (breed), rede (nest), udklækning (hatching): (11a) islam-/araberyngel (Islam/Arab brood), perkerrede (perker nest) (11b) J ævn den med jorden, sammen med de andre muslimske udklæknings-­ friskoler. (Tear it down, together with the other Muslim-hatching free-­schools9) Another animal trope is that of Muslims “occurring” en masse, in packs or swarms, or like pests, as in the following Muslim-related excerpts covering a variety of negative-sentiment animals: (11c) fl okoverfald (pack attack), en flok morderiske kakerlakker (a swarm of murderous cockroaches) (11d) fi  nde sammen med andre lemminger (band together with other lemmings) (11e) o verfaldet af muslimske unge i flok ligesom sjakaler (assaulted by a band of Muslim youngsters, like jackals) Animal association can also be achieved indirectly by employing words that are lexically constrained to an animal context: muslimer fodder (Muslim ‘animal-food’), halal-hundeæde (halal ‘dog-only food’).  Under Danish law it is possible to establish private, for example, religious, “free-schools” with about 75% public subsidies. 9

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When directly likening minority members to animals, svin (pig) is often used, because it is both a common Danish insult word and especially offensive as a taboo animal in Islam. Reference is also made to animals culturally linked to the perceived primitive origins of Muslims/ Arabs, in particular goats, apes10 and camels, sometimes in the form of sexual slurs (gedeknepper—goat fucker, kamel-pikslikker—camel cock-­ sucker) and/or in the combination of other demeaning words or compound parts: ♂

♂

(11f ) H  vad fanden laver de kamelkneppere heroppe (What the hell are these camel-fuckers doing up here? (11g) H  itler sammenlignede jøder med rotter, du muslimer med græshoppeplager. Same same. (Hitler compared Jews with rats, you Muslims with locust plagues. Same same.) (11h) ... lukke deres rottereder af mósker (... close their rat nests of mosques) One uniquely Danish animal term, hættemåge (hooded gull), alludes to the Muslim head scarf: (11i) H  eldigvis er ikke alle muslimer religiøse hættemåger. (Luckily, not all Muslims are religious hooded gulls)

7 The Use of Emoglyphs in HS Obviously, negative “emoglyphs”, be it in the form of emoticons or emojis, are ubiquitous in HS, with the latter more and more displacing the former. Emojis, or equivalent emoticons, can be used to increase HS concordance hits or to measure the negativity of concepts associated with them (cp. 10.1) In particular, this holds—in our classification—for the ‘angry’ and ‘horror’ classes, but also for individual negative emojis such as (‘dislike’). (‘vomiting’) and (‘fuck-finger’, 12b). The arguably most direct HS emoji is (‘gun’, 12a), or the triple (‘bomb-explosion-gun’).  Animal slurs are much rarer in counter speech, but examples can be found for dogs, pigs and apes, for example, the anti-right wing racistiske brøleaber (racist howler monkeys). 10

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(12a) e ndnu en araber der skal ha en kugle i skallen... (yet another Arab that deserves a bullet in the skull) (12b) Smid dem af toget i abdullaland (Throw them off a train in Abdullah-country) Apart from expressing negative sentiment, emojis can also be an efficient dehumanisation tool supporting all of the above narratives. In particular, we find (‘pile-of-poo’) and animal emojis such as (‘ape/ monkey’), (‘gorilla’), (‘monkey-face’), (‘camel’) or (‘pig’). In addition to dehumanisation, the animals emojis are associated with derogatory attributes, for example, stupid (ape), primitive (camel) or unclean (pig).

8 Syntactic Mechanisms 8.1 Generalisation Mechanisms Generalisations and othering have a well-established relation to HS. Both manifest syntactically, mainly by adding certain pronouns before target-­ specific group words in the plural. Thus, the search string ‘alle’+'de’+(ADJ @>N)?+N & [alle=‘all’, de=‘the/those’] can extract generalisations for human nouns semantically related to nationality, ethnicity and ideology. Ranked by relative frequency, the top hits in the resulting concordance were udlænding (foreigner), muslim and ukrainer (Ukrainian). Manual inspection revealed that 94% of the udlænding hits and 89% of the muslim hits carried negative sentiment or were outright HS. That negative sentiment is the default in these generalisations is also supported by the fact that half of the few positive cases were marked by the use of a positive prenominal adjective, while the overall share of adjective use in the hits was only 25%. Interestingly, for Ukrainians the negative sentiment was only 20%, and more moderate, for example, ‘will there be work for them all?’ rather than ‘throw them out!’. This shows that generalisation alone cannot be used as a sufficient criterion for HS.  The same is true to an even higher degree for a

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morphological generalisation marker, definite-plural inflection. Still, the feature constrains the search space for inspection, and is particularly useful to identify stereotypes: (13a) u dlændingene hved udmærket godt at danskerne ikke må forsvare sig... hvis det havde været tilladt så ville de nok være nervøse for at bryde ind... (The foreigners know well that Danes mustn’t defend themselves [with weapons]... if that were allowed, they would think twice before committing burglary) A more subtle form for generalisation is the use of the HS target in the genitive, followed by the desired derogatory attribution, for example, Islams racisme (the racism of Islam), muslimer vold/knivdræb/terror (the violence/knife killings/terrorism of Muslims). In Danish, the genitive is here equivalent to saying “all (of )”, but without the quantifier, the assertion escapes scrutiny and is harder to refute because it is not explicit, but implied. An interesting lexical generalisation is the HS reference to a diffuse “place of otherhood”, constructed as ‘-land’ compounds, often in the plural, as a further means of generalisation: muzziland (slur-Muslim country), perkerland (foreigner-country), sharialand, sharialov-lande, sans−/ ørkenlande (sand/desert countries), taberlande (loser countries), islamistiske/muslimske lortelande (Islamist/Muslim shit-countries), kamelklapperland (camel patter country). (13b) F  olk med oprindelse i muslimlande yngler betydeligt mere end danskere. (People from Muslimistans breed considerably faster than Danes.) (13c) M  en vis du mener voldtægt og islam er lykken så flyt dog til araber(But if you think rape and Islam mean happiness, just land move to Arabistan ) Both examples exhibit further linguistic means of pejoration: in the first, the dehumanising animal term yngle (breed) is used, and in the second, islam is coordinated—and hereby equated—with the unambiguously negative voldtægt (rape). Also, (13c) contains dehumanising emojis, ‘ape’ and ‘faeces’.

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Only rarely is a specific country addressed: (13d) S omalia er et skodland med 99% islam og ca. IQ68. (Somalia is a derogative-prefix-country with 99% Islam and an IQ of ca. 68). Lexical generalisation can also be conceptual. Thus, the word koranklodser (Koran blocks), coined by a Danish right-wing party, denotes concrete blocks used to prevent terrorists from attacking people by driving cars or trucks into them in pedestrian zones. The word “works” by insinuating that there is a general connection between terrorism and the Koran that we have to protect against. At the syntactic level, the effect of coordination (13c) as a linguistic tool for generalisation can be enhanced by adding “and other” or “and similar”, triggering an iterative presupposition (Levinson, 1983:182): (13e) M  uslimer og andre uintegrebare kriminelle udlændinge koster mindst 100 milliarder årligt i Danmark. (Muslims and other un-integrateable criminals cost Denmark at least 100 billion a year) Here, the second conjunct “rubs off” on the first, implying that all Muslims are (also) criminal. The technique can also be used to spread truth value or negative sentiment between list members. In (13f ), the otherwise positive idea of dialogue (dialogkaffe) is put on par with terror and rape, and the list effect turns individual crimes into general features of Islam: (13f ) a sylsvindleri, gruppevoldtægter, velfærdsnasseri, dominansvold, æresdrab, sharia zoner, dialogkaffe og terrorangreb, gør at jeg ikke kan tolerere en person der støtter, eller på nogen måde er venlig overfor de islamiske bosættere. (Asylum cheating, gang rapes, welfare exploitation, submission violence, honour killings, Sharia zones, dialogue coffee and terror attacks mean that I cannot tolerate people who support, or in anyway befriend these Islamic settlers) The corpus contains a great deal of very negative posts on the topic of Islam and immigration that cannot be classified as HS, but may be associated with HS elsewhere in the same thread, subscribe to the same

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stereotypes and narratives (cp. Sect. 5), and often advocate the same “solutions” or hashtags (e.g. [#]indvandringsstop—immigration stop, [#]straksudvisning—immediate expulsion), just in more moderate language. Most of these posts employ generalisation as an illocutionary tool (increasing assertiveness), but radiate argumentative factuality, evidentiality11 and objectivity by quoting statistics (13g), or personal testimony: (13g) D  et er kun 77% af de herboende muslimer, der vil dræbe os. I Tyskland er de helt nede på 30% af muslimerne, der vil dræbe tyskere til fordel for islam. (Only 77% of Muslims in Denmark want to kill us. In Germany that number is down at 30% of Muslims prepared to kill Germans for Islam)

8.2 Othering and Insults Using Personal Pronoun Constructions As in English, pronoun othering in Danish works by using a 1st or 2nd person pronoun in the nominative before the target group or self noun (also plural): (14a) N  u har I muslimer nasset på det danske system i 40 år... (Now you Muslims have exploited the Danish [welfare] system for 40 years) The construction is not common in the corpus, but a safe othering marker. More common is the combination of a 2nd person possessive pronoun with a negative-sentiment adjective and a minority target noun. This is a safe insult construction in Danish (14b), and even works without a derogatory adjective, if the noun itself is a slur (14c). General (-PG) insult words (14e) can be directed at minorities by adding group-specific adjectives. In the absence of an insult word, the possessive projects its insulting illocutionary force onto a common noun (14d).

 Danish does not mark evidentiality as a grammatical category, but an authoritative way of expressing it is quoting statistics, or borrowing terminology from statistics (e.g. overkriminalitet – higher-than-average crime rate). 11

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(14b) t ænk på at os danskere betaler din fkn mad din fede perker/din fede muslim (remember that it is us Danes who pay your fucking food, you fat perker [slur]/you fat Muslim) (14c) Ud af mit land din perker (out of my country you perker [slur]) (14d) piss af din russer bot/trold (piss off you Russian bot/troll) (14e) jo før du dør, jo bedre, dit islamistiske møgsvin!!! (the sooner you die, the better, you Islamist shit-pig) A search for the din + N insulting construction with a human noun head works as a kind of slur machine; the resulting concordance churns out an inventory of slurs (or attributes that can be turned into ad hoc slurs by the 2nd possessive), all or most of them occurring in HS contexts in the corpus. With a semantic restriction to , and , we can look for +PG slurs: mongol (mongol), racist, neger (nigger), nazi, jøde (Jew), kommunist (communist), nazist, perker, fa(s)cist, vendekåbe (turncoat), satanist, medløber (accomplice), sexist, sigøjner (gipsy), mandschauvinist (male chauvinist), farisæer, kætter (apostate), grønlænder (Greenlander), eskimo (Inuit-slur), beduin (bedouin), skrælling (Viking Inuit-slur), sodomit, etc. In this list, only the words perker, sigøjner, neger, eskimo and skrælling, as well as mongol in the sense, are safely +NPC and hereby lexical slurs. The others are functional slurs when used for a group or person outside the original meaning space—either because they have a derogatory semantics or simply because they get prefixed with din. The former is true of most words in the list (nazi, nazist, mandschauvinist, medløber, vendekøbe, satanist, sexist), the latter applies to (grønlænder, jøde). With the exception of ‘Jew’, nationality words with din are hapaxes in the corpus, and in spite of the HS directed at Muslims, the word muslim does not occur in the din constrution at all, indicating that its core semantics is not (yet) negative, or that religious terms are more resistant to lexical negativity assignment than political ideology terms (e.g. nazi/nazist, kommunist). This does not mean, however, that these words are immune to slurification, as they still can occur in compound slurs (Sect. 3.2). Thus, the above list also contains kæleafghaner (pet Afghan), lortekommunist (shit communist), alongside all-part negative compounds such as snot-mongol (snotty mongole), perkerkælling (perker bitch), and muslim allows the din construction in

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the presence of a negative attribute: din lede/lorte/taber muslim (you disgusting/shitty/loser Muslim). The din + N search can provide a much more unabridged, -PG, list of slurs when it is semantically constrained for (human attributive nouns) rather than for group-membership nouns such as . A large part of these more general slurs makes allusion to reduced mental of physical faculties (stupid, lying, clumsy), a second group insinuates sexual or social deviance, while a third makes reference to unclean things or animals. With the exception of some rarer, older or milder words (in parentheses), the corpus proves the use of these -PG slurs in HS directed at the immigrant/refugee minority (e.g. 14f ). * ‘stupid’: (kæmpe)idiot, nar, tosse, tåbe, båtnakke, analfabet [+ baryl, dumrian, turboidiot]. * ‘clumsy’: spasser, tumpe, taber [+ e.g. fjollerik, fusentast, stymper]. * ‘lying’: hykler, løgner/løgnhals, lystløgner, skurk, platugle, plattenslager [+ slyngel, snydepels, snøbel, slubbert, kæltring, lømmel, sjuft]. * sexual deviation: bøsse(røv/karl), luder, røvpuler, pædo(fil) [+ flueknepper]. * social deviation: stodder, nasserøv [+ bums, usling, lurendrejer, snob, knøs, pralhans, sociopat]. * ‘unclean’: skiderik, røvhul, møgkælling, møgso, møgsvin, smatso, blegfis.12 (14f ) Han skal bare vappes ud. Og han kan tage sin smatso af en hijabikone med sig. (He should simply be kicked out. And he can take his bitch pig of a hijab wife with him)

9 Humour 9.1 Language-Based Humour: Word Plays Humour can fulfil multiple functions when entangled with HS (Yeon & Lee, 2021:25–26). On the one hand, it is used for amelioration and hedging against HS accusations (notwithstanding the subjective effect on the target). On the other hand, it can support a sense of moral 12

 Blegfis does occur in direct CHS, but is mostly quoted in meta-discussions.

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superiority, glorifying the in-group and expressing schadenfreude towards the out-group (Sakki & Martikainen, 2021). While these are important aspects, we will here, however, limit the discussion to the linguistic aspects of HS humour. One common humouristic technique encountered in the corpus13 involves word plays, such as puns and blends. In the latter, a target concept is lexically fused with another word, either to suggest a connection, and/or to achieve pejoration. Salafislam, for instance, equates Islam with (extremist) Salafism, while Eurabia and Sverigstan support the narrative that Europe/Sweden are being Arabised and turned into a Muslim “-stan” republics. The adjectival creation tyrkertroende, used for Muslims, means literally ‘Turk(ish)-believing’, but evokes the negative Danish idiom of tyrkertro, used for false but stubborn beliefs. Word blends and puns are also used to attack pro-refugee politicians (flygtningekansler—refugee chancellor [Merkel]) or left wing parties (Allahernativet − Allah + Alternativet, de radiGale − de Radikale + insane). The arguably most sensitive word plays involve Allah and the prophet. Pædrofeten (paedophile + prophet), for instance, exploits the paedophilia trope common in anti-­ Muslim HS. Puns (15a) and other word plays targeting Muslims are also used in established joke constructions: (15a) H  vad kalder man en muslim med glutenallergi—En müslim. (What do you call a muslim with gluten intolerance?—en müslim)

9.2 Irony and Sarcasm The lion’s share of humour found in the corpus, however, is unstructured, often involving irony (15b) or sarcasm (15e), and sometimes marked as such with laughing or sceptical smileys (15c-f ). The “unjoked” message is often a stereotype (15b: Islam means war, 15c: immigrants don’t pay taxes), but can also contain outright HS or threats (15g-h)  Obviously, automatic annotation of (new) puns is not possible, but they turn up when searching for unanalysable words/spelling errors. 13

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(15b) Koranen Fredens Religion » (The Koran, the religion of peace) (15c) skulle det fandme da være første gang, en indvandrer betaler skat (That would be the bloody first time an immigrant pays tax) One particularly well-known humorous trope is the promise of 72 virgins in Paradise for martyrs: (15d) S å hjælp dem dog med at blive martyrer, skyd dem. Eller har I da slet ikke nogen medmenneskelighed tilbage, og hvad siger de 72 jomfruer? (So help them to become a martyr, shoot them. Or don’t you have any empathy left, and what would the 72 virgins say? (15e) P  røv det på min bil og jeg skaffer 72 jomfruer til dig! (Try that on my car, and I’ll get you 72 virgins [=kill you]) Predictably, the virgin joke scheme is also used to address gender equality: (15f ) F  år kvinderne så også 72 jomfruer og bliver homoseksuelle efter check­in i paradis? (So do women also get 72 virgins, and can homosexuals stay in Paradise after check-in?)

10 Polarity-Inverted HS Constructions In certain conventionalised linguistic constructions, HS manifests in a polarity clash between surface expression and intended meaning, achieving a hateful effect in an indirect fashion. In such constructions, the true, negative meaning has a symbolic correspondence with a textual construction that at the surface may contain positive-sentiment elements. A well-­ known example is “I am not a racist, but...” (Geyer et al., 2022). Here, the speaker hedges an otherwise xenophobic utterance, trying to preempt possible CS. The pattern allows a certain amount of variation: ‘I + am + (ADV) + not + X’, where X is a negative NP or adjective:

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(16a) J eg er absolut ikke racist, men kan folk ikke opføre sig og følge vores love så er det bare ud og det lige så snart dommen er afsagt. (I am absolutely no racist, but if people can’t behave and follow our laws, it’s out [of the country] as soon as the verdict is pronounced) The Danish corpus contains at least 220 examples, though most are quotes at the meta level. Another conventionalised, meaning-inverting construction is ‘(these)+oh-so+ADJ+X’, where X is a human noun, mostly generalised in the plural, and ADJ is an adjective that in isolation would evoke the opposite sentiment. (16b) K  ære venstrefløj og åh så tolerante mennesker der demonstrerer sammen med salafister i burka (Dear left-wingers and oh-so tolerant people who demonstrate together with salafist in burkas) Interestingly, though prototypical usage pairs a positive adjective with a negative indirect meaning, the corpus also contains instances with the opposite polarity, especially in CS: (16c) ... de åh så skrækkelige udlændinge (… these oh-so horrible foreigners)

11 Word Embedding Word embedding is a machine-learning technique that can be used to approximate lexical semantics in a statistical fashion. We here use it to supplement traditional corpus inspection and collocation studies, delineating the contextual semantics of terms such as ‘immigrant’ and ‘refugee’, and in particular to quantify the negative sentiment associated with certain minorities, as well as the conceptual closeness to pejorative attributes they are attributed with. The word embeddings were produced using the word2vec neural network technique (Mikolov et al., 2013) and the TensorFlow suite (Abadi et  al., 2016). This method computes, for each input token, a multi-dimensional vector, where the dimensions are hundreds or thousands of other words that either occur (value = 1) in the

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same text unit or not (value = 0). In our experiments, we used sentences (or tweets) as input units, replacing words with their lemmas and retaining only content words tagged as common/proper noun, verb or adjective, to reduce noise. The distance between individual word vector distances can be interpreted as conceptual similarity and vector bundles can be interpreted as clusters of related terms (word fields). For instance, we can examine which ethnicities are most closely associated with the slur term perker or the HS stereotypes of rapist and paedophile.

11.1 Which Kind of Perker? Though perker is the Danish default slur for foreigners, it is difficult to define, does not apply equally to all foreigners and has no clear NPC. Word embedding allows us to place different HS-targets on a kind of “perker scale”. Figure 6.2, ordered for decreasing “perkerhood”, suggests that the prototypical perker is Middle-Eastern—Muslim and/or Arab (scores>50), with Africans and Turks holding a middle ground (>40). Eastern Europeans, Afghans and Germans, on the other hand, do not align well with perker. Among the general, not-ethnic nouns, indvandrer/immigrant score higher on the perker-scale than nydansker (‘new Dane’), flygtning

Fig. 6.2  Perker-scale for minority target groups

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Fig. 6.3  Vector similarity of negative emoticons/emojis: angry, sad, horror, sceptical (rows ordered for perker-scale)

(refugee) and migrant.14 This underlines the important conceptual and sentiment difference between (“premeditated”) immigrants and (innocent) refugees, also found in collocation statistics and qualitative analysis. Nydansker is a euphemistic word and as such used in utterances less likely to employ slurs. Because perker is a slur, the perker-score can be interpreted as sentiment ranking, but a more nuanced picture can be achieved through a comparison with emoticon word vectors. Figure 6.3 plots vector similarities for four negative emoticons/emojis, emo-angry (green), emo-sad (red), emo-­ horror (blue) and emo-sceptical (yellow). As a bundle, the four emoticon vectors only roughly mirror the perker scale, and only for the upper, “immigrant” part (dotted line), not for the lower, “refugee” part. Rather, they express emotional load in the  Migrant differs from immigrant in that the former are associated with Greek islands, the English Channel and boat tragedies in the Mediterranean, while the latter are people who have made it into Denmark itself. 14

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discourse, which is also high for Russians, Ukrainians and refugees on an axis independent of perkerhood. We can get an idea about the individual sentiments by contrasting the four emoticons to each other. Thus, ‘angry’ beats ‘sad’ for the immigrant section, with the highest relative difference found for Arabs and Africans. Conversely, the refugee and war discourse (Syrians, Russians/Ukrainians) evokes more sadness than anger. The scepticism-emoticon is complicated, because it is not only used for distancing oneself from the target group, but also for marking doubt and in connection with irony, questions and critical comments in general (17a-­ b). The second interpretation has to be considered where the sceptical emoji outranks the other negative emojis, as is the case for Germans, Poles and Romanians as well as the Russia–Ukraine war. The emoji is often used to express a negative sentiment indirectly, especially when attributing stereotypes, for example, ‘Poles are alcoholics’ (17c), ‘Romanians are thieves’ (17d), ‘Germans can’t cook’ (17e). (17a) Hvorfor mon russerne ønsker England ud af EU (Why do you think the Russians want England out of EU) (17b) Er det korrekt, at ukrainerne angreb sig selv, lagde deres egne byer øde og myrdede i hundredevis af civile landsmænd blot for at kunne skyde skylden på de stakkels fredselskende russere #russianWarCrimes (Is it correct that the Ukrainians attacked themselves, destroyed their own cities and murdered hundreds of civilians just to be able to blame the poor peace-loving Russians) (17c) promille på 100 overlever du næppe, danmarksrekorden indehaves af en polak han havde en promille på 8. (you will hardly survive a blood alcohol reading of 100, the Danish record is held by a Pole he had a reading of 8) (17d) Rumænere fængslet for serie af indbrud i Jylland og på Fyn (Romanians arrested for a series of burglaries in Jutland and Funen) (17e) Du vil IKKE have en tysker til at lave din mad (You do NOT want a German to cook for you) In our bilingual corpus, Danish negativeness towards HS-targets can also be put into perspective by comparing sentiment vectors with

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Fig. 6.4  Cross-language comparison of emoticon-negativity for key lexemes (Bick, 2020)

German data. Thus, in Bick (2020b), we showed that in Danish social media, religious “otherness” is viewed more severely than being an immigrant/refugee per se, while in the German discourse, immigrant, refugee and asylum-­seeker drew more emoglyph-negativity than the religion-terms Muslim and Islam (Fig. 6.4). The graphic also shows that it is very difficult to translate slur terms, as the negativity load of otherwise equivalent slurs differs considerably. Thus, the Danish perker appears to be more negative than the German equivalent Kanacke, while the German Musel is worse as a Muslim slur than the corresponding muhamedaner in Danish. In both languages, the term nazi15 is no longer group-specific and broadly used to denigrate perceived ideological opponents of any shade and is common in compounds such as AfD-nazi, but also in coinings like feminazi, zionazi and sprognazi (‘language nazi’), targeting feminists, Jews (!) or even grammar zealots.  The fact that term has a higher negativity score for Danish can be explained by the fact that German ‘Nazi’ is used both as a historical and a slur term, while Danish uses ‘nazist’ for the former, and ‘nazi’ for the latter. 15

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11.2 Criminal Foreigner or Radical Muslim? One way of exploring the mental, prototypical images Danes have of foreigners, refugees, immigrants, etc. through a corpus optics is to find the adjectives most typically collocated with these words. Thus, the top relative frequency ranks for prenominal adjective modifiers were: udlænding (foreigner) flygtning (refugee) indvandrer (immigrant) muslim (Muslim)

kriminel (criminal), højtuddannet (well-educated), arbejdsløs (unemployed), dårlig (bad-quality) ukrainsk (Ukrainian), syrisk (Syrian), såkaldt (so-called), nyankommen (newly-arrived), afghansk (Afghan) ikke-vestlig (non-Western), arbejdsløs (unemployed), økonomisk (economical), illegal (illegal) rettroende (orthodox), kær (dear), religiøs (religious), moderat (moderate), kriminel (criminal), ekstremistisk (radical), troende (believing), pædofil (pedophile)

In other words, for refugees the focus is on where they come from, and for immigrants on whether they are legal and what their motivations is (economic). Foreigners are associated with crime and judged for their quality (as working tax-payers). Muslims, finally, are viewed as governed by religion and judged on an axis of extremism. However, the collocation method only works if the attributes in question really do occur in adjacent modifier positions. Also, the relative frequency measure quantifies concept association only in terms of usage, and does not take into account similarity or semantic relatedness. Word vector similarities, on the other hand, measure relatedness/similarity indirectly, through what could be called shared collocate clouds. Vector similarity can be computed even if not a single sentence contains both of the words to be compared. Figure  6.5 grades minorities for the (perceived) attributes ‘criminal’ and ‘radical’ in a two-dimensional plot. This is a bit like handing a questionnaire to the myriad anonymous tweeters behind the corpus asking them: “On a scale from 0 to 100, how criminal/ radical are Muslims (immigrants, Arabs, etc.)? The scatter diagram proves the two features to be fairly independent of each other (red oval markings). The strongest link is between kriminel (criminal) and the ethnicity- and religion-neutral immigrant terms,

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Fig. 6.5 Minority terms graded for ±criminal (kriminel) and ±radical (ekstremistisk)

higher for (a) udlænding (foreigner) and indvandrer (immigrant) than for (b) flygtning (refugee) and asylansøger (asylum-seeker). Muslims (muslim, muhamedaner) are perceived as more radical than either (a) or (b), but not as criminal per se. Among the ethnicity terms, Middle-Eastern and (North) African people score higher on the extremity axis than Europeans,

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but far lower than Muslim. This indicates that it is Islam as such that is viewed as radical, an assumption that is also supported by the fact that Islam correlates strongly with the term ekstremist itself (60.0), as well as with related radical concepts, such as sharia (79.8), jihad (66.5), underkastelse (submission, 64.6) and kvindeundertrykkelse (oppression of women, 63.3).

12 Conclusion Our HS review of the Danish XPEROHS corpus not only documents that HS against immigrants and refugees is pervasive in Danish social media, but also that it is found at all linguistic levels and exploits a large variety of linguistic strategies to express such hate and the stereotypes and narratives supporting it. In particular, creative word formation and slur variation (compounds, puns and derivation) are used to harness derogatory and dehumanising concepts and metaphors, and to link them to the target group.16 In addition, we find syntactic methods of generalisation and othering used with hateful intent. Our qualitative review was facilitated by pattern searches based on linguistic annotation, and it is interlinked with quantitative analysis of patterns and relations. Using collocation strength and word embedding vectors, we have quantified negative sentiment for a variety of minority group words and slurs, identified typical (negative) attributes, such as ‘criminal’ and ‘extremist’ and assessed their relevance (association strength) for the individual target groups.17 Finally, in Sect. 4, we examined the interplay between primary, anti-minority HS, counter speech and secondary, defensive HS, identifying relevant key concepts and narratives linked to the latter.  Which derogatory tools and concepts are used, and how frequent they are, may depend on whether the target is the entire minority group (general HS) or a specific individual on the grounds of group membership (directed HS). This distinction is a relevant one (ElSherief et al., 2018) and should be addressed in future research. 17  Since the Twitter part of our data is “unabridged”, it would be possible to do similar studies for different HS target groups (e.g. gender-based HS) in the same corpus, and compare the results, identifying universal linguistic traits of HS as well as target-groups specific metaphors and narratives. 16

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Part III Lexical and Rhetorical Strategies in the Expression of Hate Speech

7 Humorous Use of Figurative Language in Religious Hate Speech Liisi Laineste and Władysław Chłopicki

1 Introduction It has been established by now that there are numerous ways of expressing positive and negative emotions online (Derks et al., 2008; Waterloo et al., 2018), even though the early scholars of internet research argued that the internet environment significantly reduces social cues (Sproull & Kiesler, 1986), which makes expressing emotions risky and thus inhibited. Some of these communicative tools, for example, the use of emoticons, are straightforward and require little effort to identify and understand, whereas others—like metaphors—are more ambiguous and indirect, but still important to locate and analyse, especially in the context of online hate speech. It is possible to tell through the analysis of figurative language how people feel about the subject of their communication. We L. Laineste (*) Estonian Literary Museum/Tartu University, Tartu, Estonia e-mail: [email protected] W. Chłopicki Institute of English Studies, Jagiellonian University, Krakow, Poland © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_7

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align with Ahmed (2004), Marander-Eklund (2009) and Sandell (2018), who posit that a productive way to study the expression of emotions is to focus on the figurative language (e.g. Ahmed, 2004: 11–13). So far, metaphors have been largely discussed from the perspective of their persuasive and evaluative nature (see, e.g., Semino & Demjén, 2017) mainly in reference to political discourse (e.g. Ferrari, 2018): their creators may deliberately guide our thinking and manipulate our opinions through the evoked imagery (see also Prażmo, 2020). There are, however, almost no studies about metaphor use in the context of hate speech, even though anti-social discourse often uses metaphors (with just a few exceptions in recent literature, see, e.g., Demjén & Hardaker, 2017; Musolff, 2015; Palacios, 2009). Also, anger has been studied as (a target domain of ) metaphor itself, while hate was not normally included in the discussion (cf. Kövecses, 2018). The examples are taken from the media. Our study, based on the NETLANG corpus, takes its data from social media comments on YouTube. The NETLANG project, of which the present study is an outcome, aims to achieve an interdisciplinary understanding of the many-faceted linguistic forms and the pragmatic strategies of hate speech in computer-­ mediated communication (CMC). Thus, it needs a theoretical model that includes the less obvious and less transparent expressions of hate. We aim to attend to this by applying deliberate metaphor theory (DMT, see Steen, 2008, 2017; see Sect. 1.2) to the analysis of hate speech on YouTube, focusing on metaphors used in comments about religion. Our analysis is further informed by studies that examine the joint appearance of metaphor and humour (Laineste & Chłopicki, 2019). In this chapter we focus on the deliberate and conventional metaphors as well as humour in hate speech to elucidate how this express thinking is actualised in CMC and what effect it has on the inappropriateness of the message. This is a novel approach in that the metaphor/hate speech/humour connection has hardly been studied so far. Our results suggest that metaphor’s deliberateness and its connections with humour affect the hatefulness of the online comments, covering and moderating, but not completely erasing it.

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1.1 Internet as a Place to Express (Negative) Emotions Through Metaphors Social media are increasingly and globally taking up important areas of our daily lives (cf. for geographical and demographic divides in internet usage, see Jensen, 2015; Boyd, 2015). Nowadays it is clear that the Internet contains hubs of what has been called uncivil, inappropriate or disinhibited communication (e.g. Joinson, 2003, 2007), ‘toxic online disinhibition’ (Suler, 2004; Lapidot-Lefler & Barak, 2012) or hate speech (Delgado & Stefancic, 1995). People express, share and are influenced by the emotions of others (see Brown et al., 2003; Lee, 2011; in the context of racial fear, see Ahmed, 2004: 10). Research has shown that some topics are especially prone to trigger online arguments (e.g. Laineste, 2013), among them religion, sexual orientation and immigration. When expressing prejudice against religious groups (but also immigrants, refugees, etc.), dehumanising metaphors are often used. The source domain can be animals, dirt and infestations, food, objects, (mental) illness, natural disasters (Demjén & Hardaker, 2017). An effective way of creating a negative image is referring to the targeted group as cannibals (Laineste & Lääne, 2015). A study on figures of speech describing the inhumanness of the Other has pointed at a consistent and creative use of phrases like “dancing on the graves”, “spitting in the face/heart” (Laineste, 2020). Such metaphors are used with “a high degree of “deliberateness” and a modicum of discourse-historical awareness” (Musolff, 2015). It has been suggested that online interaction in general, representing “written speech” or “spoken writing” (Crystal, 2002), contains a high proportion of spontaneously produced metaphorical expressions. Pihlaja (2018), while studying online religious talk, has found the frequency of metaphor in CMC to be around 17%, comparable to metaphor density in written discourse (e.g. fiction, academic discourse or print news 11.7%–18.5%; see Dorst, 2015) and considerably higher than in conversation (7.7%; classroom and reconciliation talk 2.7%–10%, see Cameron, 2008). Thus, online metaphors, unlike metaphors in written texts (Lewandowski, 2012), tend to be of an ephemeral and idiosyncratic

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nature and thus provide excellent instances of deliberate, unconventional metaphors. Metaphors are ambiguous and playful in nature. Pihlaja (2012, 2014, 2018 and elsewhere) has studied conflicts in online religious discourse, especially on YouTube comments, underlining the “drama” aspect that attracts the audience and triggers the arguments (Pihlaja, 2014: 3). The role of metaphors in that process is to build a sense of camaraderie and shared belief, but also find pleasure in formulating novel and sometimes humorous ways of insulting those who think differently. While studying YouTube comments discussing religious topics, he concludes that Islamic and Christian commenters share a common ground (e.g. in being well-­ informed about religious issues) despite whatever differences they might have, while atheists stand apart: they express themselves differently and lack a coherent discourse. Very often, atheists resort simply to mocking and insulting the believers. The Islamic and Christian YouTubers are similar in their rhetoric of spreading their faith, which has been a part of their own path to religion (see Pihlaja, 2012: 63, 2018: 6, 12). In an online—and by nature asynchronous—argument, the aim is to leave the other speechless, not persuaded (Billig, 1996). As Pihlaja (2012: 66) suggests, “the goal then becomes not the persuasion of the other, but ‘winning’ an argument by holding the floor last”. This is further amplified by the fact that the online environment is described as favouring play, humour and creativity (North, 2007; Willett et  al., 2009). Figurative language offers many possibilities for such emotionally engaged play, first and foremost linguistic play, but also that on the level of communication and social relations. This is the case in both the conventional metaphors, which are used without much cognitive or emotional awareness, but also those of a more creative and deliberate nature (see Sect. 1.2 for definitions).

1.2 Definitions Within the context of this study, it is necessary to define some central concepts. Hate speech is a conscious and wilful public statement intended to denigrate a group of people (Delgado & Stefancic, 1995). More

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specifically, (1) the sender of the hate message assumes the identity of a group, (2) expresses prejudice (i.e. negative evaluations of a group, see Brandt & Crawford, 2019) against another group, and (3) targets the group or its representative publicly (esp. in social media, newspapers or YouTube comments) with the (4) purpose of attacking them (adapted from Ermida, Chap. 2, this volume). While many researchers see hate speech as a form of offensive language, other approaches treat it independently, side by side with profanity, offence, abuse, insult, etc. (e.g. Watanabe et al., 2018; Nobata et al., 2016). Sometimes, all of these terms are seen as a type of the more general umbrella term of abusive language (Niemann et al., 2020) or, alternatively, socially unacceptable discourse (Fišer et al., 2017). One of the outcomes of the NETLANG project is a five-factor annotation model for hate speech identification by Ermida (see Chap. 2) that allows distinguishing between hate speech and aggressive speech. We, on the other hand, are suggesting a scale or a continuum of degrees of hate within the socially inappropriate discourse (see also Álvarez-Benjumea, 2022) from mildly inappropriate speech to hate speech (instead of a discrete categorisation), conditioned by the presence of metaphor and humour. The deliberate metaphor, as defined by Gerald Steen, is identified as a metaphor where a conscious effort is taken (or “express thinking” is involved, see Steen, 2011a: 53) in creating and understanding it (on the levels of mind, language and communication): it draws attention to its “source domain as a separate detail for attention in working memory” (Steen, 2017: 7), which distinguishes it from non-deliberate conventional metaphor that works through established routes outside the attention span (cf. Steen, 2011b). The conventionality of metaphor is directly related to what is known as conceptual metaphor in the cognitive linguistic frameworks of Langacker (2008) and Lakoff (1987) (a mental mapping between domains which is reflected in commonly used linguistic expressions). Even though this distinction has caused controversy (e.g. Gibbs (2017) argued that deliberate metaphor does not have all the rhetorical power that Steen attributed it with; cf. Zhang & Lin, 2020), we would like to uphold it here as it does have a sufficient degree of explanatory power.

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1.3 Figurative Language and Humour Humour is essentially a playful and entertainment-focused phenomenon, which seemingly contradicts the unambiguously harmful purposes of hate speech (see Davies, 1990; for a theory that links humour with aggression, see Gruner, 1997). Nonetheless, there are some uses of humour, such as teasing, testing understanding (Norrick, 1993), but also gossip or mock aggression that contribute to the sense of group inclusion/exclusion (Alvarado Ortega, 2015). In the case of metaphor, the potentially positive and negative usages become similarly intertwined. Metaphors and figurative language, but equally so irony and humour can be used as ways of manoeuvring around hate speech, especially in countries that have strict legal liabilities against it (Aref et al., 2020). The relation between humour and metaphor has been researched by both humour and metaphor scholars, who have found a number of overlaps between the two phenomena (see Chłopicki & Laineste, 2019 for a fuller overview). Briefly, humorous triggers (Attardo, 2001) have been found to be comparable to metaphor-related words (cf. Reijnierse et al., 2018), as both bring together two meaning domains, either to underscore ambiguity (in the case of humour), or to bring about synthesis and eventually disambiguate (in the case of metaphor). Humour has been found to cause oscillation between various levels of opposition in the audience’s minds and the resulting chains of associations, which multiply its effects, while metaphors tend to draw the attention of the audience towards other, adjoining domains instead, with the original domains fading away. Some other converging qualities include humour’s use of exaggeration, wordplay and surprise, known in metaphor research as - respectively - hyperbole, drawing attention to the source domain, and novelty. Most importantly, humour as well as deliberate metaphor involve a more extensive activation of potential referents and meanings (cf Steen, 2011a) than plain utterances, especially in domains that at first instance may seem incongruous or unexpected, forcing the addressee to make new connections in the attempt to understand the utterance. However, it is the contextual use of metaphor that is the factor that determines its humorous potential (cf. Ritchie & Schell, 2009); many metaphorical

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mappings occur side by side with humorous oppositions as only secondary effects (Müller, 2007: 52; Hamrock, 2007: 149), but interpreting spontaneous (deliberate), somewhat tongue-in-cheek metaphors is not always an easy task (see, e.g., Example 2 below). Studying metaphor in (online) hate speech thus presents a particularly complex case of hate speech rhetoric, which needs to be studied in more detail not just because of the frequent appearance of metaphors on social media but because we need to know more about such liminal/marginal cases where the speaker may hide behind the justification that “it was just a joke” (see Lockyer & Pickering, 2005). Thus, we hypothesise that metaphors and humour carry an important role in moderating the hatefulness of comments through creating and expanding on humorous images, exaggeration, irony and other humour techniques.

2 Methodology Similar studies of online hate speech have tried to grapple with the phenomenon from various angles, for example, the discourse analytic approach (Assimakopoulos et al., 2017). Our approach is unique because we combine our expertise in humour and deliberate metaphor analysis with a new hate speech model (Ermida, this volume) and perform a corpus analysis, developing a scale of socially inappropriate discourse that correlates the use of humour and deliberate and conventional metaphors with the degree of hatefulness of online comments to show that both humour and deliberate metaphors cover up and ameliorate the offensiveness in comments.

2.1 Case Selection Principles The NETLANG1 corpus is designed to enable the analysis of language mechanisms of hate speech and their sociolinguistic variety. In this study,  “The Language of Cyberbullying: Forms and Mechanisms of Online Prejudice and Discrimination in Annotated Comparable Corpora of Portuguese and English”, based at the University of Minho, Portugal, and sponsored by FCT, as of 1 Oct 2018 (PTDC/LLT-LIN/29304/2017). 1

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we decided to focus on one online platform, namely YouTube. It is one of the most visited internet sites today (the 3rd in the world as of February 2022; see Alexa, 2022) with active but scarcely moderated comment sections. This is a highly suitable channel for detecting religious arguments and analysing the linguistic elements thereof. As random sampling of online data would result in scarcity of examples of specifically religious content, we decided to use the common alternative to collecting data: searching by predefined keywords related to the topic we are interested in. We performed a search of YouTube threads within the NETLANG corpus with the triple combination of keywords ‘hate’/‘anti-’, ‘against’, ‘argument’/‘Christian’, ‘Islam’, ‘atheist’ to account for the three main religion-related groups. From the resulting threads, we chose the ones that (1) contained at least 1000 comments—because the more comments, the more probable it is that the commenters get more engaged in the conversation, sometimes in the form of an argument (see also Pihlaja, 2014, 2018), and (2) dated from the years 2011–2021, in order to capture the more recent controversies. After scanning the data for a co-­ occurrence of metaphor and hate speech, a further selection was made of threads that (1) included offensive talk against religion (Christianity, Islam) and (2) used conventional and/or deliberate metaphors to do so, bringing the number of comment threads to nine and the number of comments within these threads to 122,609. For a more detailed analysis, a hundred random comments were selected. In order to annotate the data for further analysis of co-occurrence and contextual elements, we tagged2 the comments using four main categories: type of discourse (inappropriate, appropriate), type of metaphor (deliberate, conventional), humour mechanisms (exaggeration, irony, humorous images, etc.), and textual/ linguistic features (capitalisation, direct address, etc.). In case of  In fact, the process leading to the final tagging started by comparing the functionalities of three tagging engines: CATMA, UAM Corpus Tool and INCEpTION. CATMA (https://catma.de/) is an online Computer Assisted Text Markup and Analysis program that allows for easy and intuitive online tagging options, but is not suited for such a complex tagset as our study required. UAM CT (http://www.corpustool.com/) is a concordance program that provides multiple options for statistical analysis, but is complicated to use. INCEpTION (https://inception-project.github.io/) is a flexible and intuitive online annotation tool. It has a lot of (semi-)automated annotation features (which we were not able to use extensively because of the nature of online speech), but it proved to be the most suitable annotation tool for our data. 2

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discrepancies in annotation between the coders, we discussed the comments case by case and omitted those where no consensus was reached (less than 2%).

2.2 Deliberate and Conventional Metaphor Identification Procedure In order to operationalise the concept of deliberate metaphor, as seen from the threefold perspective of metaphor in thought, language and communication, and design reliable methodology to study it, we followed a deliberate metaphor identification procedure of five steps (DMIP; Reijnierse et  al., 2018). This crucially included identifying metaphor-­ related words (MRW): lexical units which refer to both the source and target domain (i.e. are ambiguous). For the sake of simplification, we have focused our report on the final step of abstract cross-domain mappings, specifying the contextual value of deliberate metaphor (the interim steps include identifying propositions, metaphorical incongruities, and parallel metaphorical structures, see Steen, 2017). This contextual value is situated at the highest level of communication within Steen’s threefold distinction and corresponds well with reconstruction of the speaker’s intentions or identifying functions (e.g. clarifying or rhetorical) as proposed by Perrez and Reuchamps (2014).

2.3 Context and Content of the Selected Threads In the analysis below, we present seven single comments that represent the steps on the scale from hate speech to mildly inappropriate speech.3 They originate from four (of the nine) YouTube threads which were all actively in use as of February 2022, although the videos have been published between the years 2011 and 2021: 1. YouTube thread “Richard Dawkins destroys arguments of crazy Muslim fanatics” features a compilation of clips from a TV debate on  We have disregarded comments that we consider completely appropriate.

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the BBC that took place in 2008. The compilation was published on YouTube by Sh3llShock (but is made available in multiple other sources and other users across the Internet) in 2014 and had (by February 2022, here and onwards) 5,789,200 views. The comment section is still active: 21,016 comments have been written and new ones are added every day. Throughout the debate, Richard Dawkins asked Mohammad Mukadam, the chairman of the Association of Muslim Schools, what the penalty for apostasy in Islam was. After dodging the question several times, Mukadam replied that it was death. The discussion in the comment section revolves mostly around this statement. 2. The thread titled “The worst of Christian TikTok” was posted to a channel ImAllexx, managed by Alex Elmslie. He is a British gaming commenter who generally posts comedy, prank, challenge, and reaction videos. The thread features commentary on a Christian TikTokker’s video made by a US TikTok star and influencer David Latting, and was posted on January 15, 2021. So far it has received 417,830 views, 35,000 likes and 4449 comments. Other compilations of David Latting’s TikToks have been published on YouTube, while this one offers funny commentary on the emotional, expressive and sometimes rather irrational social media personality. 3. The video “Does believing in God make you dumb?” was posted on August 19, 2013 and has over the years received 1,745,642 views, 24,000 likes (and 10,000 dislikes) and 43,259 comments. It is published by a science channel by the name of Seeker, an American digital media network and content publisher affiliated with DNews, whose aim is to reach millennial audiences interested in science, technology and culture. The host Trace Domingues is an established science communicator and the founder of Seeker in 2012. In this video, he reviews studies about the correlation of intelligence level and religious beliefs or identity. The thread is still in active use, while many commenters simply reply to the questions posed in the title of the thread. 4. The video entitled “We’d be better off without religion: Christopher Hitchens” was posted on YouTube on December 16, 2011, on the channel of Intelligence Squared. The channel is run by  the media company Intelligence2 or IQ2, founded in 2002, that organises live

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debates and other cultural events around the world. The video has 918,740 views, 5412 comments (and the number is still growing) and 11,000 likes. The debate dates from 2007 and features the late Christopher Hitchens, who argues that religion has been at the root of many conflicts.

3 Analysis As argued above, we hypothesise that the use of metaphors and humour may modify the perceived hatefulness of a comment. Based on the five elements Ermida (Chap. 2, this volume) outlined as central in distinguishing hate speech from aggressive speech, we coded the data for the commenter’s position, expression of prejudice, the existence of a target, and the purpose (e.g. attack), but not the channel, as this was public (YouTube) in every case. We found that the speaker in our data is usually an individual who does not position him/herself openly as a representative of a group and their purposes may be manifold. At the same time, the target (religious groups or people) is mostly present, as is the expression of prejudice. The commenter’s purpose could be not only sheer attack or accusation, but also deprecation or ridicule of the target. There were examples where commenters expressed dislike, or came up with an observation about, advice for or even an appreciation of the target. The decision as to how socially inappropriate the comment is tends to be a matter of language as well as metaphoricity or humour evoked—which is the core of our inquiry. The comments free of prejudice, particularly those classified as observations (more objective or distanced by nature), tend to be less inappropriate or even completely appropriate (see Ermida, Chap. 2 in this volume, where such examples test negative for the “content” factor in her model). In the selected subset of data, there are 47 comments that include a  conventional metaphor and 32 examples displaying  deliberate metaphor, while the remaining 21 comments comprise unidentified or mixed instances of metaphors. Deliberate metaphors often tend to soften the force of hate speech: there are only four examples where deliberate

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metaphors coincide with hate speech in our sample (out of over 30 examples of deliberate metaphor, the great majority of which are humorous), and three of these four are humorous. However, an utterance may come across as more hateful than expected when one of the domains it involves is a taboo domain, such as sex, paedophilia or excrement, as well as highly negatively loaded domains, such as organised crime, disease, mental illness, superstition, objectification of people, or stupidity. Some specific textual/linguistic elements used in the comments had some bearings on their hatefulness (such as the use of swear words), while direct addresses, rhetorical questions, quotations and metacommentary, abbreviations or repetitions did not carry this effect. The primary humour mechanism was exaggeration (16 examples), followed by humorous images (13), irony (10) and puns (6). There were 21 examples of non-humorous metaphors. Interestingly, and also crucially for our purpose, there is no single example of humour in our data that would be classified as hate speech—thus humour expectedly (at least in the online environment, as argued above) softens the force of hate speech or neutralises it altogether. Specifically, puns, as well humorous images (i.e. verbal expressions that evoke fictitious humorous situations, usually involving intense physical actions of characters; see also joint fantasising, Chovanec, 2012) and most cases of exaggeration did not coincide with hate speech and were even often classified as socially appropriate, while irony did correlate with hate speech. Below we present the most representative examples from our data that mark points on the continuum which combines two scales—from hate speech to mildly inappropriate comments, from conventional to deliberate metaphors, and from no humour to humour.

3.1 Hate Speech + Conventional Metaphor + No Humour (1) N  obody has a “right” to force primitive, immoral and ignorant beliefs on an impressionable child. This is barbaric child abuse and encourages blind authority with the threat of violence otherwise. (“Richard Dawkins destroys arguments of crazy Muslim fanatics”)

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The comment, posted on February 14, 2021, received neither likes nor replies. The immediate context of this comment is only very loosely tied to its content: Earlier on the previous day, a commenter has noted “Religious people are indoctrinated sheeps”, and a few hours later another one says “If you need a faith to be a good person then we all already know what kind of person you are”, to which the comment in question follows more than 12 hours later. Only two days later someone quips: “Everyone’s gangster until Richard Dawkins starts pointing out his finger 0:53”. This means that the comment wasn’t inspired by other commenters but rather the video itself; nor did it spark further discussion. The comment qualifies as hate speech since its author expresses a clear prejudice against the Islamic faith and attacks it directly using a cluster of strongly negative qualities: primitive, immoral and ignorant, barbaric child abuse, blind authority and threat of violence. The commenter uses a highly conventional metaphor ARGUMENT IS WAR, well established in English (cf. Lakoff & Johnson, 1980), which finds its surface reflection in the expression “force beliefs on a child” as well as “forcing beliefs is abuse”. Another conventional metaphor, CLOSED (MIND) IS BLIND (“blind authority”), reinforces the persuasive nature of the comment, by contrasting mind and body domains. The presence of established, conventional metaphors does not soften the import of the comment’s hateful nature. The comment does include exaggeration, but in spite of that it displays no humour, thus remaining very strongly negatively charged.

3.2 Hate Speech + Deliberate Metaphor + Humour (2) I m not islamophobic, im not scared of islam, but I’m very islamonauseous (“Richard Dawkins destroys arguments of crazy Muslim fanatics”) The comment was published on September 16, 2017. It has received 235 likes over the years, which makes it appear among the “top comments” when sorted by this option. It spawned 11 replies, although only one of them (“dont worry its a reasonable fear”) is a more immediate one (from November 13, 2017), while others date mostly from 2021. The replies are not discussing the pun or its content but instead focus on the

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“fear” aspect. The preceding comment reads “He is basically saying that he cannot bring up his child without a book” and thus bears no relation to the content of the comment under analysis. In contrast to the previous comment, this one, even though it is hateful (clear prejudice against Islam, and attack against it), uses a deliberate metaphor combined in one with humour—islamonauseous is a creative compound neologism, evoking contrasted body and religion domains, and specifically triggering the rather deliberate ISLAM IS BAD FOOD metaphor. The creativity of the blend and its humorous nature thus softens the otherwise strongly hateful element of the comment.

3.3 Very Inappropriate + Conventional + Humour (3) S oo freaking funny listening to religious people. Like you got lost and ended up in a nuthouse where everybody is waiting for their meds just before the other wore off: D:D:D (“The worst of Christian TikTok”) The comment was published on September 2, 2021, got no immediate response and was liked only once. The previous comment “Come to repentance, hell is not a joke!” dates from three days earlier. The next one comes two days later and reads “So you’re a coward who is extremely afraid of hell, that’s actually kinda humorous that you’d make this” (and got nine replies). Hence, the comment didn’t spark discussion, even though it offers commentary on the comment section rather than the video, addressing the other commenters and labelling the Christian portion of them as “lost” and “in a nuthouse”. The comment is classified as very socially inappropriate, though not really hate speech. Its conventional metaphorical references point to RELIGION IS MADNESS and RELIGIOUS PEOPLE AS MAD. It is not an example of hate speech because no attack is involved. The vivid humorous image represents an extension of the metaphor (conventional as it is) with the exaggerated image of the commenter physically getting lost in a lunatic asylum (nuthouse) where the patients are waiting for their medicines to calm them down because their effects are wearing off. The colloquial expressions contribute to the prevailing humorousness of the comment, too.

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3.4 Very Inappropriate + Deliberate + Humour (4) R  eligion is like a Penis—It’s okay to have one it’s okay to be proud of it HOWEVER do not pull it out in public do not push it on children do not write laws with it do not think with it and do not feed the poor and homeless with it (“Does believing in God make you dumb?”) The current comment was posted on November 21, 2020. It received 2 likes and it has one reply (“Yeah, fair enough”) from two months later. The comment that precedes it does not relate content-wise to the comment under analysis—“According to the study done by me, …”. The comment is a citation with a reference to the source (reddit; see, for example, this post: https://www.reddit.com/r/exmormon/comments/3qi0vi/ religion_is_like_a_penis_best_metaphor_ever/). It has made its way to t-shirts, billboards, and memes, as is the case of the one featuring Maggie Smith of Downtown Abbey (see https://i.stack.imgur.com/7jV0D.png). It has also been attributed to the Pakistani poet Faiz Ahmad Faiz. Both origins are not backed up by evidence and the quote has developed in various ways in the hands of anonymous Internet users. What makes the comment deliberate is a conscious attempt to creatively adjust an existing quip to enhance the aggressive reaction to the debate on religion in the video. The humorous nature stems from the deliberate metaphor RELIGION IS A PENIS, and the reference to the domain of SEX, which clashes humorously with that of RELIGION. Due to this reference, the comment is inappropriate, and yet it does not constitute hate speech (even though it displays prejudice) also because it uses a citation as an observation made in order to ridicule (not attack), and the commenter turns it around in a creative way.

3.5 Inappropriate + Conventional + Humour (5) I ’m confused, is he high or at gunpoint from Jesus (“The worst of Christian TikTok”) The comment was published on January 20, 2021, and received one like but no replies. The preceding comment (“not me doing my science

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test I’m probably gonna fail”) does not relate to the analysed comment; the ensuing comment, on the other hand, underlines this observation: “This man cries in every video like jesus is on the other side of the camera holding him at gunpoint ”. The comment does not represent hate speech, being just an observation intended to ridicule the man in the video. Its hateful nature results from the conventionality of the metaphor, which embarks on the RELIGION IS DRUGS and RELIGION IS VIOLENCE domains. The comment evokes clearly exaggerated, humorous images, suggesting the person in the video is forced to act the way he does by Jesus in person holding the gun to his head and/or by being on drugs, and thus makes humour the prevailing force here.

3.6 Inappropriate + Deliberate + Humour (6) O  f course magic is real, have you not read the gospels of middle earth? Gandalf is a far better skywizard than Jebus. Gandalf had fireworks and weed. Jebus had fish and wine... I’ll go for Gandalf thanks! (“The worst of Christian TikTok”) The comment appeared on the YouTube thread on January 31, 2021, and is one of the many replies to Alex’s own (“top”) comment “is magic real?” posted in January 15 of the same year. This is why it starts with a direct reply to the prompt, and then develops the reply in an ironic and pop-culture-conscious manner. The immediate context to the comment also focuses on Alex’s question and not on the particular comment (“it’s not “magic” mate” before the comment, and simply “Yes” after it). The comment got neither likes nor replies. This is a relatively sophisticated comment, the commenter’s creative observations referring to Lord of the Rings (Gandalf ) and The Simpsons (Jebus); they turn around the domain of MAGIC/FANTASY clashing it with RELIGION, which makes it inappropriate, although the deliberateness of the extended metaphor (gospels of the middle earth, Jebus as skywizard) considerably softens the hateful import of the comment. In the second part, humour prevails with Jebus vs Jesus, fireworks vs fish and weed vs wine oppositions, which involve a formal alliteration, too.

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3.7 Mildly Inappropriate + Deliberate + Humour (7) Th  reatning me with hell, is like a hippy threatning to punch me in my aura. (...) (“We’d be better off without religion”) Our last example is a popular comment on the thread published on March 3, 2012. It received 712 likes and 193 replies. It is part of a longer discussion between two YouTube users, and the preceding comments touch upon the concept of hell and punishment in Christianity. The commenters have an extended, non-humorous discussion, justifying their religious beliefs. This comment starts with its humorous simile with a highly conventional metaphor HELL IS AN OBJECT as the seemingly aggressive point of departure. This immediately gives way to a deliberate metaphor involving RELIGION IS COUNTERCULTURE and RELIGION IS DRUG domains, on which the humour builds with its images of an “aggressive” hippie punching the commenter in the aura (this evokes the humorous oppositions of aggressive vs non-aggressive, possible vs impossible, and true vs false, see Raskin, 1985). We consider it mildly socially inappropriate even though it ridicules a particular religious belief (hell for the sinners), but it is not hateful (particularly, there is no prejudice nor attack), which is evident in the sentences that follow. Based on this data, we suggest an adapted model for defining hate speech that takes into account metaphors and humour as rhetorical strategies of online commenters, underlining the complex, context-bound and scalar nature of inappropriate discourse. The answer to the question if an online comment is socially appropriate or inappropriate, friendly or unfriendly, polite or impolite, is not a simple yes-no answer, even if it would be a preferred one.

4 Conclusion The anonymity and asynchronicity provided by the Internet, including social media, sets up a perfect environment for expressions of hatred and thus are worth studying in detail by linguists, folklorists and

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communication scholars. Hate speech expresses prejudice that is communicated publicly by an individual representing a group, targeting another group, regardless of its status. However, hate speech frequently appears in combination with rhetorical strategies which moderate the message and enhance its persuasiveness. Humour is one of these strategies used extensively in social media and has been studied as such. It makes use of several mechanisms such as exaggeration, humorous images and joint fantasising, irony, intertextual references, etc. Metaphors also abound in online interaction, but they are rarely the focus of study in the context of hate speech. Metaphors are a useful strategy because they can both express and provoke a particular emotion or attitude in online audiences. Conventional metaphors—those that rely on established conceptual mappings—are the most readily usable because they do not require any creative work of the speaker. Deliberate metaphors, on the other hand, are creative since the engagement of the speaker in adjusting the (conventional or other) metaphor to the context in question needs some conceptual effort. While studying hate speech targeting religious groups (Christian and Islamic) on YouTube, we have noticed interesting tendencies both in the use of metaphors and in the use of humour. Expectedly, there is a tendency for conventional metaphors to prevail. Most of the examples of comments which lacked humour coincided with conventional metaphors and tended to be socially inappropriate. There was also another factor which influenced the aggressiveness of the metaphorical comments and these were the references to taboo domains (sex, paedophilia, excrement), as well as highly negatively loaded domains (organised crime, disease, mental illness, etc.) and the use of swear words. When the metaphor in question is a deliberate one, and especially when humour is involved in the utterance (as exaggeration, humorous image or other), the hatefulness of the comment is reduced. Taking this into account, we suggest a scale of hatefulness in online comments from the less hateful complex, deliberate metaphors to the more hateful conventional ones, often further moderated by the presence of humour.

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8 Rhetorical Questions as Conveyors of Hate Speech Vahid Parvaresh and Gemma Harvey

1 Introduction It is undeniable that many countries across the globe are struggling to tackle the rise of online hate speech (Williams, 2021). A variety of reasons can be attributed to this rise, but a few stand to attention. To begin with, online platforms, such as social media, enable people to conceal and disguise their identity, making it more challenging for law-enforcement agencies to identify and prosecute them (Wright, 2020). In other words, as Benesch (2014: 19) observes, on the Internet “some feel free to express their hatred and anger there, even when they would not do so in similarly public settings offline.” In this respect, it has been argued that online ways of communication have “increased opportunities for anonymous hate speech” (Woods & Ruscher, 2021: 266).

V. Parvaresh (*) • G. Harvey School of Humanities & Social Sciences, Anglia Ruskin University, Cambridge, UK e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_8

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Furthermore, the ‘openness’ and ‘ease of use’ of social media platforms means that they are ‘alarmingly effective’ tools for projecting hateful values and organising extreme, hateful activities with the potential to harm both individuals and communities at large (Motlogelwa et al., 2021: 1, Paschalides et al., 2020: 2). In the current context of global migration, which has created ‘minority communities’, the anonymity and accessibility of social media have exacerbated haters’ desire to stigmatise “the most vulnerable groups within society, such as children, minorities, and immigrants” (Paschalides et al., 2020: 2). Despite the pervasiveness of hate speech, it remains particularly challenging to study and tackle as it is not always communicated explicitly; rather ‘indirect practices’ and ‘creative rhetorical strategies’ make hate speech a notoriously resilient phenomenon (Millar et al., 2017: 50). In other words, hate speech has become increasingly implicit to avoid intervention from moderation, censorship, and legal accusations of inciting hate, making it increasingly challenging to identify consistently and accurately (Wodak, 2015). The implicit form of much hate speech may explain that while “[l]ibraries have been written on the problem of hate speech”, there is currently a “paucity of scholarly analysis” of actual utterances that manifest some type of “social identity based hostility” towards other groups (Culpeper et al., 2017: 2). Consequently, it becomes increasingly important to explore forms of language with the capacity to convey implicit hateful messages (cf. Tayebi, 2018). In this chapter, we discuss how rhetorical questions (henceforth RQs) are used to impart a diverse range of hateful messages in Instagram comments targeted at Afghan people. The reason behind choosing rhetorical questions is that RQs “are examples of utterances whose form does not match their function” (Rohde, 2006: 134). Indeed, “[a]nswers to rhetorical questions are supposed to be obvious to both speaker and hearer and hence do not need to be expressed” (Pope, 1976: 36). As RQs are typically used “to convince the addressees to accept the apparently obvious answer implied by the addressor” (Špago, 2016: 103), they may have serious ramifications in the context of hate speech as they can be used to convey to the hearer an apparently hateful idea about others in more implicative ways. According to Frank (1990: 737, italics and emphasis in the original), RQs seem to

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be most common in conflict interactions, such as “the conversational argument between spouses; the mediated therapeutic setting whose raison d’être was conflict; the ‘sociable’ argumentation of neighbours.” In such conflict interactions, as Frank (1990: 738) argues, RQs seem to “enable people to win an argument (short term), while not jeopardising a relationship (long term).”

2 Implying Hateful Meanings 2.1 Defining Hate Speech Hate speech undermines the ‘public good’, or rather makes the very objective “of sustaining it much more difficult than it would otherwise be” (Waldron, 2012, cited in Guiora & Park, 2017: 959). It does so “not only by intimating discrimination and violence, but by reawakening living nightmares of what this society was like—or what other societies have been like—in the past” (Waldron, 2012, cited in Guiora & Park, 2017: 959). However, despite the harmfulness and increasing pervasiveness of hate speech, there is no single, universal definition of the phenomenon nor complete databases of hateful language (ElSherief et al., 2018; Kopytowska et al., 2017; Millar, 2019). This is, in part, because hate speech is not ‘a pre-defined object’, rather, it is an incredibly context-dependent phenomenon (Millar, 2019: 146). It is, nonetheless, imperative for large social media corporations like Meta, the parent company of Instagram, to establish, disseminate and enforce policies that define and act upon hate speech, as such language is particularly proliferate on their platforms (Millar, 2019: 146; Paschalides et al., 2020: 2). The rise of hate speech on social media is due, in part, to their social and technological affordances, which have enabled formerly isolated groups of people who intend to disseminate hateful messages to organise in anonymity (Perry & Olsson, 2009). In response, Meta has developed and continues to refine its policy combatting hate speech. As of November 2022, they defined hate speech as:

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a direct attack against people—rather than concepts or institutions—on the basis of what we call protected characteristics: race, ethnicity, national origin, disability, religious affiliation, caste, sexual orientation, sex, gender identity and serious disease. (Meta, 2022)

Meta’s definition echoes academic interpretations of hate speech developed in order to identify, analyse and categorise such language. For example, hate speech is described by Baider et  al. (2020) as utterances that “target an individual or group on the basis of so-called ‘protected characteristics’”; these ‘protected characteristics’ referring to the perceived “race, ethnicity, religion, gender, disability, [and] sexual orientation” of the targeted group or individual. Who the target of hate speech will be at any given time is routinely influenced by the current (inter)national social and political climate (He et  al., 2021). The contextual specificity of attacks on the basis of protected social characteristics distinguishes hate speech from general impolite, offensive, and uncivil language, which may also result in its targets feeling angry, humiliated, and threatened but can, in contrast, include attacks of an individual’s identity more broadly (cf. Culpeper, 2021; Parvaresh & Tayebi, 2018). As an attack on the basis of protected characteristics, the hate speech producer can be described as attacking the target’s ‘social identity face’ (Spencer-Oatey, 2000). Face, a concept that frequents discussions of language impoliteness, refers to “the positive social value a person effectively claims for [themselves] by the line others assume [they have] taken during a particular contact” (Goffman, 1967: 5). Attacking social identity face with hate speech typically involves damaging the image of a protected social group by branding them as an ‘enemy’ or a ‘disease’ that will ‘infect’ the producer’s in-group (Van Dijk, 1993: 8–9; Hart, 2010). Hate speech producers may also take this opportunity to enhance the image of their in-group, in order to recruit new members to their in-group, increase the momentum and visibility of their hateful values and ideologies, and to justify further hateful attacks of their target (Kopytowska, 2017: 2). As “hate speech only becomes hate speech through evaluation” (Millar, 2019: 160), it is routinely met with strong negative emotional reactions of intense anger, fear, disgust and anxiety (Culpeper, 2021: 9; ElSherief et al., 2018: 48; Millar, 2019: 150). These reactions may support the hate

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speech producer and their beliefs, or denounce them in the form of counter speech (ElSherief et al., 2018; Mathew et al., 2018). While counter speech risks fuelling a ‘discursive spiral of hate’ (Kopytowska, 2017: 4), it has also been used to educate those using hate speech why their language is hateful, and to show support for targeted individuals and communities (Blitvich, 2022; Citron & Norton, 2011; Mathew et al., 2018). While attacks of protected social characteristics and related counter speech are vital indicators of hate speech, typically utterances must fulfil additional conditions before they can be classified as prosecutable offences under criminal law (cf. Tayebi & Coulthard, 2022). These conditions vary across nations, but taking the UK’s Crime and Disorder Act (1998), language must demonstrate “racial or religious hostility” and be “intended, or likely to stir up hatred based on race, religion and sexual orientation” (Law Commission Report No. 402, Sect. 1.3, 2021). In other words, language can only be officially classified as hate speech if the accused consciously planned to express hate or was motivated to do so (Culpeper et al., 2017: 5). While this requisite of intentionality has been difficult to prove in practice, it can help to distinguish hate speech from general offensive language, as in the case of the latter, language can still be deemed hurtful even if it was unintended by its producer (cf. Culpeper, 2011). To summarise the discussion thus far, utterances can be classified as hate speech if: 1. The utterance attacks the protected characteristics of a social group; 2. The utterance evokes counter speech in the form of extreme negative emotional reactions like fear, disgust and anxiety; 3. The utterance was intentionally produced to incite hatred, hostility and intolerance of the targeted social group and its members. The following section will explore the current research trends regarding hate speech.

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2.2 Research Approaches to Hate Speech Much hate speech research has followed lexical and semantic approaches which have entailed identifying the words, images and emoticons that impart hateful meanings. This has been in order to improve systems designed to automatically detect hateful language online (e.g. Bick, 2020; Mathew et  al., 2018; Motlogelwa et  al., 2021). A lexical-semantic approach was also used to distinguish between two forms of hate speech, ‘directed’ and ‘generalised’: directed hate speech attacks a specified individual, whilst generalised hate speech attacks a group of individuals that are perceived to possess a protected social characteristic (ElSherief et al., 2018: 43). A lexical approach, however, does have several limitations including that it does not detect implicit hateful meanings, it does not recognise the intentions and motivations of the hate speech producer, and it does not differentiate hate speech from general offensive and uncivil language (see Baider et al., 2020; Davidson et al., 2017 for further discussion). In recognition that hate speech is increasingly shifting from explicit to implicit forms in order to avoid online detection, research has begun to focus on forms of covert hate speech (e.g. Baider et al., 2020; Parvaresh, 2023). In other words, it is vital to not only identify the lexis, themes, and functions of hate speech, but also the forms of language that can be used as vehicles of hateful meanings. One such form, as will be elaborated on in the following section, is rhetorical questions.

2.3 Rhetorical Questions RQs are a special form of questions/interrogatives that are not informative or information-seeking but are intentionally phrased in such a way that they presuppose, or in other words make the addressee implicitly assume, particular values and beliefs (Ilie, 1994; Jung & Schrott, 2003; Rohde, 2006). As such, RQs have been previously described as ‘biased assertions’ (Sadock, 1971), ‘constrained questions’ (Rooy, 2003), and ‘redundant interrogatives’ (Rohde, 2006). In order for questions to be

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classified as RQs, they must fulfil the following three felicity conditions (Rohde, 2006): 1. The question has a predictable ‘obvious answer’. 2. The utterance is uninformative as interlocutors are already or become aware of the obvious answer. 3. All audience members’ answers are sufficiently similar. RQs’ capacity to presuppose particular beliefs has considerable communicative potential; as the form and function of RQs deviates from typical declarative statements and interrogative questions, they typically make the messages they convey more persuasive and memorable (Frank, 1990). As a result, RQs have become a powerful tool for texts with persuasive purposes such as marketing campaigns, political speeches, literary texts and news articles (Špago, 2016: 103). RQs, however, can also be instrumental in promoting hateful beliefs and intolerance of protected social groups, as producers of hate speech can trigger hateful ideas whilst not explicitly stating hateful values and beliefs.1 From the perspective of the producer of RQs, two general forms of RQs have been identified which contrast in their approach to presenting the obvious answer: ‘auto-responsive’ and ‘implicative’ (Schmidt-­ Radefeldt, 1977). Auto-responsive RQs are questions that include the obvious answer within them, so even people who are unaware of the answer beforehand are primed and constrained to particular responses. On the other hand, implicative RQs do not contain the obvious answer because the addresser expects their addressee to be aware of the answer by virtue of their shared general knowledge of the topic at hand. While auto-­ responsive RQs can convey beliefs to wider audiences, implicative RQs can challenge their audience’s commitment to certain beliefs whilst staying under the radar. As for examples, ‘Who else drives at 120 miles an hour apart from a total idiot?’ would be an example of an auto-responsive RQ, and ‘Which sensible person plays tennis at 45°C?’ would be an example of an implicative RQ.   See Abusch (2010), Belnap (1966) and Knäuper (1998) for further discussion of presuppositions. 1

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As far as the perception of RQs is concerned, there are four forms of answers RQs have the potential to elicit: negative, positive, non-null and multiple answers (Rohde, 2006: 135). These are elaborated on with examples below: (Confirmatively) Negative answer: The addressee is assumed to answer in negative agreement with the question. Example: • RQ: Who lifted a finger to help? • A: No one. (Confirmatively) Positive answer: The addressee is assumed to answer in positive agreement with the question. Example: • RQ: Has the educational system been so watered down that anybody who’s above average is now gifted? • A: Yes. Non-null answer: Only one answer is possible. Example: • RQ: Who always shows up late to class? (Emma is the only student who is consistently late to class) • A: Emma. Multiple answers: The addressee can form several answers. Example: • What’s going to happen to these kids when they grow up? • A: They will be rude to everyone. • A: They will ruin civilised society. (adapted from and influenced by Rohde, 2006: 135)

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With the potential for RQs to impart hateful meanings in mind, the following section describes the corpus of Instagram comments that target Afghan people.

3 Data and Method The dataset of the present study consists of a corpus of Instagram comments that target people of Afghan origin (Parvaresh, 2023), which was collected from BBC Persia’s official Instagram account. The comments in question were collected from the ‘comments section’ of a total of 58 posts about Afghanistan or Afghan people. The posts covered the period between early 2019 and early 2021, that is, prior to the taking over of Afghanistan by the Taliban. To remain focused on the aims of the study, special care was exercised to only choose comments made in response to the original post in question. As such, further responses to the comments were ignored.2 To ensure that the selection criteria for hate speech comments were consistent, and following Chulitskaya (2017), special care was exercised to ensure that the comments chosen for analysis were generally characterised by communication of ideas or meanings which would in some way target the dignity of Afghan people, express biased opinions about them or would expound harmful stereotypes about them. Having collected the original corpus of hate speech against Afghan people, which comprised more than 700 comments, the corpus was manually searched by one of the authors to pick out the instances of RQs. It was important to search the corpus manually as limiting the search to those comments which ended with a question mark and choosing only from those proved to be inaccurate, as there were occasions in which question marks were missing—as is typically the case in some computerised forms of communication, especially on social media (Tagg, 2015)— or did not constitute RQs. The entire corpus was therefore searched  While for the sake of tightening up the focus of the study, responses to comments were ignored, this had no effect on the quality of the analysis as the knowledge of the geopolitical context, information in the original post and familiarity of at least one of the authors with the local context, deemed sufficient in analysing RQs. 2

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manually to pick out any instances of rhetorical questions. In doing so, the focus was on the following definition of RQs: While an ordinary question seeks information or an answer from the hearer, a rhetorical question does not expect to elicit an answer. (Han, 2002: 202)

Having applied the definition, a total of 89 RQs were identified which had been used to phrase hateful statements as questions. In order to make sure that the identification of RQs was accurate and reliable, 15% of the questions were further checked by a linguist specialising in Persian to make sure that the selected examples were indeed RQs, not ordinary questions. For this purpose, and influenced by Rohde (2006: 135), the following recognition tests were used: (a) There was agreement between assessors that the questions would elicit an ‘obvious’ answer. (b) Both assessors agreed that the answers to the questions would be ‘uninformative’. (c) There was sufficient similarity between both assessors’ answers to the questions. High agreement was achieved, which could be taken as evidence that ordinary and rhetorical questions had been properly distinguished, and that the resulting corpus was ready for the next phase. The resulting corpus was then subjected to a qualitative analysis in which the role of RQs as conveyors of hate speech acts was explored. In doing so, and following recent research, it was decided to focus on the range of hateful meanings (i.e. illocutionary effects)3 the RQs under investigation would characteristically seek to convey to the addressee (cf. Parvaresh & Tayebi, 2021). In doing so, we were guided by Rohde’s  We consider illocutions as “performance of an act in saying something”, which is different from locutions, “an act of saying something” (Austin, 1975: 91, original italics; cited in Parvaresh, 2023: 65). This distinction pretty much guides our analysis of RQs as these devices (i.e. locutions) presuppose a certain state of affairs (illocutions) which they would expect the addressee to accept as unquestionable. 3

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(2006) categorisation of potential response types proposed for RQs (see Sect. 1.3. for a full discussion). In analysing the RQs, we took a so-called built reality perspective through which we viewed reality, that is, potential responses to and expectations from RQ, as “being constructed and reconstructed through the experience of social actors, like an inter-subjective and cultural experience” (Bumbuc, 2016: 422). To this aim, each rhetorical question was, following Tayebi and Parvaresh (2014), considered in its wider discourse, and by taking into account the geopolitical context in which it had been used. This way the authors managed to work out the intended meaning/ intention behind the RQs under scrutiny.

4 Findings The findings of the study show that, while RQs seem to have been of frequent use in imparting hate, there is variation in how they are formed and the responses they seek to invoke. Generally, it appears that in the corpus under investigation RQs function in four rather different ways. These are elaborated on below. Rhetorical Questions which Evoke Confirmatively Positive Responses to Hateful Illocutions It appears that RQs are sometimes formed with a view to eliciting a favourable illocutionary response from the addressee to the hateful illocution(s) being conveyed. By way of illustration, let us focus on a revealing example below, which has been taken from a comment under a post in which the addressee is informed of the fact that Afghanistan has started vaccinating some of its people with the help of the first batch of vaccines the country has managed to import from India. (1) ‫حیف واکسن نیست بزین به افعاین؟‬ Hejfe vâksan nist bezani beh afqâni? [Wouldn’t it be a waste of vaccines to inject them into Afghanis’ arms?]

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Evidently, the question in comment (1) is a rhetorical one as it does not seem to seek a genuine answer. In the geopolitical context under investigation, the answer the above question seeks to elicit from the addressee would be an obvious one, that is, something along the lines of ‘it would indeed be a waste of vaccines to use them on Afghan people’. In other words, it is through the above implicative RQ that the user in question manages to impart the hateful idea (illocution) that, for example, ‘Afghans are too inferior a people to care about’ to the addressee, which is most probably expected to invoke an agreeable response from the addressee (illocution). To further explore the role of RQs in eliciting a confirmatively positive response from the addressee, another example is provided in (2) below: (2) ‫خب به عمن! الان غصه ی این ها را مه خبورم؟‬ xob be ?anam! ?al?ân qoseje in hâ râ ham boxoram? [To hell with that! Should I pity these people as well?

]

This comment was made in response to a post in which the reader is informed that a high percentage of Afghan addicts are women. The user in (2) dismisses the importance of the news using the vulgar expression ‘To hell with that’. Evidently, the expression in question is not just an attempt by the user to express dissatisfaction and frustration at finding such content on the page in question, but also an attempt to distance himself from the news content in question. This observation is particularly supported by the fact that the user immediately asks an RQ, the answer to which would be obvious to the addressee. The answer would be nothing but an ironically positive one in which the addressee would agree (illocution), in an ironic way, with the rather hateful illocution of ‘Once again we are expected to sympathise with the sufferings of Afghans’. The repeated use of the ‘face with tears of joy’ emoji conveys that the user finds it hilarious that they are supposed to care about the number of female addicts among Afghans and as such will refuse to align with the media’s position on the matter.

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In this respect, the ‘face with tears of joy’ emojis used in the above-­ cited implicative RQ convey that the user never intends to align with the media’s anticipated response to the story. Rhetorical Questions Which Evoke Confirmatively Negative Responses As far as the corpus under investigation is concerned, it appears that on some occasions the RQs seek to elicit a negative illocutionary response from the addressee. Such a negative response would serve to confirm a negative characterisation (illocution) of the Afghan people which the RQs seek to impart to the addressee. By way of illustration, let us focus on the example cited below: (3) ‫یک افغان�ستانو دوس داره؟‬ ki ʔɑfqânestâno dust dâreh ? [Who loves Afghanistan? ]

This comment has been posted under an Instagram news post in which the reader is informed that Hamid Karzai—a former president of Afghanistan—has expressed his support for a peaceful process demanding all Afghan parties to be committed to establishing peace. Evidently, the comment in (3) does not involve any markedly hateful or derogatory expression; however, the question it poses is a rhetorical one as it does not seem to seek a genuine answer. Rather, what the implicative RQ in this example does is impart the illocutionary meaning that no one loves, and possibly by extension even cares about, Afghanistan. This interpretation is based on the observation that the propositional content of the comment is barely relevant to the content of the news post it responds to. In other words, the user was more concerned about the news item discussing Afghanistan rather than the details within the post itself. Presumably what such an RQ would seek to elicit from the addressee would be a confirmatively negative response such as ‘of course no one’. At the same time, the emoji of ‘face with tears of joy’ could indicate some personal accounts of emotional expression (Aragón, 2017), which seeks to ridicule

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the idea that anyone could love Afghanistan. In the context under investigation, such a personal account would take the format of ‘I don’t like Afghanistan at all, and I know you agree with me’, thereby further questioning its legitimacy and credibility. Here is another example of the use of RQs to evoke confirmatively negative responses: (4) ‫افغان�ستان کجاش خوبه که این جاش درست بشه؟‬ ʔɑfqânestân kojâʃ xubeh keh indʒâʃ dorost beʃeh? [In which part of Afghanistan have things ever been made right that you would think this problem [i.e. smuggling people] could ever be fixed ?]

This comment has been made in response to a post in which the reader is informed that smuggling people from Afghanistan into other countries has been on the rise and that Afghan officials are being urged to tackle this issue. The comment in question takes the form of an implicative RQ as the user does not seem to be asking a genuine question but rather one in which the expectation would be for the addressee to agree with or rather arrive at the same conclusion (illocutionary response) as the one posed by the RQ (illocution), according to which there is no part in Afghanistan which is not problematic or corrupt. In other words, this RQ imparts the hateful illocution that no part of Afghanistan is unproblematic or corrupt with the probable expectation of a confirmatively negative illocutionary response from the addressee. A sweepingly negative generalisation such as the one put forward in the above comment serves to downgrade the entirety of Afghanistan as a country in which nothing is right and everything is, for example, corrupt. Rhetorical Questions Which Will Allow Only One Response Sometimes hateful RQs are formed in such a way that the reader is led to think of only one answer. In the context under investigation, this answer would only be ‘Afghanistan’. By way of illustration, take a look at the two examples provided below. Example (5) has been taken from a post in which the reader is informed of the high death toll of a recent explosion in Afghanistan, and example (6) has been taken from a post in which the

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reader is informed of the rather high proportion of female addicts in Afghanistan. (5) ‫از ایران بدخبت تر کدوم کشوره؟‬ az ?irân badbaxt tar kodum keʃvareh? [Which country is more ill-fated than Iran?]

(6) ‫از ایران بدتر کجاست؟‬ az ?irân bad tar kojâst? [Which country is worse a place to live in than Iran?]

As both of these posts share some not-so-positive news about Afghanistan, it would be wise to consider both questions as rhetorical. In this context, in both comments the users make a tacit comparison, through implicative RQs, between the country they are most probably from, that is, Iran, and Afghanistan. This enables them to not only criticise the status of things in their own country, that is, Iran, but also impart the idea that there is only one country in the world, namely Afghanistan, where things are actually worse than Iran. Similarly, for the addressee there seems to be only one answer (illocution) and that would most probably, or rather be expected to be, ‘Afghanistan’. In other words, the RQs in these examples seem to convey the idea that the reader is not able to accept a null or vague answer and that the reader is also led not to come up with a null or vague answer to the RQs raised. RQs Which Evoke a Wider Range of Hateful Responses As far as the RQs under investigation reveal, there are occasions in which the RQ seeks to elicit a wider range of responses each of which would be aligned with the hateful illocution the original question poses. In order to better understand how these types of RQs pan out, let us focus on the below example in which the reader is informed of the news that Iran has extradited a number of Afghan prisoners to Afghanistan.

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(7) .‫ مهنی چند ماه پیش بود که رس پنج نفر ایراین توی تویرساکن رس بریدن‬.‫بدون اجازه و غریقانوین میان توی کشورمون ای دارن قاچاق می کنن ای قتل و جنایت‬ ‫ چرا ما اب بقیه هماجرهای داخل ایران مشلک ندارمی؟‬.‫شاید خوب مه توشون ابشه ویل بد مه زاید دارن‬ beduneh ?edʒâzeh va qejre qânuni miyân tuye keʃvaremun, yâ dâran qâtʃâq mikonan yâ qatl vâ djenâyat. hamin tʃand mâhe piʃ bud keh sareh pandʒ nafar ?irâni tuye tuyserkân sar boridan. ʃayad khub tuʃun bâʃeh vali bad ham zijâd dâran. tʃerâ mâ bâ baqiyeyeh mohâdʒer hâye dâxeleh ?irân moʃkel nadârim? [Uninvited and illegally they (Afghans) enter our country, and they are either busy smuggling or committing crimes. It was only a few months ago when they (Afghans) beheaded five Iranians in (the city of) Tuyserkan. There might be (a few) good ones among them but there are a lot of bad ones as well. Why don’t we have any problem with other immigrants in Iran?]

As the comment in (7) reveals, this user seems to believe that Afghans enter Iran ‘illegally’ and that they do illegal activities such as ‘smuggling’ or committing ‘crimes’. Having put such negative activity labels on Afghan immigrants living in or entering Iran, the user then goes on to provide an example of such activities to back up their claims, namely that prior to the time of speaking, Afghans had beheaded five Iranians in the city of Tuyserkan. The use of such an extreme example in which Iranians are juxtaposed with Afghans could well be considered an example of extreme ‘nationalism’ which serves to denigrate the other (i.e. Afghans) while exonerating the host (i.e. Iranians) of any wrongdoings at all. The user then goes on to say that there ‘might’ be some good people among the immigrant Afghan community, but they have ‘lots of ’ what they describe as ‘bad’ people. In this respect, the hedging device ‘might’ frames the positive evaluation of Afghan immigrants as one of a range of opinions. This allows the user to remain relatively detached from the claim that there are good Afghan immigrants whilst strengthening the second part of the argument, that is, that there are a lot of bad people among them. Towards the end of the comment, the user wonders in what seems like an implicative RQ why we (Iranians) do not have issues with other immigrants in Iran. While it appears that there is no single answer to the question raised, given the context under investigation, it would be reasonable to assume that the user has expected that the addressee should be able to provide a range of ‘obvious’ (Špago, 2016) answers to the question, ranging from ‘because they are not bad people’ and ‘because they are not like

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Afghans’ to ‘because they are not coming to the country illegally’. Regardless of the exact answer/response the RQ in (7) seeks to evoke in the addressees, it goes without saying that the responses would hinge around the rather tacit idea that ‘Afghans are not like other immigrants, they are worse’. To further discuss the use of such RQs, let us focus on the next example. This comment was made in response to a post in which the reader is informed of the closure, by Iranian officials, of an Afghan-run charity in Iran whose aims were to help disabled Afghans. (8) ‫اوالغ ها تاکی بزاریم افغانی جماعت هر کاری که دلش می خواد بکنه؟‬ ?olâq hâ! tâ kej bezârim ?afqâni dʒamâ’?at har kâri ke deleʃ mixâd bokoneh? [You idiots! Till when are we going to allow Afghan-types do whatever they want [in Iran?]

The user begins the comment by addressing what seems to be his fellow Iranian citizens. This is done through the vulgar expletive ‘(you) idiots’, which could be taken as evidence that they are unhappy about their behaviour. As we move on, the source of their anger and frustration becomes more obvious; they are unhappy that according to them Afghans are allowed (by Iranians) to do whatever they want in Iran. The user even refers to Afghans as ‘Afghan-types’, which could be a discursive strategy aiming to reduce Afghans to a homogeneous group by ignoring their skills, education, status, etc. The expression of their frustration is, however, done through an implicative RQ in which they ask the question: “[T]ill when are we going to allow Afghans do whatever they want (in Iran)?”. Interestingly the question has been formed in such a way as to include the user as well (i.e. the use of the inclusive pronoun ‘we’). In other words, the speaker seems to believe that everyone (including themselves) is responsible for what is being described as a situation in which Afghans are allowed to do whatever they want. Indeed, the user seems to be trying to impart the idea that the situation with Afghan immigrants needs action as it is far from ideal. In the context under investigation, it would not be difficult to imagine a number of possible answers the user in question would expect the reader to arrive at (illocution). Possible

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responses could be ‘Afghan immigration to Iran should come to an end’ to ‘Let’s do something about Afghans in Iran’ among others. Regardless of the exact response it may seek to evoke, all the possible responses revolve around the central idea that we need to stop Afghans from coming to Iran or at least stop them from doing whatever they want there.

5 Conclusion Despite the devastating effects of hate speech and its rapidly increasing presence on social media, combatting hate speech is a difficult task. This observation was supported in the seventh evaluation of the Code of Conduct on countering illegal hate speech online which disclosed that the number of reports of hate speech reviewed within twenty-four hours by social media companies committed to the online code of conduct dropped from 90.4% in 2020 to 64.4% in 2022 (European Commission, 2022). Given the regularity of RQs in conflict interactions, the present study focused on how rhetorical questions are used to denigrate people of Afghan origin, a group of people who have experienced widespread displacement and been subjected to intensifying hate speech. To this aim, the study focused on the use of such devices in an online context, that is, BBC Persian official Instagram account. The findings of the present study revealed that hateful RQs targeting Afghans elicited four types of illocutionary responses: confirmatively positive, confirmatively negative, non-null and multiple responses. Confirmatively positive responses were elicited by leading the addressee to (ironically) agree with the hateful RQ posed about Afghans, confirmatively negative responses were elicited by leading the addressee to assert that no part of Afghanistan is desirable or functional, singular non-null answers were elicited by implying that ‘Afghanistan’ is the only possible answer to the hateful RQ, and multiple responses were elicited by allowing the addressee to use their own prior geopolitical knowledge to form responses that would frame Afghans in a negative light. Also of note was the ‘face with tears of joy’ emoji that accompanied some RQs. These appeared to intensify hateful illocutions by indicating

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that the addresser finds it hilarious that anyone would care about Afghanistan and Afghan people. In other words, whilst not the focus of the present study, this supports previous findings that emojis and GIFs can be implemented in hate speech to support and intensify online hate speech (cf. Bick, 2020; Motlogelwa et  al., 2021). As such, the role of emojis and GIFs in online hate speech, especially in under-explored lingua-­cultures, may be an interesting avenue for future research to pursue. Whilst the findings of the present study may be applied to automatic processes of identifying hate speech, hate speech can take many other forms in order to achieve hateful illocutions and avoid censorship. Such forms can include, but are not limited to, the addition of “intentional typos and changing word boundaries” (Gröndahl et al., 2018). As such, whilst the present study sheds some light on how hate speech can manifest in the context of Afghan people, future researchers may be interested in exploring the relationship between other communicative forms and hateful meaning. Another avenue for continued research is distinguishing between what is and is not hate speech on local, national, and global scales. By refining definitions of hate speech, the process of identifying hate speech will become more accurate and reliable by extension. This, however, is no easy task as in the words of Millar (2019: 161), “[W]hat and whose meanings are prioritised in this process will differ according to interests despite additional attempts to standardise.” Even such a standard definition is far from established as “[d]efining hate speech in international criminal law continues to be elusive” (Fino, 2020: 57).

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9 Enabling Concepts in Hate Speech: The Function of the Apartheid Analogy in Antisemitic Online Discourse About Israel Matthew Bolton, Matthias J. Becker, Laura Ascone, and Karolina Placzynta

1 Introduction The new communicative conditions engendered by the development of the interactive web over the past two decades—in particular, the anonymity of social media and the social reinforcement dynamics of online communication—have contributed to a new boldness in articulating hate speech (Mondal et al., 2017). Hate ideologies of various stripes— from anti-blackness and anti-Muslim bigotry to misogyny, homophobia and, our focus in this article, antisemitism—have thus been granted a wider and potentially more receptive audience than perhaps in any time M. Bolton • M. J. Becker (*) • L. Ascone • K. Placzynta Zentrum für Antisemitismusforschung (ZfA) at Technische Universität Berlin, Berlin, Germany e-mail: [email protected]; [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_9

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in history. Each of these hate ideologies carries its own distinct, if at times intersecting, discursive repertoire of concepts, stereotypes, analogies and negative attributions that are in perpetual development. These can be used in a wide variety of ways: incorporated into statements whose meaning is direct and clearly discernible from a surface reading, or disguised through implicit linguistic structures and coded forms, requiring further contextual knowledge to parse. The significance of the latter is that hateful concepts and stereotypes which might otherwise face social (and even legal) sanction in online spaces can be articulated with greater confidence. This is particularly true of antisemitism: perhaps no other form of hate ideology contains such an array of implicit and disguised forms of expression. The long history of anti-Jewish prejudice has generated a deep ‘reservoir’ of tropes, stereotypes and myths that can be revived and reworked in different historical contexts (Gidley et al., 2020). Antisemitism can be, and frequently is, expressed directly in online discourse, with comments taking direct aim at ‘Jews as Jews’, and invoking classical antisemitic stereotypes such as the assertion of global Jewish power and influence, control over financial and media institutions, and even blood libel. But perhaps the majority of antisemitism online today eschews such direct means of communication; instead, it takes the indirect form of rhetorical questions, allusions, wordplay, irony, jokes and memes. Moreover, in the often-heated discussions around Israel and the Middle East conflict, condemnations and demonisations of Israel and Zionism are often characterised by the reworking and updating of classical antisemitic stereotypes now projected onto Israel as a Jewish state (Rosenfeld, 2016; Holz & Haury, 2021). Assertions that Israel constitutes the greatest threat to humanity or world peace today, that Israel is replicating the Nazis, or that Israel is deliberately targeting and revelling in the deaths of young children, cannot be regarded as a ‘legitimate criticism’ of the state, but rather

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a mode of antisemitism disguised in the form of anti-Zionism (Rensmann, 2020; Allington & Hirsh, 2019).1 In this chapter, we suggest that there is another means by which hate speech can be legitimated or escape sanction in online spaces—the use of what we are calling ‘enabling concepts’. In linguistic terms, an enabling (or opportunity) structure refers to statements which seek to extend what is ‘possible, sayable and legitimate in political discourse’ (Rensmann, 2004: 23). An enabling concept is one which creates the discursive conditions for such extensions. It may not appear to rank among the most extreme within the repertoire of each hate ideology. Indeed, it may not be classed as part of hate speech at all, although it should have a close connection to concepts which are. Its ambiguous status means that it may attract broad agreement or support within certain sections of society. As such, its use does not lead to the forms of counter speech or social sanction which the use of other, more obviously extreme, concepts may attract. But this relative security means that the enabling concept can be used as means to facilitate or legitimate the use of further, more extreme, forms of hate speech. This dynamic of facilitation is particularly effective when the enabling concept is itself routinely used by socially respected individuals and institutions—politicians, media figures, non-­ governmental organisations. This seems to be the case with the apartheid analogy in antisemitic discourse; it has become decoupled from its historical meaning and transformed into an expression of political commitment, or harsh criticism of (existing or assumed) injustice. By creating an appearance of reasoned, non-hateful discourse, one backed up by authority figures, it opens up a space in which more extreme concepts are granted a form of legitimacy that they would otherwise be denied. The  The question of where legitimate critique of the state of Israel ends and antisemitism begins is one of the most contested in both academic and public debates around this issue. It is the main point of contention amongst the multiple definitions of antisemitism that have been produced over the past 30 years, the International Holocaust Remembrance Alliance ‘working definition’, the Jerusalem Declaration on Antisemitism, and the Nexus definition being the most prominent. For all the heat this discussion has generated, however, there is broad agreement on all sides that antisemitic forms of anti-Israeli discourse exist, with notions of an all-consuming Israeli power and influence, or inherent evil, being agreed to be antisemitic across the field. The antisemitic status of the apartheid analogy does not, as discussed below, attract such broad agreement. Our aim in this chapter is not to settle this dispute. 1

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hypothesis put forward in this chapter is that each mode of hate ideology will have its own particular selection of enabling concepts which can be used to sanitise further expressions of hate speech. Our focus here will be on an increasingly central concept in discussions around Israel, Palestine and the Arab–Israeli conflict: what is known in antisemitism studies as the ‘apartheid analogy’, in which the state of Israel is compared to, or conflated with, the former Apartheid regime in South Africa. The chapter is based on qualitative analysis of over 10,000 comments posted on Facebook in response to UK media reports, from outlets across the political spectrum, on the May 2021 escalation phase of the conflict, in which the various forms of antisemitic expression articulated by web users were identified. Our analysis shows the apartheid analogy is one of the most frequent within online discussions around Israel. This should come as no surprise: in recent years, multiple human rights organisations and campaign groups within and without Israel have repeatedly made this analogy in official reports and public commentary, emboldening web users to conceptualise Israel in the same way. Indeed, many users directly appeal to the authority of these institutions when making the analogy. We show how the use of the apartheid analogy is often combined with other, more extreme, antisemitic concepts and stereotypes, and suggest that the social acceptability of the apartheid analogy acts as a gateway through which other antisemitic concepts are able to percolate through public discourse. This enabling function remains regardless of whether the apartheid analogy is itself regarded as a form of antisemitism. The first section of the article sets out the competing meanings of the term ‘apartheid’ in contemporary discourse, and the controversies over its use in discussion of Israel, before examining the meaning of ‘analogy.’ The second section presents examples of the analogy in online comments in response to UK media reports of the May 2021 escalation phase, focusing on instances where the analogy acts as an ‘enabler’ for other, more extreme, antisemitic concepts.

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2 The Meaning(s) of Apartheid In contemporary political discourse, to accuse a state or a political movement of apartheid is—in terms of moral weight and deserved opprobrium—second only to that of Nazism. ‘Apartheid’ today represents the most aggravated form of colonial oppression, political domination and state-sponsored racism, a universal and unqualified moral wrong that should never be repeated. Accusations of apartheid thus constitute the strongest mode of moral condemnation a modern nation state can face. And yet, for all its power and historical significance, the concept of apartheid is strangely elusive. There are three distinct meanings attached to the concept of apartheid. The first is a direct reference to the system of institutionalised racial ‘separation’ existing in South Africa from 1948 until 1994.2 Apartheid South Africa was a single nation state in which ‘whites’ and ‘blacks’ were segregated by a strict white-supremacist racial hierarchy, enforced by state violence. The second meaning is the legal definition, inspired by but not limited to the actual practice of South African apartheid. There is no single legal codification of apartheid as a crime in international law (see Kern & Herzberg, 2021). The most recent was the inclusion of apartheid as a crime against humanity in the 1998 Rome Statute of the International Criminal Court, where it was defined as “inhumane acts … committed in the context of an institutionalised regime of systematic oppression and domination by one racial group over any other racial group or groups and committed with the intention of maintaining that regime”.3 Each of these terms is subject to continued legal contestation, with the question of intent—is a regime of oppression specifically intended to secure racial domination—a particular point of debate. As such, the concept of apartheid as formulated in in international law is ‘ambiguous and inoperable’ (Bultz, 2013).

 The most comprehensive historical overview of twentieth-century South Africa and the development of apartheid remains Davenport and Saunders (2000 [1977]). 3  Rome Statute of the International Criminal Court (1998), https://legal.un.org/icc/statute/99_ corr/cstatute.htm (last accessed on 30 November 2022). 2

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The third meaning is that ascribed to ‘apartheid’ in popular public discourse. Over the past 30 years, ‘use of the word apartheid in the world has broadened and softened, referring to just about anything that means separation’—from tourists banned from some hotels in Cuba, to the disproportionate number of black men in US prisons, to the unequal global distribution of Covid-19 vaccines (Pogrund, 2014: xix). ‘Apartheid’ here takes on the character of what the conceptual historian Reinhart Koselleck calls a sloganwort, or ‘buzzword’—a ‘widely used forceful expression’ characterised by a ‘marked … lack of conceptual clarity’ (Koselleck, 2004: 43). And yet this inflated, indeterminate use has not detracted from its moral weight when applied to the actions or existence of states—hence the heated debate that has followed its use to describe Israel in recent years by Amnesty International, Human Rights Watch B’Tselem.4 These reports position themselves within the second, legalistic category. The Amnesty report (2022) admits, despite its uncompromising ‘Israel’s Apartheid’ headline, that it does not ‘argue … or assess whether, any system of oppression and domination as perpetrated in Israel and the Occupied Palestinian Territories (OPT) is … the same or analogous to the system of segregation, oppression and domination as perpetrated in South Africa between 1948 and 1994’ (p. 14). Rather it uses the ‘framework of apartheid … grounded in international law’ to name the ‘situation of segregation, oppression and domination by one racial group over another’ that it contends exists both within the borders of Israel and the West Bank and Gaza (p. 13). The question of whether using the term ‘apartheid’ in this loose way in discussions about Israel constitutes antisemitism has stimulated huge political and academic debate. For some, such as the authors of the 2021 Jerusalem Declaration on Antisemitism, the apartheid analogy fits within broader critiques of ‘systematic racial discrimination’ which ‘apply’ in ‘the case of Israel and Palestine’ as much as ‘other conflicts over national  Amnesty International (2022). Israel’s Apartheid against Palestinians: Cruel System of Domination and Crime Against Humanity. Amnesty International; Human Rights Watch, 2021. A Threshold Crossed: Israeli Authorities and the Crimes of Apartheid and Persecution. Human Rights Watch; B’Tselem, 2021. A regime of Jewish supremacy from the Jordan River to the Mediterranean Sea: This is apartheid. https://www.btselem.org/publications/fulltext/202101_this_is_apartheid (last accessed on 30 November 2022). 4

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self-determination.’5 Therefore, they suggest, ‘it is not antisemitic, in and of itself, to compare Israel with other historical cases, including settler-­ colonialism or apartheid’. This position is countered by other antisemitism scholars, who argue that, given the qualitative differences between the history of and current situation in Israel and that of Apartheid South Africa (Pogrund, 2014)—most importantly that, unlike the Arab–Israeli conflict the latter was not, in essence, a dispute around national self-­ determination (Morris, 2022)—the apartheid analogy turns what might otherwise be legitimate criticism of racist practices within Israeli society into a distorted, generalised depiction. The specific historical context of the conflict is thereby replaced by an overwrought emotional projection of the familiar image of South African Apartheid. Such projections—in which the wrongs of others or oneself are externalised onto the Jewish ‘other’—have long been regarded as a crucial distinctive ingredient of antisemitism (Adorno & Horkheimer, 1997). Moreover, the fact that supposed associations with colonialism and white supremacy have been used to uniquely condemn and demonise aspirations for a Jewish nation state long before the existence of the State of Israel, and certainly before the wars of 1967 and 1973, means for these scholars that the apartheid analogy is inherently antisemitic. While we do take a stand in this debate, we will leave this argument aside, and instead focus on the extent to which the social acceptability of the apartheid analogy acts as a gateway through which other, more extreme antisemitic concepts—over which there is far less controversy within the field—can enter mainstream discourse unopposed.6

 Jerusalem Declaration on Antisemitism (JDA). https://jerusalemdeclaration.org (last accessed on 24 November 2022). 6  Throughout the Decoding Antisemitism project, from which the analysis in this chapter derives, references to Israel as an ‘apartheid state’ have been categorised as antisemitic, due both to the historical genealogy of the concept briefly set out above, and for the linguistic function of the concept in discourse around Israel. However, both for reasons of space and because this judgement is not necessary for the analysis or acceptance of any potential enabling function of the apartheid analogy, we wish to bracket this debate for the remainder of the chapter. 5

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2.1 The Meaning of Analogy In linguistic terms, an antisemitic ‘analogy’ should be distinguished from a ‘stereotype’. A stereotype represents ‘characteristics of an individual or a group’ which ‘create a distorted or incorrect representation of the object through generalisations’ (Becker, 2021: 49). The stereotypical association of Jews with money, for example, is produced by combining two abstract representations of existing phenomena. Once combined, the stereotype can circulate through society, becoming embedded within shared conceptual frameworks, and creating a platform for further stereotypical associations, such as the notion of Jewish greed. An analogy, by contrast, is formed by creating a ‘relationship of equivalence’ between concepts ‘which do not primarily refer to abstract [ideas]’ but are relatively concrete in content, often including references to real historical scenarios (p. 171). This analogical equivalence can be made at the level of type, or nature— A is essentially the same as B—or at the level of modality, or action—A acts like B in some way (Thurmair, 2001: 3). The basis, or tertium comparationis, of historical analogies, such as that between Israel and Apartheid South Africa, is a concrete historical phenomenon or scenario, assumed to be familiar to the audience, and which the commenter seeks to mobilise in order to provide supposedly authoritative, and previously hidden, information about commonalities with the current scenario. Despite this concrete basis, historical analogies are—as noted above—frequently used loosely, with both the basis of comparison (the historical scenario) and the objects compared (the current one) presented in distorted, generalised, even stereotyped form. This vagueness, combined with the continued power of the memory of the evoked historical scenario, allows for greater emotional and argumentative power.

2.2 The Apartheid Analogy in Online Discourse The following sections of this chapter will seek to substantiate our hypothesis that the use of the apartheid analogy in online discussion around Israel enables other, more extreme antisemitic concepts, in a way

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that concepts such as Jewish evil or greed, widely recognised and reproached, do not. We will not merely identify which enabling interactions occur between the apartheid analogy and other analogies or stereotypes on the conceptual level, but also trace how this is achieved on the level of linguistic structure and rhetorical devices. This means taking account of the ways some users express these concepts in coded or implicit form, such as puns, intentional misspellings, and allusions. In order to identify relevant web debates on Israel that potentially trigger the phenomena introduced above, we focused on online reactions to the May 2021 escalation phase in the Arab–Israeli conflict. To summarise the background: a long-running legal dispute over the ownership of a number of properties in the Sheikh Jarrah area of East Jerusalem exploded into violence. Clashes between Israeli police and protestors at the Al-Aqsa Mosque were followed by hundreds of rockets fired at Israeli cities by Hamas militants in Gaza. In response, Israeli forces bombed targets in Gaza, with many civilian casualties. Violence between Jews and Arabs within Israel spilled onto the streets. Across Europe and the United States, large protests were held against Israeli actions in Gaza in numerous cities. Multiple violent and verbal attacks on Jewish people, Jewish-owned shops and synagogues were recorded in the wake of the protests. The escalation phase generated a huge amount of coverage in the UK media, and a similarly large number of web user comments responding to the reports.

3 Corpora Using a custom-designed tool crawling data from media outlets, we downloaded and analysed a total of 10,002 web user comments across 46 threads, collected over the period from May 8 to May 22, 2021, from nine major UK media outlets: The Daily Mail, The Guardian, The Independent, BBC News, The Spectator, The Times, The Telegraph, Metro, and Financial Times. As some do not host comments on Israel-related stories, or limit access to the comment function on their websites, we also took comments from their official Facebook pages. Out of this corpus, we selected threads with a minimum of 100 comments.

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4 Research Design Patterns of language use in each of the 46 selected threads of online comments were analysed according to the framework of Mayring’s (2015) qualitative content analysis, following highly detailed classification guidelines for the identification of antisemitism.7 The guidelines provide greater complexity to the basic IHRA definition of antisemitism in order to enable the recognition and classification of more than 40 antisemitic stereotypes, analogies and other concepts.8 These range from classical ascriptions of Jewish evil, ‘otherness,’ greed and vengeance, to updated concepts which blame Jews for ‘not learning from the past’, depict Israel as a genocidal rogue state, or equate the country with the Nazi regime in a victim–perpetrator reversal, discursively turning the (Jewish) victims of the past into the (Israeli) perpetrators of the present; the apartheid analogy forms one of the categories. If during the manual, qualitative analysis carried out by the expert research team there was any doubt about the antisemitic content of the comment, it was coded as non-antisemitic, to ensure accurate results. For such decision to be made, a comment needed to contain at least one antisemitic concept clearly recognised in the classification guidelines. Once identified as antisemitic, a comment was then further annotated according to its explicit or implicit linguistic structure (wordplay, metaphors and allusions, or speech acts such as call for action, appeal to authority, threat, or ironic statements), and any further semiotic elements (e.g. emojis or memes). When identifying comments in which the apartheid analogy acts as an enabler for other concepts, our analysis followed a linear, rather than logical, interpretation: all antisemitic concepts that followed the use of this

 We started by designing a classification system, composed of antisemitic concepts as well as linguistic and semiotic categories. We then collected the data, built a corpus, and examined it using MAXQDA, a data analysis program for qualitative and quantitative studies. By implementing the code system into the software, we were able to manually annotate each comment in the corpus in a systematic and efficient manner. We also integrated inductive categories that emerged while examining online discourse. 8  International Holocaust Remembrance Alliance. Working Definition of Antisemitism. https:// www.holocaustremembrance.com/resources/working-definitions-charters/working-definition-­ antisemitism (last accessed on 24 November 2022). 7

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analogy within a comment were regarded as being enabled by it.9 Such comments might include simple constructions such as “Israel is an apartheid state and the evil of the world” or “Israel is an apartheid state that represents the last bastion of Nazi atrocities”, but also comments where the web user establishes a causality, for example, by writing “Israel is an apartheid state because it carries out genocide”—where from a logical standpoint, the concept of genocide comes first, followed by apartheid. The understanding of enabling structure as a linear one stems from the inductive element of our approach to the analysed corpus, and more broadly to the online antisemitic discourse; we have observed that this syntactic pattern emerges as the most prominent one. Cognitive linguistic research has shown that, when processing the information within a statement, the priming effect achieved through the lexical ordering of individual concepts outweighs the meaning of conjunctions such as ‘because’ (see, e.g., Aujla, 2021; Foertsch & Gernsbacher, 1999). We have not, however, included the many comments in which the apartheid analogy is placed after a previous antisemitic concept, as this does not indicate an actively enabling effect. As soon as Israel is negatively connoted in an utterance, and in a way that seems acceptable to the discourse participants (as is the case with the apartheid analogy), subsequent demonising attributions can be made much more unrestrictedly, since the commenters already presuppose institutionalised segregation in the state (eventually demonised via stereotypes or Nazi comparisons). In other words, communication takes place on side paths which are not necessarily the focus of the utterance, but which decisively determine the conceptual framing of the reference area.  The extent to which the apartheid analogy plays a role of an enabler or solely a reinforcer or companion of clearly antisemitic attributions could be determined with all certainty through further, extensive long-term studies on corpora of different languages and web milieus. Another approach would be to conduct surveys on two large target groups, addressed separately. For target group A, a questionnaire would be used in which Israel and Israelis were conceptualised at the beginning via references to apartheid, and in the second part via antisemitic stereotypes. For target group B, the reference area would be characterised by stereotypes from the outset. This would allow to evaluate whether the presupposition of apartheid scenarios in the Middle East leads to a confirmation and justification of stereotypical attributions. If target group A confirmed the clearly antisemitic attributions more strongly than target group B, this would support the characterisation of the apartheid analogy as an enabler. 9

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5 Empirical Findings 5.1 General Observations We identified antisemitic concepts within 18% of the total corpus, most targeting Israel directly. The most frequent antisemitic concept was the representation of Israel as an evil10 entity (appearing in 23.7% of the antisemitic comments). This was followed by depictions of Israel as a terrorist state (10.8%)—an essentialising attribution distinct from accusations of acts of Israeli state terror, which—even if a blatant distortion—we do not code as antisemitic. Just over 10% of antisemitic comments made Israel solely responsible for the Arab–Israeli conflict (coded as a modern, distinct version of the older antisemitic idea that Jews are solely responsible for their own misfortune and persecution), implying that the conflict is not, in fact, a conflict at all but rather a one-sided expression of absolute domination by a single party. Nearly 7% accused Israel of committing genocide against the Palestinians. The depiction of Israel as a racist state—requiring more than specific accusations of racism— appeared in 4.1% of comments, which we regard as being very close if not identical in communicative function to the apartheid analogy. These stereotypes were often used by users to justify the denial of Israel’s right to exist (13%). The most frequent antisemitic analogies in the corpus were the apartheid analogy (11.7%), the nazi analogy in various linguistic forms (6%), and those comparing Israel as such (and not merely the West Bank settlements) to European colonialism (3%). In most of the comments in which it appears, the apartheid analogy opens the way to more than one further antisemitic stereotype or concept: 60 different sequences were identified in the entire corpus, most of which appeared only once. However, if we consider the first stereotype enabled by the apartheid analogy in each case, some tendencies can be identified. The most frequently enabled concept by the apartheid analogy was the evil representation of Jews/Israelis, followed by terrorist state and  Since stereotypes are phenomena that exist on the conceptual, that is, mental level and can be reproduced using language, they are presented in small caps according to the conventions of cognitive linguistics. 10

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Israel’s guilt. The most frequently enabled analogies were the nazi analogy and colonialism analogies. Other comments saw the apartheid analogy used to facilitate calls for an Israeli boycott, either in general terms or explicitly referring to the BDS (Boycott, Divestment and Sanctions) movement (see Topor, 2021).

5.2 Stereotypes Enabled by the Apartheid Analogy 5.2.1 Evil One of the most enduring examples of classic antisemitic stereotypes is that of the alleged maliciousness or evil nature of Jews, and the constant, large-scaled threat posed by Jews’ evil, malevolent or immoral acts to a specific society, or all mankind (Smith, 1996). The stereotype has been documented since the Middle Ages, finding expression in representations of Jews as monstrous, demonic or Satanic creatures; later on, the concept would emerge—in both verbal and visual form—in anti-Jewish or anti-­ Israeli campaigns, and was prominent within Nazi propaganda (Wistrich, 2013: 4, see also Herf, 2008; Schwarz-Friesel & Reinharz, 2017). Today, allegations of evil Jewish character are frequently attached onto grossly exaggerated accusations of Israeli human rights violations, war crimes, ethnic cleansing and genocide, or more generally the charge of threatening world peace. What sets the stereotype apart from justified accusations of law-­ breaking or immorality, is one or more types of generalisation—extending the actions of an individual or group to the whole Jewish nation or state, attributing to them a universal character devoid of specific temporal and spatial context, and explaining them by reference to innate maliciousness, intent on committing evil for evil’s sake or for personal gain. If an utterance lacks sufficient markers of such generalisation or contains details tying it to a more specific context, we do not categorise it as antisemitic in our analysis. Despite this conservative approach, the concept of evil was the most frequent antisemitic concept in the corpus, and the most commonly enabled by the apartheid analogy. The accusations range from short,

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direct and unsupported by further argumentation, to those where commenters elaborated at length. At either end of this spectrum, they tend to be expressed in strong, emotive language, and positioned as a call for help on behalf of the victims. The evil concept was expressed via a variety of explicit and implicit forms, examples here and in the following sections are presented in the order of their explicitness: (1) Israel an apartheid murderous state such a disgrace to humanity [author’s emphasis] (FB-METRO_20210510)11 Here the commenter first refers to Israeli ‘apartheid’, following it immediately with the implicit suggestion of evilness through epithets of ‘murderous’ (which implies either being guilty of committing murder, or intending to commit it) and ‘disgrace to humanity’—that is, posing a threat to mankind’s moral standing as such. The strongly derogatory word choices, unaccompanied by any further explanation, leave no doubt about the commenter’s strength of conviction. Importantly, here both the apartheid accusation and of malevolent nature are generalised—on the one hand, the source of threat is all of Israel; on the other hand, the victim is all of humanity. (2) Israel is apartheid regime and terrorist stateless bloodthirsty extremists and those who support it directly or indirectly are heartless crazy criminals. Justice for innocents Palestinians (FB-GUARD_20210514) (3) (…) Israel is a completely out of control rogue state intent on genocide of the Palestinians! Their filthy aparthied and murder goes utterly unchallenged by the worlds media, which makes them totally complicit in israels atrocitites! #IstandwithGaza #israelhellRogueState (FB-FT_20210513) The second and third examples both list apartheid within a longer list of perceived evil Israeli actions or behaviours. In strongly critical words, comment 2 equates ‘terrorist’ Israel to ‘bloodthirsty extremists’, while comment 3 adds a hashtag slogan referring to Israel as ‘hell’, hinting at its  Examples quoted in the empirical chapter appear in their original form, including all spelling, punctuation and grammatical errors. They are set off from the main text and numbered. 11

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infernal nature. In comment 2, the Israel criminal and evil nature is emphasised by the juxtaposition of its antithesis ‘innocents Palestinians’. Both undermine Israel’s legitimacy—comment 2 by calling Israel ‘stateless,’ implying Israel’s recognition as a nation state is fraudulent, and comment 3 by the descriptor ‘rogue’. Both comments extend the culpability to other actors, calling Israel’s supporters ‘heartless crazy criminals’ and implying that international media are complicit through their supposed goodwill towards it; the latter regurgitating the trope according to which the media has an allegedly complicit relationship with Jews.12

5.2.2 Guilt Efforts to delegitimise and even demonise Israel can sometimes be expressed through the presentation of Israel as the only party responsible for the Arab–Israeli conflict, or the denial that there is a conflict at all. Rather, Israel is accused of deliberate and unjustified attacks against the Palestinians, and the supposed contrast between the innately evil Israelis and innocent Palestinians emphasised: (4) Stop spreading wrong titles!Report the right order: (1) Occupation/ apartheid violence against Palestinians (2) Israel continues to escalate through forced expulsions, illegal settlements, beatings, prisoning and ethnic cleansing of Palestinians (3) Injured helpless Palestinians defend themselves desperately by throwing rocks (4) Silence from the world! So some respond with violence using primitive tools (5) Israel with fully loaded forces (supported by US tax $) responds with massacres! … Israel’s escalation of attacks in Jerusalem is part of a long-­ term policy to change the status quo in the city, displace the Palestinian population and replace them with Israeli settlers, and take over the city’s lands by approving illegal settlement projects. What

 This may be expressed in a range of ways, not necessarily antisemitic. While in some reiterations the media are presented as servile, devoid of agency and under Jewish control (see section on the concepts of power and instrumentalisation), in others they are merely sympathetic towards Jews and act of their own good will. 12

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Hamas did is a self defense action as the world is fabricating what is truly happening!!! #GazaUnderAttack#savesheikhjarrah (FB-DAILY_20210514) As in example 3, in this comment the user implicitly accuses the media of distorting the truth by using both the verb ‘stop’, which implies that the media would be used to this modus operandi (Simons, 2006), and the direct directive ‘report’ (Craven & Potter, 2010). The user reconstructs the historical narrative of the conflict to form a logical sequence of facts which erases the actual circumstances of Israel’s founding, the origins of Israel’s occupation of the West Bank, the violent, and at times terroristic, nature of Palestinian attacks on Israel, and the ideological intransigence of Hamas. This narrative leads the user to conclude that Israel is solely responsible for the conflict. This general concept is brought to bear in the specific circumstances of the escalation phase: by focusing on Hamas’s perspective (‘What Hamas did is a self defense action’), the user misleadingly suggests that the first violent attacks in May 2021 were launched by Israel. In example 5, the apartheid analogy enables the concept of guilt to be implicitly expressed through the description of ‘Palestine’—presumably Mandate Palestine—in idyllic terms prior to Israel’s founding, as ‘a peaceful and welcoming home and refuge to ALL people of different beliefs, backgrounds and faiths’. (5) Before the illegal establishment of the israeli appartheid state, Palestine was a peaceful and welcoming home and refuge to ALL people of different beliefs, backgrounds and faiths. The ethniccleansing crimes and zionist existence of Israel including its army, police, Government and illegal settlers are not acceptable according to UN law, all religions, morals and logic!—PLEASE SPREAD THE WORD and let’s help in making the global community less silent’ (FB-INDEP_20210510) Through the time clause at the beginning of the comment, the user implies that any tension or conflict in the region is the sole responsibility of Israel. The user’s emphasis of ‘ALL people’ allows them to underline

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the difference between an inclusive Palestine and an ‘israeli apartheid state’. The second passage emphasised (‘PLEASE SPREAD THE WORD’) suggests that this truth is being concealed by the media and needs to be unveiled and spread. By presenting themselves as the holder of the truth, the user tries to legitimise their comment and position, reinforcing through the declaration that Israel’s actions ‘are not acceptable to UN law, religions, morals and logic.’ This appeal to authority operates on four different levels—legal, religious, moral and logical. The order in which these four authorities are presented does not seem to be random. The author lists them in ascending order of certainty—from the legal realm, contested and determined by human beings, through religion and morality and onto the incontestable, transcendent nature of logic, thus implying that avoiding condemnation of the ‘Zionist existence of Israel’ is a literal impossibility.

5.2.3 Power and Instrumentalisation The stereotype of jewish power attributes disproportionate Jewish or Israeli influence upon, or even total control of, global public opinion, politics and the economy. Our analysis revealed that the apartheid analogy is frequently used to enable the claim that Jews and Israel wield disproportionate influence and power over the media: (6) Israel is an apartheid rule, which is ethnically cleansing Palestinians. It is in violation of numerous security council resolutions. BBC is the media arm of the Zionist, stop paying the TV license (FB-BBC_20210517) In this comment, the apartheid analogy is followed by the claim that the ‘BBC is the media arm of the Zionist.’ Here the user implies that Israel (or ‘Zionists’) directly owns the BBC, controls its content, and uses that control to spread ideas and narratives that suit its agenda. Thus, the apartheid analogy, used in relation to the Arab–Israeli conflict, opens a path to a more general and stronger antisemitic stereotype of jewish power that is only implicitly and indirectly linked to the conflict, and

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usually projected onto Jewish communities or an alleged all-powerful ‘lobby’ in Europe and the United States. By using the presuppositional verb ‘stop’, the user then incites the readership to actively counter this alleged jewish power over the media by boycotting the mandatory BBC license fee. (7) Israel is an occupation power, whether or not its supporters would shower you with adverts/ sponsorship … it is an apartheid regime as per HRW april report (release delayed by 20  years). (FB-SPECT_20210520) In this example, the apartheid analogy explicitly triggers the idea that Jews, or in this case Israel, enjoy a form of privilege which means, unlike other peoples or states, they cannot be criticised. According to the user, the alleged jewish power over the media, mentioned in the first sentence, is used to prevent the expression of legitimate critique through the imposition of a taboo of criticism. The apartheid analogy is used here to reinforce and develop the antisemitic stereotype expressed at the beginning of the comment. The user’s reference to the HRW report aims at giving weight to the accusation of Israel as an apartheid regime, with the claim its publication was “delayed by 20 years” possibly hinting at Israeli-­ imposed censorship. Thus, here the apartheid analogy and the HRW report are used both as a trigger and as evidence of the taboo of criticism imposed through the jewish power. In the following comment, the apartheid analogy triggers the idea that Israel actively redefines the boundaries of antisemitism in order to instrumentalise claims of antisemitism, cynically using them to secure political advantage: (8) The international community has known about the bulldozing of Palestinian settlements on the West Bank to build new homes for Jewish immigrants from abroad, the ghetto-ising, witholding of healthcare, violation of citizen and human rights for decades, with the US blocking every single UN resolution against Israel and finding this terrorist apartheid state selling it weapons … Of course it was Israel, which defined and criticism of illegal behaviour as anti-­

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Semitism, and which wanted and supported Hamas as official “government” of the Palestinians, to declare them all a terrorist organization aiming at destroying Israel, so they can keep up the staged self-defense war to expand their territory and fully annex the Westbank. (FB-INDEP_20210520) Accusations of the instrumentalisation of antisemitism implicitly activate the jewish power stereotype—without this power, Israel could not impose its definition of antisemitism. Here the user links it to the deceit stereotype—“so they can keep up the staged self-defense war”—implying that Israeli claims of responding to Hamas rocket fire are a ‘false flag’ act of disinformation and manipulation.

5.2.4 Child Murder The antisemitic trope of blood libel can be traced back to the thirteenth century, when Jews were accused of the kidnap and murder of non-­ Jewish, particularly Christian, children for use in ritualistic religious sacrifice (Teter, 2020: 4). Today the blood libel appears both directly and in the form of claims of child murder—powerful and often highly emotional accusations of the deliberate, even gleeful, targeting of Palestinian children by Israel, Israelis or Jews. As with the evil trope, identifying the child murder trope entails taking into account markers of generalisation and intentionality, , a lack of specific circumstances, disproportional focus on child victims in comments, suggestions that children are targeted deliberately, or that all Jews or Israelis are guilty of, or support, the killing of children. We were careful not to dismiss or become desensitised to genuine reports of children’s deaths; our aim was only to recognise the manipulation of facts that could result in generalised demonisation of Jews or Israel as such. (9) Israel is an apartheid state led by terrorists and baby killers. (FB-GUARD_20210514)

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Here the child murder trope is expressed explicitly, unaccompanied by supporting arguments. The phrase ‘baby killers’ makes an immediate and direct appeal to the reader through its informal register and nonchalant form, which contrasts with the seriousness of the content. The commenter does not qualify their statement by placing it in the context of war or military activity but relies only on the co-text of the comment thread to generate its relevance and meaning. It not only attributes the label of child murderers to the entire country of Israel, but also fails to specify the time, place or other circumstances in which they may be guilty of this serious accusation, instead presenting it as a general and innate national trait. (10) Apartheid israel uses Children as human shields.https://www.bbc. com/news/world-­middle-­east-­11462635 (FB-FT_20210513) Unlike the previous example, comment 10 refers to specific alleged actions of Israeli forces. It may seem that it does not qualify as antisemitic: the linked article reports on a concrete case involving two soldiers of the Israeli Defense Forces unlawfully using a Palestinian child as a human shield; the child murder trope is directly connected to the military context (‘human shields’). However, the user creates a fictional headline for the article, attributing the crime to the entire state of Israel, and uses an equally generalised grammatical structure that suggests habitual action, a suggestion strengthened through the reference to ‘children’ instead of ‘child.’ Appealing to the authority of an established national media outlet, the comment purports to offer a factual argument; in reality, the idea of ‘apartheid’ and child murder are linked together in a paralogistic sequence.

5.3 Intensifying Analogies Enabled by the Apartheid Analogy This section presents user comments in which the apartheid analogy is used as an enabler for the articulation of other, more demonising analogies, to question Israel’s right to exist and to call for a general boycott.

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Here the apartheid analogy no longer solely functions as an enabler of antisemitic stereotypes but lays the groundwork for portrayals of Israel as the continuation of historic European atrocities, from which imperatives for action, such as calls for a universal boycott or destruction, are derived.

5.3.1 Nazi Analogy (11) Why is the racist state of Israel allowed to get away with the confiscation of land from a military occupation lasting over 50  years? Why is the apartheid state of Israel given billions in Western money? Why is the Nazi state of Israel allowed to evade and threaten the International criminal court? There is only ONE answer, and that tells you how rotten and tied up the Western world has become by those allied to Israel. (FB-INDEP_20210510) In this comment, the user says that Israel is a ‘racist state’—a claim that gains precision through the attribution of Israeli ‘apartheid.’ This leads to the climatic attribution of state injustice through the equation of Israel with Nazi Germany.13 It is also striking that the commenter on the second level suggests that the Jewish state is granted a ‘free pass’ to act as they wish by international institutions and other states, implying a form of privilege perhaps created through the wielding of Jewish global power. Nearly all the allegations are communicated via rhetorical questions, in the centre of which, however, the justification of corresponding demonising attributions is not addressed. Since the commenter presents these insinuations as presuppositions, they continue to evoke the concept in the minds of readers even if the allegation posed implicitly by the rhetorical question is negated by a following comment. The positioning of the Nazi analogy at the end indicates a successive increase of the demonising potential which—through the repetitive, slogan-like conveyance throughout the comment—gains additional persuasion. In the last ­  The communicative functions of the Nazi analogy are the same as those of the apartheid analogy (see Becker, 2021: 197). The difference lies in the quality or intensity: Nazi atrocities represent the end point of humanly produced and industrially implemented cruelty and an unprecedented breach of civilisation. Accordingly, the arrangement of analogies described here represents an intensification of the scenarios. 13

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sentence, the user stresses that Israel and the Western states must be seen in opposition to each other, as the latter are moral and the former a rogue state that should not enjoy their support. (12) Stop playing victim when you have been the oppressor for decades. Zionist apartheid Israel, stop murdering Palestinian children and stealing Palestinians land Stop doing to the Palestinians. what Hitler did to you!!Stand with Palestine PS (FB-INDEP_20210515) In example 12, the apartheid accusation is again brought forth in connection with stereotypes (the allegation of child murder and mendacity, via the claim that Jews are playing the victim card to cover up alleged atrocities). And here, too, the attribution of Israeli ‘apartheid’ is used to subsequently draw the Nazi analogy. By making an emotional, direct appeal to the state of Israel, the author employs a powerful rhetorical device which, along with the personification of the state of Israel, serves as a strategy to intensify the alleged scenario. Moreover, through the repetition of the presuppositional verb ‘stop’, the user alludes to the stereotypical habits Israel is in such as the self-victimisation. In the following comment, the apartheid analogy is again combined with the nazi analogy. Jews ‘in this apartheid state’ are portrayed as a group that has not learned from the past, in that they are now inflicting what they once suffered on others. Statements like these indirectly allude to the Holocaust and equate Israelis in particular with Jews in general. This leads to a negative depiction of an alleged moral failure, through the claim that the victims of the Shoah should now behave decently: (13) The Jewish people in this apartheid state treats the Palestinians like the Nazis treated the Jews put them behind walls and shot at them people don’t realize a rubber bullet can still kill you so they shouldn’t be shooting them at all and maybe if they wouldn’t have this apartheid state and treat the Palestinian people like they were there before they ever arrived then maybe this wouldn’t be happening and just think all over the man-made invention of religion which is almost delusional because why would an entity allow this sort of behavior have a nice day (FB-DM_20210508)

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At the same time, the commenter claims that the Palestinians were the inhabitants of this region before the Jews immigrated—a subtle way of questioning Israel’s right to exist. Example 14 mobilises the famous Holocaust memorialisation phrase ‘never again’ to justify an equation between Israel—previously depicted as apartheid—and Nazi Germany. Furthermore, with the phrase ‘invoking the horrors of the past,’ the commenter refers to the generalising stereotype that Israel cynically exploits the Holocaust: (14) Do they like being illegally occupied, thrown out their homes or living under apartheid? Invoking the horrors of the past doesn’t help your case, I thought when we said never again, we were applying that to anyone and everyone? (FB-SPECT_20210514) Other users seek to justify defamatory statements about Israel, including apartheid and Nazi analogies, through the evocation of Holocaust survivors: (15) Enough bs.. Israel just needs to get the #^% out and stop this embarrassing apartheid … it embarrasses the true survivors of nazi atrocities..get a grip Israel (FB-BBC_20210521) The choice of the words ‘true survivors of the [N]azi atrocities’ here implies a division between good Jews and bad Israelis, whereby the former, according to the commenter, would necessarily have no sympathy for Israel. This argumentative strategy is based on Van Dijk’s (2006) ideological square. More precisely, by valorising a subgroup of this out-group, that is ‘the true survivors of nazi atrocities’, the user aims at legitimising and giving weight to the diminishment of the Israeli out-group (see also Van Dijk, 2015).

5.3.2 Colonialism Analogies In reaction to a comment describing Israel as a democracy, the following post denies Israel precisely this status—first by conceptualising it as an

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oppressor state (‘murderous military occupation’); second, because democracies cannot be apartheid regimes: (16) Sorry mate, democracies do not hold 6 million indigenous people under murderous military occupation, denying them human and civil rights. Neither can they be apartheid states.Israel as a religious state for Jews, is not, never was, and never can be a democracy. Not until it ends the occupation and gives full and equal rights to everyone, sharing the land of Palestine between the Israeli European colonists and the indigenous Palestinians. (FB-SPECT_20210522) Through the characterisation of Israelis as ‘European colonists’—which frames them as foreigners and illegitimate inhabitants of the Middle East—but also via the description of Palestinians as ‘indigenous’ twice, the comment combines the accusation of apartheid with the allegation of an illegitimate colonial state. The reference to ‘6 million’ people may potentially constitute an implicit allusion to the Holocaust.14 Example 17 positions the insinuation that Israel is an apartheid and ultimately colonial state into an even larger postcolonial context: (17) There is no fighting. There is only Israeli occupation, ethnic cleansing, military occupation and apartheid. When I say ‘Israel’ I’m referring to a group of settlers who are colonising Palestine. settler colonialism seeks to replace the native population of the colonised land with a new society of settlers. This is what ‘Israel’ is. Palestine is the country they’re colonising. How is it accepted? Because ‘Israel’ has the support from other settler colonies like the US/Australia and Canada as well as colonial powers like the UK/France/Belgium and others. For them to call out Israel, they’d have to answer ques-

 The reference to ‘6 million’ people living under Israeli occupation is a clear distortion of the facts: three million Palestinians live in the West Bank. According to the UN, there are a total of 5.6 million refugees in the surrounding areas outside Israel (including their descendants, as of 2019), but no reference is made to them in the quote. While the commentator’s usage of ‘6 million’ may not be a conscious Holocaust analogy, the instinctive use of such a historically-loaded figure—one that is repeated by another web user in a later comment—nevertheless activates the association between Israel and Nazi Germany frequently found within online discussions of the Middle East conflict. 14

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tions about their own existence, about reparations and justice for the people they colonised. (FB-BBC_20210511) The comment begins by insinuating that the idea of a conflict is misplaced (‘[t]here is no fighting’), indirectly giving Israel the status of an evil power (and the only guilty party) that maintains an antagonistic relationship with another people for no reason (see Sect. 5.2.1 on evil). The user claims that because of the historical legacies of former colonial states, Israel’s alleged crimes as a fellow ‘colonial’ state are not publicly criticised and the country continues to receive international support. The delegitimisation that emerges from this ascription is reinforced by placing Israel in inverted commas several times.

5.3.3 References to Fascism and Terrorism In the following two comments, the apartheid analogy is drawn in order to subsequently conceptualise Israel as a terrorist or fascist state: (18) The land taken over the last 73 years has not been part of any agreement. It has been taken illegally. The is a matter of international record. It doesn’t need to come from the Palestinians who are defending their very lives. Israel is the aggressive occupier of the country of Palenstine. It holds the Palestinians in apartheid. This is also a matter of international record. Israel is the terrorist. Israel is the mass murderer. Israel is a fascist state. All a matter of international record. If you’re gullible enough to ignore this to justify eth if cleansing, then you’re part of the problem, not the solution. (FB-INDEP_20210515) (19) the Israelis are the aggressors. You target civilian areas. You target children. You murder children. The Israelis are the terrorists. Holding Palestinians in locked up areas in a racist apartheid regime. Israel murder civilians to take land illegally in the biggest and most aggressive move in ethnic cleansing seen from your government and military over more than 7 decades. 131 Palestinians dead. 30 if those are children. 8 Israelis dead. You can’t blame those murderous

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numbers on Hamas. That blood is directly the responsibility of Israel. You are largely a fascist and racist nation with no problem in mass murder. (FB-INDEP_20210515) These examples show forms already discussed in the section on stereotypes. In example 18, Israel is personified as ‘terrorist’ and ‘mass murderer’, involved in ongoing ‘ethnic cleansing’ (see Sect. 5.2.1. on evil); in example 19, it bears sole responsibility for the conflict and deliberately murders children. These extremely negative attributions are then combined with a fundamental delegitimisation of the state, by (a) referring to ‘the land taken over the last 73 years’ and (b) transferring the negative attributions to a state that has existed for ‘more than 7 decades’. By pointing to this period, it becomes clear that Israel as such has been questioned from its founding—in contrast to criticism of West Bank policies. Thus, the ‘fascist and racist nation’, which according to the user is characterised by numerous atrocities, has no right to exist. Many commenters make the apartheid analogy by means of an appeal to authority: (20) thanks, I am up on the facts, it’s apartheid, the UN and most of the international community recognise that, I would further ad it is clearly ethno-supremacist Fascism and an illegal occupation with a litany of war crimes, breaches of international law and human rights, I don’t think an Instagram profile will change that reality. https://waronwant. org/news.../israeli-­apartheid-­factsheet (FB-SPECT_20210514) Here we see a clear example of how the use of the apartheid analogy in reports by NGOs (discussed in Sect. 2) provides social acceptance for the articulation of more extreme and explicit antisemitic concepts. Based on the allegation of an internationally shared perception of Israel, this commenter goes on to describe the country as ‘ethno-supremacist fascism’. In other similar comments, writers invoke authorities—here the writer Peter Beinart—to justify the call for the dissolution of Israel as a Jewish state:

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(21) There seem to be a lot of supporters of apartheid on here, but then it is the Speccy, so perhaps one shouldn’t be surprised. As many people, including liberal Zionists like Peter Beinart have concluded, the only viable long term solution is a single, confederate state covering all of historic Palestine with one person one vote, a written constitution and equal rights for all.Unfortunately, most Israelis won’t accept this. As Jewish supremacists they regard the Palestinians as lesser beings, and in some cases an enemy since Old Testament times that they must completely obliterate, for it is ‘God’s will. (FB-SPECT_20210514) In this example, the characterisation of Israel as an apartheid state is done through the corresponding labelling of other pro-Israel commenters (‘supporters of apartheid’), a form of indirect insinuation whose final target is the Jewish state. The user’s proposal to establish a ‘state covering all of historic Palestine’ instead of Israel would, in his eyes, be prevented solely by ‘Jewish supremacists’ who—allegedly influenced by the Old Testament—would deprive Palestinians of their status of human beings. In addition to reproducing the stereotype of Jewish guilt for the conflict, this commentary fails to recognise the very real danger that the dissolution of Israel within a bi-national or unified state could pose to Jewish life in the Middle East. Other comments substitute a vague labelling of Israel as an apartheid state with direct references to the historical scenario in South Africa. This in turn is used to again demonise the Jewish state via further categories such as terrorist state: (22) Better just to compare it to South Africa under apartheid. Or simply a terrorist state. (FB-GUARD_20210513) Such direct references are also used to question Israel’s right to exist, linked in the following with the colonialism analogy as well as the assignment of sole guilt on Israel’s side: (23) The reality is that Hamas does not need to fight for a Palestinian State per se:Israel has made two states impossible, which means, the

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only outcome now is one state shared equally by the European colonists who call themselves Israelis and the indigenous Palestinians. The Palestinians increasingly know they have right, justice, time and numbers on their side and since Israel cannot kill or drive out 6 million men, women and children, they simply have to wait until world opinion ends Israeli injustice and apartheid as it did end apartheid in South Africa.One State, shared equally by all as other colonisers have done, and a democracy where religion is secondary to State and citizenship. (FB-SPECT_20210522) The colonialism comparison is drawn by speaking of “European colonists who call themselves Israelis and the indigenous Palestinians”; the stereotypes of Israel’s evilness (just by mentioning Israel’s possible consideration to ‘kill’ millions of Palestinians) as well as its alleged sole guilt in the conflict communicated (via ‘The Palestinians increasingly know they have right, justice … on their side’)—and again, there is the potentially allusive reference to ‘6 million’ Palestinians (see example 19 above). The user’s call is to follow the example of South Africa and wait until Israel is forced to give up through isolation and boycott. As above, the proposal of establishing a state inhabited by both parties to the conflict as a solution to the Middle East conflict ignores the concrete reality of the conflict and how serious the consequences for Jewish life in the region would be under these circumstances. In example 24, one commenter draws an indirect comparison with South Africa (via a conditional clause) not to propose a one-state solution, but rather the forced emigration of all Jews. The latter should learn from their past and leave the country in time to forestall ‘humiliation’: (24) If south Africa is free from apartheid occupation Palestine will also be free. Now Jews will decide to leave Palestine in peace or with humiliation PSPSPSPSPSPS (FB-TELEG_20210517) Here, the commenter mentions only South Africa and Palestine by name. Given that the Apartheid South African state was dismantled in 1994, it can be assumed that the user is questioning Israel’s very existence.

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6 Conclusion This chapter has introduced the idea of ‘enabling concepts’ that facilitate the expression of hate ideologies. It has argued that the apartheid analogy acts as an ‘enabling concept’ facilitating the use of other antisemitic concepts in discourse around Israel and has demonstrated this function through analysis of web comments responding to news reports of the May 2021 Arab–Israeli conflict. We found that the apartheid analogy was not only one of the most frequent concepts in this corpus, but that it frequently acted as a bridge to the expression of other stereotypes, such as Jewish evil, guilt, power and instrumentalisation, and child murder, and intensifying analogies, such as the Nazi analogy, colonialism analogies and references to fascism. This is achieved by referencing the apartheid analogy before other concepts within the comment, priming the reader to deem the latter, more extreme concepts and stereotypes, acceptable. Web users cite these stereotypes and analogies in many different ways—the examples range from very explicit to subtle expressions: from clear comparative constructions and imperatives to the use of puns, allusions, intentional misspellings and rhetorical questions. The linguistic complexity of modes of antisemitism analysed in this article makes clear how important a qualitative approach is for examining online antisemitism. First, qualitative analysis means that ‘false positives’ resulting from hasty, context-insensitive categorisations can be avoided. Second, only qualitative research can capture the manifold possibilities of linguistic expression, and the interactions between different antisemitic concepts and the trends associated with them. Acceptance of the idea of the apartheid analogy’s function as an enabler for other antisemitic concepts—creating a kind of ‘breach through which the reservoir of antisemitism can flow,’ as Gidley et al. put it—does not entail agreement of its own antisemitic status (Gidley et al., 2020). Rather it indicates that there is a high level of tolerance for antisemitic speech within online communities today, which needs only a superficial level of supposed justification and legitimacy to be articulated, supported and/or left unchallenged. Our analysis shows that the enabling conditions of antisemitic attitudinal patterns on the web today are not merely a

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consequence of the anonymity and emotionalising discourse that characterises the contemporary online world, and which may trigger corresponding attitudes. Rather, developments and modes of discourse in the offline world create new conditions on the web itself. The increasing use and respectability of the apartheid analogy by public bodies and influential figures is precisely one such discursive development. By enabling the expression of antisemitic concepts and stereotypes to be couched in terms of a socially approved, abstract commitment against institutionalised injustice, the apartheid analogy provides ample opportunity for web users to freely articulate forms of more extreme antisemitic speech that would otherwise risk social sanction. The use of ‘enabling concepts’ to facilitate and sanitise the use of more extreme concepts is unlikely to be reserved for antisemitic discourse. Researchers investigating the dynamics of other modes of hate speech may be able to apply this model to their own object of study: identifying concepts which, by virtue of their broad social acceptability, create an ‘opportunity structure’ through which other concepts can be articulated without generating the opposition or social sanction they might normally face, thus expanding the space for racist, sexist and other discriminatory discourses within the public sphere.

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Schwarz-Friesel, M., & Reinharz, Y. (2017). Inside the Antisemitic mind: The language of Jew-hatred in contemporary Germany. Brandeis University Press. Simons, G. (2006). The use of rhetoric and the mass media in Russia’s war on terror. Institutionen för euroasiatiska studier. Smith, D. N. (1996). The social construction of enemies: Jews and the representation of evil. Sociological Theory, 14(3), 203–240. Teter, M. (2020). Blood libel: On the trail of an antisemitic myth. Harvard University Press. Topor, L. (2021). The covert war: From BDS to De-legitimization to antisemitism. Israel Affairs, 27(1), 166–180. Thurmair, M. (2001). Vergleiche und Vergleichen. Eine Studie zu Form und Funktion der Vergleichsstrukturen im Deutschen. Niemeyer. Van Dijk, T. A. (2006). Discourse, context and cognition. Discourse Studies, 8(1), 159–177. Van Dijk, T. A. (2015). Critical discourse analysis. The handbook of discourse analysis (pp. 466–485). Wistrich, R. S. (2013). Demonizing the other: Antisemitism, racism and xenophobia. Routledge.

Sources FB-BBC_20210511. Death toll mounts as Israel-Gaza violence escalates, BBC, May 11, 2021. Accessed from https://www.facebook.com/bbcnews/ posts/10158760308477217 FB-BBC_20210517. Homes and buildings destroyed in Israel and Gaza. BBC, May 17, 2021. Accessed from https://www.facebook.com/watch/?v= 926870761503663 FB-BBC_20210519. Biden tells Israel: De-escalate Gaza conflict today. BBC, May 19, 2021. Accessed from https://www.facebook.com/bbcnews/posts/10158 778864357217 FB-BBC_20210521. The ceasefire deal between Israel and Hamas. BBC, May 21, 2021. Accessed from https://www.facebook.com/bbcnews/posts/ 10158783951037217 FB-DM_20210508. Clashes in Jerusalem see 178 Palestinians injured. Daily Mail, May 8, 2021. Accessed from https://www.facebook.com/DailyMail/ posts/7000011310058557

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FB-DM_20210514. Netanyahu issues new warning as his forces drop 1,000 bombs into Gaza overnight. Daily Mail, May 14, 2021. Accessed from https://www. facebook.com/DailyMail/posts/7022558694470485. FB-FT_20210513. Homemade Hamas arsenal shakes Israel. Financial Times, May 13, 2021. Accessed from https://www.facebook.com/financialtimes/ posts/10159318798055750 FB-GUARD_20210513. Israel continues Gaza airstrikes amid rise in mob violence. The Guardian, May 13, 2021. Accessed from https://www.facebook. com/theguardian/posts/10160230486596323 FB-GUARD_20210514. Israel air and ground forces hit targets in Gaza Strip as death toll climbs. The Guardian, May 14, 2021. Accessed from https://www. facebook.com/theguardian/posts/10160232854331323 FB-GUARD_20210519. Biden tells Netanyahu he ‘expects significant de-­escalation today on path to ceasefire–live’. The Guardian, May 19, 2021. Accessed from https://www.facebook.com/theguardian/posts/10160247321306323 FB-INDEP_20210510. Explosions heard in Jerusalem after Hamas fires rockets. The Independent, May 10, 2021. Accessed from https://www.facebook.com/ TheIndependentOnline/posts/10159391552271636 FB-INDEP_20210513. Israel launches air and ground assault on Gaza strip. The Independent, May 13, 2021. Accessed from https://www.facebook.com/ TheIndependentOnline/posts/10159399510306636 FB-INDEP_20210515. A map of Gaza’s conflict zones as Arab-Israeli fighting intensifies. The Independent, May 15, 2021. Accessed from https://www.facebook.com/TheIndependentOnline/posts/10159403296311636 FB-INDEP_20210520. Israel and Hamas agree Gaza ceasefire. The Independent, May 20, 2021. Accessed from https://www.facebook.com/ TheIndependentOnline/posts/10159417096116636 FB-METRO_20210510. 20 killed including children in Israel air strikes after Hamas fires rockets. Metro, May 10, 2021. Accessed from https://www.facebook.com/MetroUK/posts/4413376472030940 FB-SPECT_20210514. Don’t compare Israel to Hamas. The Spectator, May 14, 2021. Accessed from https://www.facebook.com/OfficialSpectator/ posts/4557196434309924 FB-SPECT_20210520. What’s the real reason so many people hate Israel? The Spectator, May 20, 2021. Accessed from https://www.facebook.com/ OfficialSpectator/posts/4576461429050091

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FB-SPECT_20210522. Hamas doesn’t want a Palestinian state. The Spectator, May 22, 2021. Accessed from https://www.facebook.com/OfficialSpectator/ posts/4582613518434882 FB-TELEG_20210517. Yesterday, an Israeli air strike in Gaza killed 42 people, including 10 children, and destroyed several homes. The Telegraph, May 17, 2021. Accessed from https://www.facebook.com/TELEGRAPH.CO.UK/ posts/10160055283834749

10 Hate Speech in Poland in the Context of the War in Ukraine Lucyna Harmon

1 Introduction This chapter tackles an awkward and perhaps politically incorrect issue since it concerns a recent social phenomenon that is taboo in the mainstream media in Poland, namely the slowly increasing negative sentiments towards the Ukrainian immigrants in Poland, the Ukrainians altogether or Ukraine—in the middle of the war across the border. Although they are outbalanced by the Polish nation’s prevailing determination to shelter the refugees from the war-torn neighbour country, such negative feelings are perceived as a blemish on the desired image of Poland as Ukraine’s faithful and selfless friend, whose nation not only backs the government’s political and military aid for Ukraine but also unconditionally accepts the large influx of Ukrainian refugees as well as all the rights and social benefits that were granted to them. My research interest was aroused by a few rather strange Ukraine-­ related expressions encountered by accident in social media but also

L. Harmon (*) University of Rzeszow, Rzeszow, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_10

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heard in live conversations, the practical use of which I decided to check on Twitter, out of curiosity, entering them as keywords. My short list of items to be examined included the nouns ukry, Ukropolin and UPAdlina (all explained below) but it was soon extended by a few more expressions, retrieved from the scrutinised conversations, all of them intuitively perceived as negative in the context of their use. Based on the list compiled, the following research question was advanced: To what extent, if at all, do utterances based on the listed vocabulary meet the hate speech criteria? By analogy with Culpeper’s (2011: 6) observation on impoliteness, according to which “there is more to being impolite than swearing”, it was assumed that there is more to producing hate speech than using a negative expression.

2 A Definition Problem No universally accepted definition of hate speech exists, despite frequent usage of this term. A random examination of several volumes devoted to hate speech and displaying this phrase in the title, for example Assimakopoulos et  al. (2017), Pejchal (2020) and Neller (2022), offer just the information about the lack of a generally accepted definition. Interestingly, without providing a definition, Neller (2022: 11–12) quotes Gordon’s classification of hate speech into three types (general hate speech, harassment and incitement). He rightly points out that “[c]ontext is essential to our ability to understand an utterance or display as hate speech” (2022: 1), which the present research will confirm, too. A frequently quoted academic definition of hate speech is Farrior’s (1996: 3) proposition to apply this label to any utterance that is “abusive, insulting, intimidating, harassing and/or which incites to violence, hatred or discrimination”. The general character of this definition, that seeks to “protect” any individual from undeserved verbal attack, is its undoubted advantage. In more recent approaches, the concept of hate speech is usually narrowed to just selected semantic aspects or target groups. Richardson-Self (2021: 57), who refers to Gelber’s (2017) concept, claims that “to be a candidate for ‘hate speech’, the statement must target a shared trait that is a basis of a socially salient identity-based group”.

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Carlson (2021: 43–44, emphasis added) refers to the definition of Matsuda et al., who, in her paraphrasing words, characterise hate speech as “assaultive speech that can have negative long-term effects on the individuals targeted” and that “damages members of the defamed group by influencing them to believe in their own inferiority”. The problem with the practical application of this definition is obvious. It cannot be known in advance what possible long-term effects (if any) will occur; it can be only speculated about them, which entails that no utterance could be immediately identified as hate speech. In the glossary attached to her book, Carlson (2021, emphasis added) describes hate speech as “expression that seeks to malign an individual for their immutable characteristics, such as their race, ethnicity, national origin, religion, gender, gender identity, sexual orientation, age, or disability”. This definition, when extended over groups, too, seems to reflect the essence of what is usually presented as hate speech in relevant discourse in that it spells out the most sensitive human attributes that are often attacked and need protection. Still, it displays at least one obvious drawback, limiting the scope of hate speech to immutable characteristics, and thus excluding chosen statuses, like emigrants or clergymen, who are covered by the following, valid definition of the Council of Europe: “the term hate speech shall be understood as covering all forms of expression which spread, incite, promote or justify racial hatred, xenophobia, antisemitism or other forms of hatred based on intolerance, including: intolerance expressed by aggressive nationalism and ethnocentrism, discrimination and hostility against minorities, migrants and people of immigrant origin”1 [emphasis added]. It cannot pass unnoticed either that the European-Council definition seems to particularly protect only few selected groups at the expense of many others and leave a large margin for interpretation of its constituents, especially the notions of intolerance, aggressive nationalism, hostility and minority. Howard (2018: 2), whose book is devoted to religious hate speech, views hate speech as “speech that incites to hatred on the grounds of the religion or belief of the targets”, which implies a general definition of hate speech as speech inciting to hatred. It goes without saying that—unless  https://rm.coe.int/1680505d5b [Accessed 2.11.2022].

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explicitly said so by the speaker—it is difficult to determine, not to mention prove, if a given speech incites anyone to hatred. Brown and Sinclair (2020: 7) observe that “hate speech is a political and politicised issue” and is used both by the powerholders and those who seek power in order to discredit the opponents. Fish (2019: 71) argues that hate speech cannot be defined because it is “an unstable category”. He goes on to confirm its political character, adding that it constitutes “what your enemy says loudly, and if you are lucky enough to prevail in an election you may be able to get your enemy’s speech labelled ‘hateful’” (p. 73, italics original). Strossen (2018: 31) defines hate speech as “speech that expresses hateful or discriminatory views about certain groups that historically have been subject to discrimination (such as African Americans, Jews, women and LGBT persons) or about certain personal characteristics that have been the basis of discrimination (such as race, religion, gender, and sexual orientation”. This approach implies that previous persecution or harassment is necessary for groups or their representatives to be collectively protected, limiting the number of those entitled to such protection to well-known, “spectacular” cases. In this research, hate speech is understood as a metonymy, this is to say a concept that relates to hate, be it on the part of the speaker, target person, outside recipient or the text itself. The postulate of hate on the part of the text corresponds to Eco’s (1992: 64) concept of intentio operis as a restriction of the readers’ freedom to interpret the text based on their own experience horizon, requiring a reference to a given text as a whole. It is frequently necessary, though, to take into consideration the extratextual context in order, too, to recognise hate speech as such.

3 Method The corpus, retrieved from Twitter in November 2022, consists of 150 entries by anonymous users who express their negative thoughts about Ukrainians or Ukraine. In this chapter, only 25 selected, robust examples are discussed. The initial intuitive impression of negativity was verified with the use of Hart’s (2010: 67) topoi extracted from UK press articles on migrants, employed with the purpose to emphasise the objectionable

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features of this group of people and thus convey their overall negative image. Hart’s list of topoi includes burden (the out-group needs to be supported by the in-group), character (the out-group has certain undesirable characteristics), crime (the out-group consists of criminals), culture (the out-group has different norms and values than the in-group and is unable to assimilate), danger (the out-group is dangerous), disadvantage (the out-group brings no advantage/is of no use to the in-group), disease (the out-group is dirty and carries infectious diseases), displacement (the out-group will eventually outnumber and/or dominate the in-group and will get privileged access to limited socio-economic resources, over and above in-group) and exploitation (the out-group exploits the welfare system of the in-group). Regarding the common history and neighbourhood between Poland in Ukraine, it was expected that some of Hart’s topoi established for ethnically and culturally differentiated migration waves would not be reflected in the Polish corpus that is limited to Ukraine-related utterances. Finally, the corpus was examined for hate speech features. Vulgar components are quoted in full extension as required by the mere notion of a quotation. They are also translated with full-length equivalents, according to the comprehension of translation as a possibly faithful rendition without forced manipulations.

4 Polish-Ukrainian Relations 4.1 Historical Background It was only in 1991, after the breakdown of the Soviet Union, that Ukraine gained full independence as a state, for the first time in history. In the meantime, there was a brief episode of Ukrainian independence when the People’s Republic of Ukraine was proclaimed in 1918, but the new state was denied broad international recognition and soon became one of the republics of the emerging Soviet Union in 1922. Historically, the vast areas of the present West Ukraine, including the cities of Lviv, Iwano-Frankiwsk and Ternopil, belonged to the Polish territory until the breakout of the Second World War. On grounds of the

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Paris Peace Treaty of 1944, they were incorporated in the body of Soviet Union, whilst Poland was partly compensated for the loss with some German lands in the west. As a consequence, immense waves of forced deportations of German people from the newly established territory of Poland to Germany as well as from Poland to the Soviet Union and the other way round, constituted a permanent element of the desolate post-­ war landscape in this part of Europe. As pointed out by Makar (2016: 172), in the post-Soviet era, Poland assumed the role of the independent Ukraine’s “advocate” in its contacts with the West. The same author provides a list of prominent Polish intellectuals and politicians of strong moral authority who openly declared their support for the Ukrainian cause (p. 177). In addition, he stresses that both the Polish government and the majority (more than 60 per cent) of Polish people approve of Ukraine’s aspiration to join NATO and EU (p.  178). Importantly, about 1.35 million of Ukrainians lived in Poland before the Russian invasion of their country, most of them neatly assimilated through work, family, friends and neighbours.

4.2 Resentments As is usual between neighbours, Polish-Ukrainian relations, both official and informal, are full of mutual resentments based upon and resulting from historical conflicts that each party perceives from their own perspective (Tupalski, 2014: 136; Chodubski, 2012: 131). Polish people remember in the first place the Volhynian massacre of 1943, in which 100,000 Polish people were killed in a most primitive and barbaric way by members of the Ukrainian Insurgent Army (UPA), the armed forces of the Organisation of Ukrainian Nationalists (OUN), that is now appreciated and honoured in Ukraine for its struggle for an independent Ukraine. As pointed out by Kordas (2019: 99), the resolution of April 2015 of the Ukrainian National Council orders to venerate this organisation and prohibits their criticism, whilst a law passed by the Polish parliament in January 2018 penalises the denial of the crimes committed by Ukrainian nationalists. Some of the prominent OUN members, Stepan Bandera and Roman Shukhevych, who are celebrated in

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Ukraine as heroes and immortalised in statues and street names, are only referred to as bandits in Poland, with the blood of thousands of innocent people on their hands. In addition, the crimes committed in Poland by SS Galizien, a military troop formed in 1943 of Ukrainian volunteers who supported the Germans and helped them suppress the Warsaw Uprising of August 1944, are not forgotten and are still investigated by the Polish Institute of National Membrance (IPN). There is a stereotype of a cruel Ukrainian warrior based on the Ukrainian verb riezat (cut), resulting from the historical fact that UPA killers did not shoot but used axes, knives and bayonets to injure or mutilate their victims, and thus intentionally expose them to slow and painful agony. This stereotype was reinforced through the film titled “Wołyń” (Volhynia) of 2016, which was not approved for distribution in Ukraine. In Poland, it has been frequently emphasised that the Ukrainians have never apologised for the war crime in Volhynia, thus denying their guilt. It goes without saying that the same issues are discussed from a different angle in Ukraine (Bożyk, 2008: 192–194), where the Volhynian massacre is depicted as a civil war event, and members of the Ukrainian Insurgent Army are viewed through the lenses of national patriotism. Moreover, the Ukrainians have a grudge against Poland for the so-called Vistula operation that boiled down to a forced resettlement of ca. 15,000 Ukrainian people to the Soviet Union (strictly speaking: the Soviet Republic of Ukraine) in 1947, which meant for them the total loss of property they had to leave behind, and often separation from their close family members, too.2 Certainly, the descendants of the people affected by this action are still alive and remember. There is nothing irregular about divergent interpretations of disastrous events by the parties involved and no reason to perceive related comments as hate speech on the grounds of the mere topic. Nevertheless, as will be argued below, a new historical context may allow for such a perception.  Born in Poland close to the Ukrainian border, I have abundant first-hand information about this operation. The people who were baptised in the orthodox church were automatically registered as Ukrainians and those baptised by a catholic priest—as Poles. In the case of mixed marriages (there were many), sons were baptised after their fathers and daughters after their mothers. This entailed that a part of the family had to leave whilst the other was allowed to stay. 2

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4.3 Ukrainian Refugees in Poland in 2022 According to the information of the Polish Border Guard, about 7.3 million Ukrainian people, mainly women and children, have crossed the Polish border between the date of the Russian invasion of Ukraine, 24 February 2022, and 27 October 2022, when the data was published.3 In late September 2022, about 1.3 million of them were legally registered for social benefits, which considerably exceeds the EU norms of humanitarian help. They received accommodation subsidy, the same types of social support as Polish people, including child support and playschool subsidy, and have unlimited access to health services. Moreover, they were granted a residency permit for 18 months with nearly a guarantee to be allowed, on request, to stay longer, and immediate full access to the labour market. At the initial stage of the war, when the refugee wave was very strong, Ukrainian citizens could use local and long-distance public transport in Poland free of charge. It is vital to articulate that a vast majority of Polish people got engaged in all sorts of help for the Ukrainians, donating money, food and everyday supplies, and offering the refugees private accommodation in their homes. It is noteworthy in this context that in the poll conducted in 2017 in Poland, almost 70 per cent participants excluded the possibility of accommodating a foreigner in need in their home (Bera, 2019: 63). The enthusiasm, though, declined considerably with time, partly because strong emotions never last too long, but mainly because people in Poland faced progressive economic problems due to rampant inflation and tremendous increase of prices, which made them focus on their own misery rather than their neighbours’ but also blame the incoming Ukrainians for using and misusing the social system. Undoubtedly, the disclosed information on multiple cases of benefits unlawfully received by Ukrainian people, including some who only crossed the border in order to claim and cash them,4 did not change the public opinion for the

 https://300gospodarka.pl/news/uchodzcy-z-ukrainy-w-polsce-liczba [Accessed 27.10.2022].  https://www.portalsamorzadowy.pl/polityka-i-spoleczenstwo/mamy-nieszczelny-system-opiekispolecznej-­korzystaja-nieuczciwi-ukraincy,407418.html [Accessed 20.10.2022]. 3 4

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better. The common knowledge of similar practices by many immigrants, including Polish, in other countries, did not help either.

4.4 Linguistic Irritations A few outrageous “linguistic incidents” took place in Poland shortly after the Russian invasion of Ukraine. First, in his eagerness to announce Poland’s promptitude to support the Ukrainians in need, the speaker of the Ministry of Foreign Affairs said in public: “jesteśmy sługami narodu ukraińskiego” [we are servants of the Ukrainian nation]. His speech and the immediate comments, which are full of wrath and indignation, can be followed on Facebook.5 Nobody noticed the minister’s abundant explanations of the intended meaning, namely that we would do anything in our power to satisfy the needs of our Ukrainian friends. The said wording has become a popular slogan in Poland and is frequently used in attacks against the government. Second, during his speech on the Polish National Day on 3 May 2022, Poland’s president voiced his hope that there would be no border between Poland and Ukraine in the future, thus activating a conspiracy theory according to which a hybrid political body called Ukropolin should be created as a consequence of the war in Ukraine. The relevant part of his speech and some decrying comments can be followed on YouTube.6 Nobody paid attention to the president’s words highlighting a metaphorical character of the utterance in question which is now employed by the critics of the government’s policies in Poland labelled anti-Polish and pro-Ukrainian. Third, the Council of the Polish Language, a body that decides the rules of the use of Polish, recommended to replace the preposition na (mostly rendered into English with on) with w (mostly rendered into English with in) in the locative phrase na Ukrainie [in Ukraine]. This  https://www.facebook.com/watch/?v=3138869379659262 [Accessed 18.11.2022].  https://www.google.com/search?q=duda+nie+b%C4%99dzie+granicy+mi%C4%99dzy+polsk %C4%85+a+ukrain%C4%85&oq=duda+nie+b%C4%99dzie+granicy&aqs=chrome.3.69i57j0 i22i30l4.9403j0j15&sourceid=chrome&ie=UTF-8#fpstate=ive&vld=cid:b595ef59,vid:sLWvccE LJ8o [Accessed 18.11.2022]. 5 6

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means that the propositional phrase na Ukrainie (in Ukraine) should be replaced with w Ukrainie. The Council argued that the preposition ‘na’, when combined with a geographical name, suggested a region rather than an independent country, so the expression na Ukrainie hurts Ukrainian people. This argument contradicts the feeling of native speakers of Polish, who do not notice the difference. About a hundred of private people I have interviewed on this occasion (family, neighbours, my students and colleagues) share the following opinion posted on Twitter: Po polsku od wieków mówimy ‘na Ukrainie’, nie dlatego by komuś na złość zrobić. ‘W Ukrainie’ to fraza sztuczna, źle brzmiąca po polsku, narzucana ideologicznie, poniżająca Polaków. Damy sobie podyktować by obcy nam mówili jak mamy mówić po polsku? Dalej będę mówić ‘na Ukrainie’. [In Polish, we have said ‘na Ukrainie’ for centuries, and this not with the aim to annoy anybody. ‘W Ukrainie’ is an artificial phrase, imposed ideologically, humiliating Polish people. Shall we allow foreigners to dictate how we should speak Polish? I will continue saying ‘na Ukrainie’].

Fourth, when a missile struck the territory of Poland near the Ukrainian border on 15 November 2022 killing two people, Ukraine’s president publicly insisted—against the opinion of all international experts—that it was not fired by Ukrainians and demanded the right to participate in the relevant investigations.7 This declaration entailed vehement indignation in Poland, especially as Ukraine was not blamed for the incident that—in international experts’ opinion—was caused by a missile launched by Ukrainian air defense in response to the Russian bomb attack and hit Poland only accidentally. What the above-mentioned facts have in common is that they were redundant, served no palpable good purpose and caused nothing but a wave of public umbrage in Poland. In addition, the Ukrainian president’s stand is taken to imply that no Ukrainian apology is in order for the death of the two Polish men, which triggered emotional comparisons with the missing Ukrainian apology for the Volhynia slaughter.  https://www.dailymail.co.uk/news/article-11436145/Zelensky-insists-missile-hit-Poland-­­ Russian.html [Accessed 19.11.2022]. 7

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5 Hate Speech Against Ukrainians in Polish Twitter Entries In this section, I will quote some representative examples of the comments on Ukrainians and Ukraine posted by Polish Twitter users since the beginning of the war, mainly in November 2022. First, some derogatory derivatives are discussed, followed by presentation of selected entries on the forum dedicated to what is labelled “Poland’s Ukrainisation”. Finally, historical allusions in the new context of the Ukrainian immigration wave are quoted and analysed.

5.1 Simple Derogatory Derivatives 5.1.1 Ukry for Ukrainians The clipping ukry, short for Ukraińcy (Ukrainians), frequently appears, mainly in the plural, in comments hostile to Ukrainian people (technically, an analogue English noun would be ukres). This word dates back to 2014 and the famous rebellion known as Maidan but it is only now that it is gaining momentum and is used as an expression of disapproval of the Ukrainians’ presence in Poland. The original entry is always presented in italics and followed by a faithful English translation in square brackets and the category from Hart’s classification to which it belongs. Sometimes the entry contains a word or a phrase which obviously relates to the domain represented by the chosen category by its definition or implication. For instance, the kernel meaning of the noun Ukrainisation boils down to intended displacement, and bestiality in somebody’s genes implies danger to everybody around. The word “Banderites” stands in Poland for those who are responsible for manslaughter and is associated with crime. Similarly, Volhynia is only associated in Poland with manslaughter committed with particular cruelty, so the application of this name boils down to a reference to crime. Negative expressives are qualified as references to character since it is assumed that they are rooted in the perception of the object as bad and thus blameworthy.

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Some tweets reveal expressions of unlimited dislike of Ukrainian people, for instance: (1) Ukry bestialstwa mają w genach [Ukres have bestiality in their genes]: DANGER. (2) zgoda, ukry to od pokoleń największy wróg PL i tak już zostanie... [I agree, ukres are for generations the biggest enemy of PL and it will remain so…]: DANGER. (3) Jebane ukry. [Fucking ukres]: CHARACTER (4) ukry to zmutowane kacapy, cieszy jak się psie syny wyrzynają:) [ukres are mutated katsaps, pleasure to see how the bastards butcher one another]: CHARACTER. A user expresses his discontent with a large number of working Ukrainians in his area: (5) Ukry sprzedają chleb w mej piekarni Ukry prowadzą autobusy w mieście Ukry sprzątają w szpitalach Ukry są wszędzie na budowach Ukry pracują w bankach, firmach Ukry rządzą polityką tego “kraju” Ukry sprzedają znicze na cmentarzach Ukry grzebią jako grabarze nasze zwłoki. [Ukres sell bread in my bakery, Ukres drive the city buses, Ukres clean in hospitals, Ukres are everywhere on building sites, Ukres govern the politics of this “country”, Ukres sell candles at cemetaries, Ukres bury our bodies as gravediggers]: DISPLACEMENT. Interestingly, if the scathing aspect of the clipping (ukres) is ignored, the above recital can pass for an informative speech act in which verifiable information is provided. It clearly contradicts exclamations like this: (6) Ukry to tępe, nieskalane myśleniem ryje. Łatwo ich poznać. Najlepiej znają drogę po nasz socjal i później po pół litra, na początek, do Żabki. I jeszcze rozpuszczone socjalem ukrainki nie garną się do swojej roboty! [Ukres are dull snouts, untained by thinking. They best know the way to our social support and then to half a litre, to start with, to Żabka (a convenience store chain—L.H.). What’s more, Ukrainian

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women spoiled by the social support are not eager to work!]: CHARACTER + EXPLOITATION. It is clear that Ukrainian people are blamed both for working and for avoiding work in Poland, which comes down to a strong disapproval of their being there. Both entries picture the world around as it is but should not be in the eyes of the quoted beholders.

5.1.2 Banderowcy for Ukrainians Historically, the designation Banderowcy (Banderites, in Ukrainian: Banderovtsi) applied to the followers of Stepan Bandera, who, as mentioned above, was one of the leaders of the Ukrainian nationalist movement, associated in Poland with war crimes against the Polish population. In Ukraine, there still are organisations that promote Bandera’s nationalist ideas, calling themselves and being called Banderovtsi, without any negative aspects. In Poland, though, the word “Banderowcy” has a clearly negative connotation of an insult. Its use does not only stigmatise a given group as dangerous or criminal but also appeals to a very strong resentment. In many Tweets, this word is used as a synonym of Ukrainian people in general. To illustrate: (7) 1/3 mieszkańców Wrocławia to banderowcy. Oni też są nadreprezentowani w polskich służbach. [1/3 of the inhabitants of Wrocław (Poland’s third biggest city—L.H.) are Banderites. They are also overrepresented in Polish services]: DISPLACEMENT+CRIME. (8) Banderowcy w Polsce i na Zachodzie, głównie oblegają lokale rozrywkowe i sklepy z dostępem do alkoholu (…). [Banderites in Poland and in the West, they mainly occupy the entertainment venues and stores with alcohol]: CRIME+DISADVANTAGE. (9) Wynocha z Polski, my was banderowcy nie chcemy. [Get out of Poland, we don’t want you Banderites]: CRIME + CHARACTER. (10) Banderowcy wysysaja krew z Polski i Europy a walcza tylko dla siebie, dla swoich korzyści materialnych. [The Banderites suck the blood off

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Poland and Europe, and fight solely for themselves, for their own material benefits] BURDEN + DISADVANTAGE. The extension of the attributive Banderowcy over the whole Ukrainian nation resembles the practice of labelling the German people as fascists or Hitlerites in the wake of the Second World War, which is not common any longer but not completely abandoned either in Poland, especially when resentments relating to the barbarity of the said war come to the fore.

5.1.3 Upadlina or UPAdlina for Ukraine The Polish noun padlina means carcass or roadkill. It is combined with the abbreviation UPA, which stands in Polish—in Polish eyes—for the military formation that is responsible, as already mentioned, for the murder of Polish people during the Second World War. The use of this blending comes down to identifying Ukraine (in Polish: Ukraina) and its inhabitants with cruel troops who are hostile to Poland and should be feared rather than welcome and supported in Poland. These are some relevant examples: (11) Mi jest smutno jak widzę, że taki twór jak upadlina istnieje- mordercy i rzeźnicy [It makes me sad to see that a creation like upadlina exists—murderers and butchers]: CRIME + DANGER. (12) Żaden Polak w dzień święta niepodległości nie jest myślą z upadliną. Wszystko co dla Polaków ważne ukrainizują. [No Polish person’s thoughts are with upadlina on the national independence day. They ukrainise whatever matters to Polish people]: CHARACTER + DISPLACEMENT. At the same time, the blending Upadlina echoes the verb upadać (to fall) and its nominal derivative upadek (fall), thus suggesting Ukraine’s predictable or desired defeat.

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Another variant of the above word formation is Upaina, where the abbreviation UPA substitutes the first formant as evidenced in the following Twitter entries: (13) Kogo interesuje upaina? [Who is interested in upaine?]: CHARACTER. (14) Upaina i Rosja to jedno wielkie zło. Jedno warci drugich. [Upaine and Russia constitute one big evil. They are worth one another]: CHARACTER. (15) UPAina nie jest w NATO, ale całe NATO jej broń wysyła. [UPAine is not a NATO member but all NATO provides it with weapons]: DISADVANTAGE. These sorts of blending reinforce the stereotype of Ukrainians as Poland’s enemies who should be feared and fought rather than supported.

5.2 Ukrainisation of Poland The term Ukrainisation is an obvious linguistic analogon to the nouns Germanisation and Russification, both denoting comprehensive activities of Prussian and Russian occupation authorities aimed at suppressing the Polish culture and language during Poland’s partitions between 1773 and 1918, when Poland did not exist, and its former territory was divided between three occupant powers (Russia, Prussia and Austria-Hungary). Those who adopt the word “Ukrainisation” usually mean the quantity of Ukrainian people in Poland as well as their increasing importance in terms of rights bestowed on them within a short period of time through legal regulations, and perceive these phenomena as detrimental to Poland’s well-being. (16) Mieszkam w mieście, w którym już ponad 1/4 mieszkańców to Ukraińcy. Rosyjski (bo niemal wyłącznie tym językiem się ci ukraińscy patrioci posługują) słychać na każdym kroku. Tak więc jeśli to nie jest ukrainizacja, to ja nie wiem co by nią mogło jeszcze być [I live in a town where more than ¼ of inhabitants are Ukrainian. You can

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hear Russian at every turn (because these Ukrainian patriots use this language nearly exclusively). If this is not Ukrainisation, then I don’t know what else it could be]: DISPLACEMENT (17) Ukrainizacja czyli przesiedlanie do Polski Ukraińców i stwarzanie im lepszych niż mają Polacy warunków to fakt widoczny gołym okiem. [Ukrainisation as transferring Ukrainians to Poland and creating for them better conditions than those available to Polish people is a fact that can be seen with the naked eye]: EXPLOITATION + DISPLACEMENT. Like the example quoted in Sect. 5.1.1, the above entries can be perceived as informative speech acts which convey verifiable claims. But they also display a relevant characteristic of declaratives, giving an emotionally marked name to some phenomena which do not naturally fall under its scope. Some people believe that the influx of Ukrainian people to Poland is a deliberate, remote-controlled action, orchestrated by undefined supranational powers: (18) Ukrainizacja Polski jest jednym ze sposobów na zniewolenie nas. Trzeba się temu przeciwstawiać czynnie i biernie (…). [Ukrainisation of Poland is one of the ways to enslave us. We should oppose it actively and passively]: DISPLACEMENT. At the same time, it should be added that “Stop Poland’s Ukrainisation” is a slogan coined by the radical right-wing politicians, led by the parliament member Grzegorz Braun, who are openly opposed not only to the government’s policies supporting Ukrainian emigration but also to Poland’s financial, political and military assistance for the Ukrainian army. They highlight the divergence of Polish and Ukrainian interests and accuse the Polish government of representing the latter rather than the former. They discussed their relevant views in this matter during a meeting of the Parliamentary Board for International Relations and Polish Interests, held in August 2022.8  https://www.youtube.com/watch?app=desktop&v=PRpFfk0eW6A [Accessed 15.11.2022].

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5.3 Reminders of the Volhynia Slaughter As mentioned before, the Volhynia slaughter of Polish population in 1943 is one of the open wounds that need to be treated by historians, politicians and people of goodwill on both sides. There is a plethora of websites and forums, where new facts are revealed, and comments are placed. However, since the outbreak of the war in Ukraine, the name “Wołyń” [Volhynia] is used outside these dedicated places, in two main functions. Some suggest making the shape of Polish-Ukrainian relations depend on their apology for Volhynia: (19) Czy na poważnie sądzicie, że sojusz Polski z Ukrainą jest możliwy bez bardzo jednoznacznej skruchy Ukraińców za Wołyń? [Do you really believe that Poland’s alliance with Ukraine is possible without the Ukrainians’ unequivocal repentance for Volhynia?]: CHARACTER + CRIME. (20) Jeżeli teraz pozwolimy sobie na to, by wymazać Wołyń z kart historii bez rozliczenia I chociaż jednego słowa “przepraszam” ze strony Ukraińskiej, to będzie to jedna z największych naszych klęsk w historii. [If now we allow ourselves to delete Volhynia from the cards of history without settling accounts or at least one word “sorry’ from the Ukrainian side, it will be one of our biggest defeats in our history]: CHARACTER + DANGER + CRIME. Others allude to Volhynia in order to pass a warning of Ukrainian people as heirs of their criminal ascendants: (21) Ukraińcy stają się w wielu krajach nacją niepożądaną! Pozbawieni naturalnej dla innych zdolności adaptacyjnej żyją z socjalu! Owszem bywa wygodne ale degeneruje! Stają się pariasami Europy! Na koniec wylądują gdzie? Tak! W Polsce, gdzie zgotują następny Wołyń… [The Ukrainians become an unwelcome nation in many countries! Lacking the natural adaptation ability of other peoples, they live off social support! Certainly, it may prove comfortable but it degenerates! Finally, where will they end up? In Poland, where they will

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arrange another Volhynia…]: CHARACTER + EXPLOITATION + DANGER + CRIME. (22) Banderowskie ścierwa, szykują się na drugi Wołyń. Polacy obudźcie się. [Banderas’s stinkards, they are preparing for another Volhynia, Polish people wake up]: DANGER + CRIME. It is difficult to determine if, or to what extent, the authors of the above Twitter entries, and similar, foster and express their real resentments resulting from unsolved historical feuds. It cannot be ruled out that they unload their frustration about Poland’s weakened position in Europe and the resulting need to find and stigmatise the culprit.

5.4 The Ukrainians Provoke a Third World War In public discourse, no major politicians or political commentators suggest that the Ukrainians deliberately fired a missile on Poland’s, and thus NATO’s, territory in order to enforce NATO’s military reaction. There is, perhaps, one exception, namely the aforementioned faction around Grzegorz Braun which is perceived as anti-Ukrainian in essence—perhaps, because its status as a major power is disputable. However, the opinion that Ukraine’s president attempted to directly involve NATO in the war appeared on the Polish Internet long before the fatal incident, in March 2022,9 and have continued since then.10 Some comments posted after the unfortunate event correspond to these arguments: (23) Rakiety lecą ze wschodu. Ukry strzelały na zachód. Przypadek? [Missiles are coming from the east. Ukres fired west. Accident?]: DANGER. (24) Wszystko wskazuje na to, że był to celowy ukraiński atak rakietowy na Polskę by nas zmusić do udziału w tej wojnie. [Everything indicates that it was a deliberate Ukrainian missile attack on Poland in order to make us participate in the war]: DANGER.  https://wolnemedia.net/zelenski-probuje-wciagnac-nato-do-wojny/ [Accessed 26.11.2022].  https://konfederacja.com.pl/prezydent-zelensky-chce-wciagnac-nato-do-wojny-kuriozalna-­ propozycja- prezydent/ [Accessed 26.11.2022]. 9

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(25) Ukry puścili rakietę na Polskę celowo by rozpętać wojnę. [Ukres launched the missile at Poland on purpose in order to start a war]: DANGER Such views seem to reiterate not only the aforementioned interpretations of Ukrainian intentions as attempts to make NATO members join the war but also the stereotype of a hostile Ukrainian who harmed us in the past, so they cannot be trusted and should be feared instead.

6 Discussion and Conclusion In the 25 examples scrutinised, 7 of the 9 topoi excerpted by Hart from the British press are represented. The absence of culture does not surprise for obvious reasons, since there are no essential cultural differences between both neighbouring and historically closely related nations. What may surprise an outsider is the absence (form the totality of 150 entries collected for this research) of the category disease. However, it does not necessarily surprise an insider who knows that most troublesome Covid restrictions started being ignored in Poland with the huge, uncontrolled influx of Ukrainian refugees, which meant a collectively desired relief. Since then, a sarcastic saying has been going around in private circles that can be easily tracked in social media too: “Vladimir Putin put an end to the Covid-restrictions in Poland”. The character of Ukrainian people and the danger they pose for the Polish nation, followed by fear of crime and displacement, prevail in the comments, pushing in the background the aspects of exploitation, disadvantage and burden (which, by the way, are sometimes difficult to tell from one another). All the utterances quoted illustrate, in my opinion, hate speech as defined in Sect. 3 since they may reinforce or evoke negative feelings towards the Ukrainian nation during the fatal time of an armed invasion of their country, when this nation is granted protection of the international community. Twitter entries like those quoted above do, indeed, reinforce or evoke hostile attitudes of some Polish people to their Ukrainian neighbours, which can be evidenced through the likes they get or enthusiastic comments. But they are also—and this must not pass

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unnoticed—countered by those who disapprove of this manifested hostility, not necessarily in a cultivated way. For example: (26) Każdy kto pluje na Ukraińców i nazywa ich „ukrami”, jest putinowską kurwą. [Whoever spits on Ukrainians and calls them “ukres” is Putin’s whore]. (27) Jaka Ukrainizacja? To że flagi wiszą obok kogoś okna lub czasami słyszysz ukraiński cię wkurza? [What Ukrainisation? Is it the flags hanging next to somebody’s window or the Ukrainian language you sometimes hear that pisses you off?]. (28) Wołyń to było dawno. Trzeba żyć teraźniejszością, a obecni Ukraińcy nie mają nic wspólnego z ludobójstwem Polaków. [Volhynia was long ago. We should live in the present, and today’s Ukrainians have nothing to do with the genocide of Polish people]. Perhaps it is the counterspeech as “a non-coercive and non-censoring method for reacting to harmful speech, with the aim of impeding or at least diminishing its damaging effects” (Donzelli, 2021: 76), rather than penalisation attempts, that should be promoted as an antidote for the virulent contents of what comes over as hate speech, regardless of the handbook definitions of the latter. This opinion is also represented by Strossen (2018), which is clearly indicated in her book’s title, and Fish (2019), who describes the effectiveness of extralegal interventions in this area. Human emotions cannot be controlled, and they are known to contradict the reason and they will always find an outlet. Before the Internet era, a wide range of people’s sentiments were reflected in inscriptions on walls and fences. Hate speech as such is often linked with the author’s intention to spread or incite hatred, a factor that may prove impossible to determine. However, it can be replaced with the text’s intention, which corresponds to Eco’s concept of intentio operis, as explained above. Hate speech can be sometimes recognised as such only in context, for instance as one more contribution to an already-existing cluster of hostile expressions or persistent allusion to undisputed facts in order to undermine somebody’s human qualities. Nobody can deny a nation the right to recall their painful history, but the use of history in a new context,

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especially against the people in need, will not solve the existing problems and can only generate more pain and more disaster. It is noteworthy that on many occasions hate speech resembles a metonymy in that the speakers seem to articulate their disapproval of the world around rather than their strong dislike of a group of people who happen to constitute a part of the latter. Polish people are concerned about the rapidly rising living costs, the marginalisation of Poland’s position in European Union and the possibility that the war may spill into their territory. Some of them unload their frustration that hits the weakest of those who are around, namely the strange and displaced. Perhaps a more adequate, unambiguous name is needed for the kind of speech that concerns the Council of Europe as much as it worries the people of goodwill, namely repeated (and thus intentional) malicious agitation against an individual or a particular group of people that constitutes an affront to their dignity, with consequences unknown but potentially harmful to them.

References Assimakopoulos, S., Baider, F., & Millar, S. (Eds.) (2017). Online Hate Speech in the European Union: A Discourse-Analytical Perspective. Springer Open. Bera, R. (2019). Mowa nienawiści źródłem agresji i przemocy [Hate speech as a source of violence and aggression]. Annales Universitatis Mariae Curie-­ Skłodowska, XXII(3), 59–66. https://doi.org/10.17951/j.2019.32.3.59-­66 Bożyk, A. (2008). Konflikt polsko-ukraiński na południoo-schodniej Lubelszczyźnie podczas okupacji niemieckiej w świetle badań polskich i ukraińskich po roku 1989 [The Polish-Ukrainian conflict in the south-­eastern Province of Lublin under German occupation in Polish and Ukrainian historiography after 1989]. Facta Simonidis, 1(1), 191–206. Brown, A., & Sinclair, A. (2020). The politics of hate speech law. Routledge. Carlson, C. R. (2021). Hate speech. MIT Press. Chodubski, A. (2012). Stosunki polsko-ukraińskie: granice współpracy na początku lat 90. XX wieku [The Polish-Ukrainian relations: The limits of cooperation at the beginning of the 1990s]. Nowa Polityka Wschodnia, 1(2), 131–150. ISSN 2084-3291.

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Culpeper, J. (2011). Impoliteness: Using Language to Cause Offence. Cambridge: CUP. Donzelli, S. (2021). Countering harmful speech online. (In)effective strategies and the duty to counterspeak. Phenomenology and Mind, 20(202), 76–87. Eco, U. (1992). Interpretation and overinterpretation. Cambridge University Press. Farrior, S. (1996). Molding the Matrix: The Theoretical and Historical Foundations of International Law and Practice Concerning Hate Speech. Berkley Journal of International Law, 14(1), 1–98. Fish, S. (2019). How to think about hate speech, campus speech, religious speech, fake news, post-truth and Donald Trump. Atria. Gelber, K. (2017). Hate speech – Definitions & empirical evidence. Constitutional Commentary, 32(3), 619–629. Hart, C. (2010). Critical discourse analysis and cognitive science: New perspectives on immigration discourse. Palgrave Macmillan. Howard, E. (2018). Freedom of expression and religious hate speech in Europe. Routledge. Kordas, J. (2019). Geneza “ukraińskich zmian” w ustawie o Instytucie Pamięci Narodowej z 2018r. [Genesis of ‘Ukrainian changes” in the law on the Institute of National Membrance of 2018]. Annales Universitatis Paedagogicae Cracoviensis. Studia de Securitate, 9, 99–115. Makar, J. (2016). Współczesne stosunki ukraińsko-polskie w kontekście integracji europejskiej [Contemporary Ukrainian-Polish relations in the context of European integration]. Wschód Europy, 2, 169–181. https://doi. org/10.17951/we.2016.2.2.169 Neller, J. (2022). Stirring up hatred: Myth, identity and order in the regulation of hate speech. Palgrave Macmillan. Pejchal, V. (2020). Hate Speech and Human Rights in Eastern Europe: Legislating for Divergent Value. London and New York: Routledge. Recommendation No. R (97) 20 of the Committee of Ministers to Member States on “hate speech” (Adopted by the Committee of Ministers on 30 October 1997 at the 607th meeting of the Ministers’ Deputies). Accessed from https://rm.coe.int/1680505d5b Richardson-Self, L. (2021). Hate speech against women online: Concepts and countermeasures. Rowman & Littlefield Publishers. Strossen, N. (2018). Hate: Why we should resist it with free speech, not censorship. Oxford University Press. Tupalski, T. (2014). Stosunki polsko-ukraińskie w latach 2004-2010 [Polish-­ Ukrainian relations in the years 2004-2010]. Colloquium WNHiS Kwartalnik, 3, 135–157.

Part IV The Interactional Dimension of Hate Speech: Negotiating, Stance-Taking, Countering

11 Stance-Taking and Gender: Hateful Representations of Portuguese Women Public Figures in the NETLANG Corpus Rita Faria

1 Introduction The aim of this study is to examine hateful language directed at women public figures (whether public office holders or well-known media figures) in the Portuguese newspaper section of the NETLANG Corpus, focusing on how participants consistently take an adversarial stance of misalignment and opposition towards women and how patterns of gendered hateful discourse emerge. Derived from this, a distinction between aggressive language and hate speech proper will also be posited. So as to settle on an operationalised framework of analysis, this study sees hateful language as instances of stance-taking in discourse (Du Bois, 2007; Englebretson, 2007), whilst drawing on three main research strands: elements of the Appraisal framework (Martin & White, 2005), the theory of social actors (SAs) by Van Leeuwen (1996, 2008) and impoliteness studies, namely Culpeper (2011) and Culpeper et al. (2017). The

R. Faria (*) Universidade Católica Portuguesa (UCP) / CECC, Lisbon, Portugal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_11

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methodology is of a prominently qualitative nature so as to better capture the discursive nuances of adversarial verbal positionings towards women. Quantitative aspects of the analysis are provided merely in support of the qualitative examination. The following section will discuss the foundational concepts of this study, namely the notion of hate speech and its interaction with the notions of verbal aggression and/or impolite language, and misogynistic language (Sect. 2). Section 3 offers a detailed explanation of the methodology, followed by the discussion and analysis of the dataset (Sect. 4), before concluding with final remarks (Sect. 5).

2 Hate Speech and Misogynistic Language, Verbal Aggression and Impoliteness A crucial aspect of this study is to understand what hate speech is, how it is different from merely aggressive or impolite language and how it interacts with misogynistic language. There are differing notions of hate speech (see Fortuna and Nunes 2018 and MacAvaney et al. 2019 for a compilation of the different concepts subsumed under hate speech), reflected in its variegated nomenclature: “socially unacceptable discourse” (Fiser et  al., 2017; Vehovar & Jontes, 2021) and “assaultive speech” (the latter put forward by Matsuda et al., 1993, who focus on the impact of racist language) are examples. Assimakopoulos et al. (2017: 3) define hate speech as “the expression of hatred towards an individual or group of individuals on the basis of protected characteristics, where the term ‘protected characteristics’ denotes membership to some specific social group that could, on its own, trigger discrimination (…);” such characteristics are usually subsumed under “race, color, ethnicity, gender, sexual orientation, nationality, religion, or other” (Nockleby, 2000) and hate speech therefore generates “fear, intimidation, harassment and discrimination” (Nielsen, 2002). Furthermore, Fortuna and Nunes (2018: 5) aver that “hate speech is language that attacks or diminishes, that incites violence or hate against groups, based on specific characteristics such as physical appearance, religion, descent, national or ethnic origin, sexual orientation, gender identity or other (…)” and thus add the notion of “incitement” as part of hate speech.

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Although it is clear that a “one size fits all” definition is intangible (Baider, 2020), it is also clear that there is a semantic coherence in the literature available. Hate speech is an exercise of power resorting to discriminatory language, produced by someone who understands themselves to be superior to a more vulnerable group or individual who they verbally attack—it consummates a process of “otherisation” caused by “the actual or perceived presence of various Others in our socio-political reality” (Kopytowska & Baider, 2017: 135). Despite this semantic coherence, the concept of hate speech remains somewhat elusive; whether the notion of “incitement” should be included under hate speech is a point of contention, and the arrival of online platforms promoting verbal interactions between strangers has only amplified the difficulty in containing and regulating it (Assimakopoulos et al., 2017; Banks, 2010; Fiser et al., 2017; Fortuna & Nunes, 2018; Laaksonen et al., 2020; MacAvaney et al., 2019; Vehovar & Jontes, 2021; Warner & Hirschberg, 2012). This is why the Five-Factor Model advanced by Ermida and adopted by the NETLANG Project (see Chap. 2, this volume) seems to us to be a transparent proposition resolving much of the elusiveness encountered in the definition of hate speech. Under this model, anti-social discourse is qualified as hate speech based on content, target, purpose, agent and channel: hate speech expresses prejudice, targets a minority or underprivileged group or their representatives, its communicative intent is to demean or exclude members of such group, it is deployed by subjects who understand themselves as part of a dominant group and is uttered and disseminated in a public channel. Incitement may or may not be part of hate speech—hate speech can include incitement if its purpose is to encourage others to target disadvantaged groups, although incitement is not necessarily part of all instances of hate speech. The distinction between aggressive and hateful language thus becomes clearer—anti-social discourse that fails to meet all the five factors enunciated above is aggressive but not hateful. To this definition of hate speech, we would add an important proviso, which is its coercive nature, making it akin to “coercive impoliteness” (Culpeper, 2011; Culpeper et al., 2017), that is, “impoliteness that seeks a realignment of values between the producer and the target such that the producer benefits or has their current benefits reinforced or protected”

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(Culpeper, 2011:252). The motivation for this is a sense of justice and restoration of core values that the producer feels can be achieved by resorting to coercive, hateful language (Culpeper et al., 2017). Aggressive language in itself begs some reflection—it is usually studied under the umbrella of im/politeness studies and is very often seen as the equivalent of impolite language as posited by Culpeper (2011). The concept of im/politeness itself (encompassing the prefix “im” so as to designate both politeness and impoliteness) is, as Watts (2003) points out, the subject of “discursive struggle”—speakers themselves do not agree on the meaning of the lay term “im/politeness” (im/politeness 1 or first-order im/politeness, following the terminology by Eelen (2001) and Watts (2003)), which is mirrored in an array of differing scholarly definitions (second-order im/politeness or im/politeness 2). The discursive view of im/politeness (Bousfield, 2008; Locher, 2006; Locher & Watts, 2005, 2008; Mills, 2003) postulates that there is no inherently polite or impolite utterances, but rather discourse which is evaluated and taken by participants as such. Im/politeness thus becomes the subject of localised evaluation on the part of interactants. This view contravenes the previous pragmalinguistic, Gricean uptake on politeness seen as a derivation from the Cooperative Principle such as Leech (1983) and Brown and Levinson (1987). The latter’s influential theory posited politeness as “facework”, that is, as a set of verbal strategies designed to redress attacks to “face”, a notion drawn from Goffman (1967) and understood as a set of wants imposed by “individuals’ self-esteem”, namely the desire to be approved by others (positive face) and the desire to have one’s actions unimpeded (negative face). The mitigation of face attacks often demanded deviations from the CP, or politeness strategies. Bearing in mind the polar ends of the im/politeness2 spectrum—the defined pragmalinguistic phenomenon of facework and a negotiated, localised meaning dependent on participants’ evaluations of particular discursive behaviour—Culpeper’s (2011) definition of impoliteness “proper” strikes a balance, which is the reason why we find it appealing. Acknowledging that “it is difficult to see how communication could proceed without some shared conventions of meaning” (Culpeper, 2011: 123), the author sees im/politeness as a semantic and a pragmatic phenomenon, “inter-dependent opposites on a scale. (Im)politeness can be

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more determined by a linguistic expression or can be more determined by context (…)” (Culpeper, 2011: 125). Ultimately, it is the interaction between the two that determines impoliteness, that is, the interaction between the “semanticised components” and their “interpersonal contextual effects”. Impoliteness is therefore more or less semanticised, or more or less context-dependent, but it always entails “a negative attitude towards specific behaviours occurring in specific contexts”. Further, such behaviours are impolite because “they cause or are presumed to cause offence” (Culpeper, 2011: 23) and on the whole, “[a]ll impoliteness has the general function of reinforcing or opposing specific identities” (Culpeper, 2011: 252). The nexus drawn between offensive (or impolite) language and identity is of import as it encapsulates the “social harm” (Culpeper, 2011; Culpeper et al., 2017) caused, that is, “damage to the social identity of target persons and a lowering of their power or status” (Culpeper, 2011: 4). Therefore, we do not feel that a distinction between impolite and aggressive language is needed, and we will follow Culpeper’s (2011) suggestion that “impoliteness” should be used as an umbrella term for language bearing the potential to cause social harm, therefore including aggressive language. Impoliteness (or impolite language), verbal aggression and aggressive language are thus taken as synonyms, are used interchangeably in this study and are generally understood as language causing some sort of social harm. Ultimately, the greatest social harm is caused by hate speech, with its potential to severely damage a collective public good by forcing a coercive realignment of values dependent on those of the dominant group. What is left now is to ponder on the role of gender in the realm of hate speech. As with hate speech, the definition of misogynistic language (especially online) seems surprisingly elusive—“desperately seeking a definition” for online misogyny, Jane (2017: 17) defines it as “‘gendered cyberhate’, ‘gendered e-bile’, and ‘cyber VAWG’ [ violence against women and girls] (…) to refer to material that is directed at girls or women; that involves abuse, death threats, rape threats, and/or sexually violent rhetoric; and that involves the internet, social media platforms, or communications technology (…)”. Misogyny itself is thus not confined to language, although language plays an important part in it—Duggan and

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Mason-Bish (2021: 19) define it simply as “the dislike of, contempt for or ingrained prejudice against, women”, and Siapera (2019: 21) also sees misogyny as an “umbrella term” encompassing “all kinds of negative experiences that women go through online because of their gender, ranging from harassment and name calling to doxing and rape threats”. Interestingly, Ging and Siapera (2018) place the notion of “harm” (psychological or physical) at the core of misogyny whilst recognising how it has been “intensified and amplified in online environments”. This takes us back to the notion of “social harm”, which remains relevant to understand misogynistic language as a means to undermine the public voice that has taken women centuries to reclaim: the Internet is evolving rapidly into a space which is increasingly hostile, particularly for vocal women advocates. The backlash that such women receive for speaking out is (…) not only damaging, but also severely undermines the idea of equality of participation in public life. (Barker & Jurasz, 2019: 96)

Indeed, the nexus between hate speech and misogynistic speech has been recognised by Richardson-Self (2018: 12) when she equates the two. The latter bears “all the hallmark traits of hate speech. It targets a historically and contemporarily oppressed group, is characteristically hostile, systematically violent, and degrades, stigmatizes, vilifies, and disparages its targets (…)”. In light of this, misogynistic language is discriminatory, prejudiced language following the NETLANG Five-Factor Model in terms of message, target, purpose, agent and channel. Further, its coercive nature causes social harm due to its effort to strip women from effective public participation. The coercive facet of hate speech proves instrumental in order to draw a line between misogynistic language and verbal aggression against women—hateful language is directive in its core insofar as it means to direct the targets to confine themselves to a subaltern position against their will. This also ties in with our view that hate speech is akin to coercive impoliteness, a function of impoliteness involving actions “not in the interest of the target” (Culpeper, 2011: 226) with the aim of constraining such target’s freedom of action and opposing their identity.

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The following sections will present the data subset selected from the Portuguese NETLANG Corpus (2018–2022) and the constitution of an analytical and methodological framework for data analysis, followed by discussion.

3 Theoretical Framework and Methodology This study worked on an already-built, ready-to-use corpus of hateful language, NETLANG; our corpus is a subset extracted from the latter and consists of 180 articles collected from its Portuguese newspaper section. The comments selected were in response to articles focusing on the activities of Portuguese women public figures, resulting in a corpus of 1831 comments, 64,995 tokens and 55,088 words. Our sub-corpus focuses on five public figures which were the object of most comments and therefore had the most representation: • Catarina Martins, leader of the left-wing party Bloco de Esquerda [Left-­ Wing Block], with five elected MPs. • Cristina Ferreira, a very well-known TV presenter who started her career on the morning show of the open-channel TVI and is now not only a presenter but also a partial owner of the TV station. • Graça Freitas, a doctor and head of DGS (Direção Geral de Saúde [Directorate-General of Health]), who, during the pandemic, was in charge of a daily broadcast informing citizens of COVID-19-related numbers and guidelines. • Joacine Katar Moreira—until recently, a Member of Parliament (she lost her seat in the 2022 Parliamentary election). She ran as part of the left-wing Livre [Free] party, was expelled and remained an independent MP. She was one of the only three black women in the Portuguese Parliament, and speaks with a consistent stutter—this detail is mentioned here because it is recurrent in the comments about her. • Marisa Matias—a member of the aforementioned Bloco de Esquerda led by Catarina Martins. Marisa Matias was the party’s presidential candidate in the 2021 elections and came in the fifth place, with three, 95 voting percentage.

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The particularity of these comments is that they were unsolicited and part of online polylogues (Kerbrat-Orecchioni, 2004; Marcoccia, 2004), that is, multi-participant, computer-mediated interactions taking place online and thus in a “deindividuated setting” (Bou-Franch & Blitvich, 2014) where individual characteristics give way to collective, social identities which are often built in opposition or misalignment to other (minority, or more vulnerable) identities. These are breeding grounds to conflict and polarisation (Blitvich, 2010) emphasised by the relative anonymity, lack of physical presence and consequent “deinviduation” of computer-mediated communication (CMC)—the more individual identities are effaced, the more shared notions of a collective “Self ” based on in-group perceived features gain prominence in opposition to outer-­ groups perceived as the “Other” (Kopytowska et al., 2017). To adequately render the rich, fine-grained discursive representations of women in online polylogues, a suitable analytical framework to capture this complexity was in order. Due to its intersubjective nature, the notion of “stance” was our point of departure as it fits the relational nature of a corpus where participants position themselves towards social actors (women, in this case), thus providing a useful lead-in to the examination of evaluative language. Indeed, “[o]ne of the most important things we do with words is to take a stance. Stance has the power to assign value to objects of interest, to position social actors with respect to those objects, to calibrate alignment between stance takers (…)” (Du Bois, 2007: 139). Underlying this notion is thus an intersubjective view of language which underpins its “communicative functionality (…) fundamentally dialogic or interactive” (White, 2003: 260). Meaning is negotiated by social actors and bears not only a subjective dimension in the sense that it comes from the speaker’s knowledge or beliefs but also an intersubjective facet resulting from engagement with other subjects. In view of this, our goal was to identify the discursive stance markers and devices used in the Portuguese NETLANG Corpus by which participants align or misalign themselves towards public figure women. In order to do that, we resort to aspects of three main research strands we have found particularly useful: Martin and White’s (2005) analysis of evaluative language (Appraisal), Van Leeuwen’s (1996, 2008) theory of representation of SAs and the aforementioned Culpeper’s (2011) and Culpeper et  al.’s (2017) notion of impoliteness and respective analysis of impolite language.

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The Appraisal framework deals with stance-taking as it describes evaluative language, that is, how speakers take a stance by resorting to “linguistic mechanisms for the sharing of emotions, tastes and normative assessments” (Martin & White, 2005: 1). Negotiating meaning by (mis)alignment with other subjects is performed by two primary modes of evaluative positioning, Attitude and Engagement, both explicated at length in Martin and White (2005). Attitudinal meanings derive from “ways of feeling” and are concerned with positive and negative assessments that speakers/writers perform towards others. Affect evaluates others based on emotions; Judgement is concerned with an ethical dimension whereby human behaviour is negatively or positively assessed, and Appreciation deals with an aesthetic appraisal evaluating texts or natural phenomena. From the three facets of attitudinal meaning, we focus on Appreciation and mostly on Judgement. According to Martin and White (2005), Appreciation is an aesthetical evaluation which, unlike Judgement, is not a normative assessment of behaviour, but rather of aesthetic appreciation of properties. Attitudinal meanings of Appreciation are relevant insofar as women are often evaluated based on the appraisal of physical properties such as “ugly” or “beautiful” (for example, media coverage of women politicians tends to attribute an inordinate amount of attention to appearance—Fernandez-Garcia, 2016; Kahn, 1994; Rohrbach et al., 2020; Van Der Pas & Aaldering, 2020). Judgement is a relevant category of evaluation because our aim is to understand how public figure women are appraised by participants in online polylogues, and such appraisal usually comes with a strong “moral evaluation of behaviour” (Martin, 2000: 145), or Judgement. Judgement is cleft into Social Esteem, comprising normality, capacity and tenacity, and Sanction, which includes appraisals of veracity (how honest?) and propriety (how ethical?). Since “Social Esteem involves admiration and criticism, whereas Social Sanction involves praise and condemnation” (Oteíza, 2017: 462), and both dimensions are important to analyse hateful language towards women, we focus on linguistic realisations of both Social Esteem and Social Sanction in our corpus. The other mode of evaluative positioning as reflected in language is the representation of SAs in discourse. Van Leeuwen’s (1996, 2008) theory of

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representation of social actors is paramount here as it examines how the latter are represented in English discourse—our challenge is to understand how the SAs of relevance to this study are represented in Portuguese. The most important categories of representation deployed in this study are subsumed under the Nomination and Categorisation dyad, as summarised in Table 11.1. Under Nomination, which includes first names, surnames and titles/ honorifics, we also consider names with epithetic or qualifying value akin to adjectives which effectively work to qualify an SA—designating Cristina Ferreira by Rainha da Malveira [queen of Malveira], for example. Finally, to gauge the impoliteness or verbal aggression conveyed in the appraisal of women in our corpus, we resort to Culpeper (2010, 2011) and Culpeper et al. (2017), positing conventionalised impoliteness and non-conventionalised, or implicational, impoliteness, as summarised in Table 11.2. Table 11.1  Van Leeuwen’s (1996, 2008) categorisation of social actors Nomination: SAs are represented by unique identity traits Categorisation: SAs are represented based on shared, not individual, identity traits

Names Honorification

Functionalisation: SAs are represented in terms of what they do (their occupation or role). Identification: SAs are represented based on who they are.

• First name (FN) • FN + Surname • Surname. • Titles and ranks. Classification: The SAs are referenced by government, means of the general the class to which they Prime-­ are perceived to Minister… belong. • Age Physical identification: • Gender a form of classifying • Provenance SAs by referencing • Class physical • Race characteristics “which • Ethnicity uniquely identify • Religion them in a given • Sexual context” (Van orientation, Leeuwen, 1996: 57). etc. Nouns, adjectives.

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Table 11.2  Impoliteness framework following Culpeper (2010, 2011) and Culpeper et al. (2017) Conventional impoliteness: it points to the “general idea (…) of co-occurrence regularities between language forms and specific contexts” (Culpeper, 2010: 3237); linguistic expressions bearing more stable, recurrent connections with contexts of impoliteness than others.

Implicational impoliteness: non-conventionalised, which means that the co-occurrence between context and linguistic form falls outside convention and can be completely novel. The verbal attack to face is thus implicated and achieved by logical inference.

• Intensifiers such as taboo and swear words • Insult—Personalised negative assertions: Uma phuta vaidosa que passa a vida perseguir os partidos mais à direita [A vain whore who spends half her life persecuting right-wing parties] • Unpalatable Questions/Pressupositions: Quem esta sra. julga que é? [Who does this lady think she is?] Porque mente Graça Freitas [Why does Graça Freitas lie?] • Pointed Criticisms/Complaints—Essa Catarina é mesmo traidora e tem duas caras. que se deixe de parvalhices (..) é lamentável [This Catarina really is a two-faced traitor. She should stop her silly things, she is not going to vote in favour but she is going to abstain. How unfortunate]. • Negative Expressives—as examples of Negative Expressives, Culpeper (2011) includes curses or ill-wishes. We have adopted a broad understanding of negative expressives to include, in addition to ill-wishes, speech acts which direct the SA onto a different activity from the one they currently perform, given their perceived ineptitude. For example:   i. Constructions marked for deontic modality: A Dra. Graça Freitas já deveria estar a cuidar dos netos, desde há muito [Dr Graça Freitas should have been taking care of her grandchildren for a long time now]; dedica-te ao teatro, de onde nunca deverias ter saído [Devote yourself to theatre, which you should never have left].   ii. Imperative-hortative constructions as realisations of coercive impoliteness (Culpeper et al., 2017)—since coercive impoliteness is about symbolic power, we have included here directive speech acts using the imperative and with enough illocutionary force to make the coercion salient: Joacine Katar Pulgas pra Guiné já [Joacine Katar Fleas to Guinea now]. Form-driven: the “surface form” is marked for impoliteness by means of “triggers” of impoliteness inferences. • innuendo • snide comments Convention-driven: convention is defied by a mismatch between context and behaviour—cases of conventiondriven impoliteness point to interpretations of politeness but are in fact realisations of impoliteness. • sarcasm • teasing • humour Context-driven: “unmarked” insofar as impoliteness is creative and derived from an inferential effort, in the absence of marked triggers or more or less glaring mismatches between context-form/behaviour.

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An important caveat is that conventional impoliteness precludes “the case that any particular linguistic form guarantees an evaluation of impoliteness in all contexts” (Culpeper, 2011: 113). Furthermore, and as previously mentioned, the notion of “coercive impoliteness” and its connection to hate speech is paramount—coercive impoliteness is a function of impolite behaviour tantamount to a coercive action, “an action taken with the intention of imposing harm on another person or forcing compliance. Actors engaged in coercive actions expect that their behaviour will either harm the target or lead to compliance, and they value one of these proximate outcomes” (Tedeschi & Felson, 1994  in Culpeper, 2011: 226). Our unit of analysis was the utterance, meaning each comment was manually coded according to an annotation key encompassing the aforementioned categories, and the same comment could be coded several times depending on the occurrence of these categories.

4 Discussion The following charts display the most relevant annotation categories pertaining to SA representation and Appraisal, divided by participant (for the sake of intelligibility, impoliteness devices will be presented on a separate chart) (Table 11.3): Regarding Catarina Martins, it is her ideological classification that explains the high concentration of Judgements of Propriety and Veracity—she is recurrently represented as an unethical liar because of her political leanings: (1) Mas esta não se olhou ao espelho para ver que anda a mentir com quantos dentes tem com a hipocrisia do “socialismo”? [But hasn’t this one taken a good look in the mirror to see that she’s been lying about that “socialism” hypocrisy?] Judgement should be seen in articulation with Nomination strategies, which reinforce the unethical ideological character of this SA:

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Fig. 11.1  Corpus annotation following representation of social actors and Appraisal (Judgement and Appreciation) divided by social actor—in percentages

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Table 11.3  Key to Fig. 11.1

(2) E não é que esta berloquista de esterco continua com a parvoice de ser “presidentA”? [And this “berloquista” made of poo insists on the silliness of being “president”?] In (2), the nomination “berloquista de esterco” is an adulterated reference to the Bloco de Esquerda party, led by Catarina Martins, “esterco” meaning “poo” and “berloquista” a derogatory noun achieved by suffixation (noun berloque, in itself a pun with the word “Bloco”, to which the suffix –ista is added). This highlights the importance of suffixation as a stance-marker of opposition, recurrent in the corpus. The use of the demonstrative determiner esta [this (one)] in (1) is also of relevance; in fact, the distribution of demonstratives in the whole corpus points to these pronouns acting as markers of aggressive discursive representation, thus bearing a pragmatic meaning more than a deictic one (see Aguiar and Barbosa in this volume for an analysis of demonstrative pronouns and their aggressive potential in the representation of women). In example (2), the second p. singular, feminine demonstrative esta [this one] as

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head of the nominal group followed by an impolite Nomination, is particularly illustrative. Judgement remains a crucial category in the representation of Cristina Ferreira, consistently sanctioned for lack of propriety based on her perceived social class. References to her provenance (the town of Malveira, about 30 km outside Lisbon) are recurrent (example 3 below), as well as the social group to which she is perceived to belong, saloios (loosely translated, “hill-billies, in example 4): (3) A Rainha da Malveira, Imperatriz de Portugal. É a nossa grande “greta”!!! Coitadinha ... Um desperdício de talento ... [The Queen of Malveira, Empress of Portugal. She’s our big “crack”!!! Poor thing... A waste of talent...] (4) Uma saloia admirada pela ralé. Armada em fina mas sem maneiras nem educação. Uma “nova rica” de trato rude e com “educação fingida” [A hill-billy admired by the rabble, who thinks she’s posh but has no manners or upbringing. A nouveau-riche of rude manners and with “fake education/manners/upbringing”]. Judgments of Propriety should again be seen in articulation with Nomination strategies, which are significant in the representation of Cristina Ferreira—besides Rainha da Malveira, example (3) displays a sexual innuendo by calling her greta (“crack”) and further variations of “hill-billy” associated to social class (labrega, for instance) are found in the corpus. Variations of her first name are also used to mock her social origins—Tina da Malveira [Tina from Malveira], for example. The discursive representation of Graça Freitas hinges on her classification based on age and what is perceived as a consequent lack of discernment, thus explaining why Judgements of Capacity fall mostly on her: (5) Este fóssil da DGS devia estar num lar da terceira idade. Graças à incompetência da criatura, morreram mais de 18.000 portugueses com covid-19 e morreram muitos mais por falta de assistência médica. [This DGS fossil should be in an old people’s home. Thanks to the incompetence of this creature, more than 18.000 Portuguese have died of covid-19 and many more due to lack of medical assistance.]

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Judgements of Capacity to mark an oppositional stance against Graça Freitas are again to be coupled with Nomination strategies—her first name is sometimes used to form puns such as Desgraça [disgrace] Freitas, coupled with epithets such as “senile old woman” or Matrafona da DGS [DGS Matrafona], matrafona being a bizarre-looking man dressed as a woman during Carnival. Utterance autonomisation, that is, representing SAs by means of their utterances (what they say) is also a relevant strategy to produce Judgements of Capacity. Some examples are: (6) a. Esta velha senil só arrota baboseiras [This senile old woman can only burp silly/idiotic things] b. Aquelas homilias diárias e enfadonhas que debitava todos os dias [Those daily boring homilies she would spit out every day] (“Homily” refers to Graça Freita’s broadcasts during the pandemic).

The case of Joacine Katar Moreira seems to be one of overdetermination, whereby SAs “are represented as participating, at the same time, in more than one social practice” (Van Leeuwen, 1996: 61), hence the numerous relevant representation categories used to mark oppositional stances. Nomination includes a vast array of nouns, ranging from First Name + Surname, Surname, puns such as Vayte Katar (a blend of her surname with the expression vai-te catar, loosely translated as [get lost]) or Lady Gaga (in Portuguese, gaga is a noun or adjective to refer to someone who stutters) to, most importantly, intensifiers of aggressive representation, namely alliteration: (7) a. a gaja não gaga nada [the broad stutters nothing] b. macaca de caca [monkey NOUN, feminine made of poo]

The categories of ethnicitiy and nationality, as well as physical characteristics, are particularly relevant in the case of Katar Moreira due to her skin colour, her stutter and the fact that she was born in Guinea-Bissau. These three elements produce a hateful representation conducive to otherisation and exclusion:

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(8) Esta parasita estrangeira é parva ou faz-se? O que é que Portugal tem a ver com as confusões em que a pretalhada se mete continuamente? [Is this foreign parasite stupid or what? What does Portugal have to do with the mess that black people permanently get themselves into?] The offensive term pretalhada [black people, based on the offensive lemma preto/a to which the suffix –ada is added] is achieved by suffixation and signals the belonging of Joacine Katar Moreira to a group differing from “Portugal”, or from the Portuguese. Further, it sets a border between “white Portuguese” and “black foreigners”, aggressively represented as pretalhada. It is this attitudinal meaning of exclusion which explains why Joacine Katar Moreira is more objectified and de-­humanised than the other SAs in the corpus: (9) racista e xenófobo seria mandar a macaca NOUN sing. fem. para um lado diferente de onde ela pertence, temos que ser justos, lugar de macacos com cheiro a catinga é na selva [What would be racist and xenophobic would be to send the monkey to a different place from where she belongs, we have to be fair, monkeys “with a stink” belong in the jungle] In its very aggravated face attack, which otherises Katar Moreira by means of de-humanising imagery (Musolff, 2015), this comment fully illustrates the complexity of gendered discursive representation, which never “comes alone” and intersects with other aspects, such as full-fledged racism, in this case. Although a discussion on the intersectionality of gender falls outside the scope of this article, it is nevertheless noteworthy that the analysis of the data in the corpus shows how gender cannot be dissociated from other factors at work in the discursive representation of women, although it remains the axis of such hateful language. Here, it is gender coupled with racism which contributes to the full impact of otherisation discourses. The directive meaning of the comment, effectively sending Katar Moreira to the jungle, is also in line with the coercive nature of heightened aggressive language tantamount to hate speech. Unlike previous SAs, functionalisation is of note in the representation of Katar Moreira, usually contributing to mark an antagonistic stance:

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(10) Com o putedo que existe na AR, não me admira nada que esta gaja tenha usado a bandeira da Guiné-Bissau [What with the whore-­ people in Parliament, I’m not surprised that this broad wore the flag of Guinea-Bissau] Putedo is a compound achieved by means of suffixation (adding the suffix –edo to the noun puta [whore]) and is used here to represent female MPs, such as Katar Moreira. Judgements of Propriety are also important to mark oppositional stances against Katar Moreira, the most common linguistic realisations of which are nouns or adjectives: (11) Esta senhora, que se diz uma lutadora e defensora dos fracos e oprimidos, não é mais que uma racista mal formada e oportunista [This lady, who claims to be a defender of the weak and the oppressed, is nothing more than an opportunistic racist of ill-character] In example (11), the adjective mal formada literally means “misshapen”, pointing to a semantic content more connected to Appreciation than Judgement. Most of the time, however, it is used in its metaphorical meaning of a character that has been “badly formed”, “distorted”, thus describing someone of ill-character and, in this case, inscribing a Judgement of Social Sanction—Propriety. This is a relevant example as it also underpins the feeble moral character attributed to Katar Moreira. Appreciation, that is, the evaluation of SAs based on aesthetic appraisal, did not have much quantitative significance, but it is relevant in the representation of Marisa Matias. This should be seen in articulation with de-humanising metaphors (12a) and with somatisation, that is, verbally representing a part of her body as a metonymic reference to the person herself (12b): (12) a. Esta vaca cada vez está mais gorda. [This cow is getting fatter and fatter by the day.] b. Felizmente teve 2 grandes air bags a amortecer a queda. [Luckily, she’s had 2 big airbags to break the fall—in response to an article reporting Marisa Matias had fallen and broken her ribs.]

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Fig. 11.2  Corpus annotation for Impoliteness divided by social actor—in percentages

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Table 11.4  Key to Fig. 11.2 Conventional impoliteness

• Insult—Personalised negative assertions: Insult Neg. Assert. • Unpalatable Questions/Pressupositions: Q/Pressup. • Pointed criticisms/complaints—Critic./Compl. • Negative Expressives—Neg. Expr.

Implicational impoliteness—Impl. Impol.

Figure 11.2 now lays out the recurrence of the most relevant impoliteness devices in the corpus for each SA (Table 11.4): As expected, insults permeate the whole corpus and correlate with the previous examination of evaluative language representing each of the SAs. For example, conventionally impolite negative assertions directed at Cristina Ferreira make a point of associating her social class to sexual improprieties: (13) Essa histérica depravada só pode comer tora da grossa e de preferência mais do que uma ao mesmo tempo. É o resultado que dá, tentarem fazer uma pseudo/rainha duma galdéria. [The only thing this depraved hysteric can do is eat thick cock and preferably more than one at the same time. That’s what happens when you try to make a pseudo/ queen out of a slut.] Insults are followed closely by implicational impoliteness. This might have to do, on the one hand, with a cultural facet of Portuguese (in its European variety, at least) tending to prioritise linguistic indirectness for an array of im/polite pragmatic effects, an aspect warranting much more exploration that the one this study can afford. On the other hand, the relevance of implicational impoliteness has to do with what Culpeper (2011) repeatedly explains, which is that impoliteness is always an interactional balance between contextual and semantic properties. Thus, form-driven implicational impoliteness is common in the corpus, whether by allusion to sexual acts and connected euphemistic terms (examples 13, 14a), or to dehumanising metaphors of animals (example 14b; see also examples 9 and 12a. above), marking the surface level of the utterance and triggering an implication of impoliteness of severe face attack:

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(14) a. Essa do ‘cobrir’ o PM tem a sua Graça. Os touros é que cobrem as vacas. E como o PM até gosta de vacas... [That one about “covering” the PM (prime minister) is funny (the Portuguese word for “funny” is “graça”, clearly a wordplay with Graça Freitas’ first name). Bulls cover cows. And as the PM happens to like cows…] b. parece que abriram a jaula para deixarem os macacos virem cá todos defender a sua prima NOUN, fem [It’s as if they’ve opened the cage to let all the monkeys come here and defend their cousin.]

Another relevant general observation is that impolite language targets all SAs equally, that is, both conventionalised impoliteness formulae and implicational impoliteness are used by participants to take an adversarial stance towards the women represented in the corpus. Implicational impoliteness in particular is considerably significant in the corpus and, as examples 14a. and 14b. illustrate, does not point to mild forms of face attack, but to insidious hateful language bearing as much face-­threatening potential as more direct, conventionalised formulae (this runs counter to the Brown and Levinson tenet that the more indirect an FTA is, the more mitigation it achieves). Considering what we have just examined about the representation of SAs, namely the racist discourse targeting Joacine Katar Moreira, it might seem surprising that she is not at the receiving end of the highest number of conventionalised impoliteness formulae such as insults. This is why a qualitative, more than a quantitative, analysis is warranted, so as to underpin the hateful potential that the kind of insults targeting Katar Moreira bear. In fact, these are subsumed not only to negative assertions but also to what we call “graphic insults”, that is, graphic alterations to point to a physical characteristic or to dialectical phonological marks. This impolite formula is applied to Joacine Katar Moreira as a means of otherisation not only because of her stutter (represented in example 15a. by means of spaced-out letters and hyphens) but also because she was born in Guinea-Bissau (the implication being she does not speak Standard Portuguese—example 15b. is an attempt to verbally represent her phonetic articulation):

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(15) a. d-d-d-d-d-d-inheiro gaguejado é m-m-m-m-ultiplicado [stuttered m-m-m-m-m-m-money is m-m-m-m-multiplied money] b. Os minina joacine devi ires para os guiné [Joacine, the little girl, should go to Guinea]

What these insults show is that, unlike the other SAs represented in the corpus, who are mostly attacked based on behavioural, occupational or ideological traits (features that can be changed if needed), Katar Moreira is offended on the basis of her immovable identity traits, that is, on who she is perceived to be (what cannot be changed)—a black, “foreign” woman who speaks with a stutter. Some of the insults targeting Katar Moreira require a degree of inferential effort revelatory of the strong drive to mark an antagonist stance against her: (16) Devia ser circuncidada se ainda não foi!!! Vá para a sua terra impor as suas leis racistas! Porque racista é ela ou tem complexos disso. Helicóptero do caaralho!!! [She should be circumcised if she hasn’t been yet!!! Go home and enforce your racist laws! Because she is either racist or has a complex about it. Fucking helicopter!!!] We now need to take up the notion of indirectness and its pragmatic effects, which have been noted before in this study but warrant further discussion. Part of the aggressive face attack of example (16) lies in the deciphering of the impolite potential of “helicopter”, an apparently harmless noun used here to target Joacine Katar Moreira’s stutter (the implication is that the sound of a helicopter is similar to the sound she makes when she speaks), and rendering it an example of implicational impoliteness. Contrary to Brown and Levinson’s predictions, indirectness is not a necessary index of politeness and can in fact work in opposite ways to signal impoliteness—as Haugh (2015: 16) rightly clarifies, “one of the reasons that indirectness is not always perceived as polite is that it can in fact give rise to a whole range of interpersonal effects, of which politeness is just one”. Impoliteness is of course one of such “interpersonal effects”. In example (16), the inferential effort involved in recognising “helicopter” as an insult exacerbates the pragmatic effect of

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impoliteness because it simply directs the participants’ cognitive efforts towards such interpretation. In doing so, the comment renders impoliteness unavoidable and salient—albeit in a quite insidious manner due to the “plausible deniability” (Haugh, 2015: 20) which accompanies indirecteness but which is precluded in directness. Unpalatable questions and pointed criticism or complaints are relevant impolite devices to represent both Joacine Katar Moreira and Marisa Matias, the linguistic realisation of which is often anchored in possessivation. As Van Leeuwen (1996) explains, possessivation is a form of passivation inasmuch as it discursively represents SAs as being at the receiving end of an activity and therefore devoid of agency. In Portuguese, the removal of agency from SAs is clear when possessive pronouns work as predeterminers of insults, as in following common collocation frame of possessive determiner + adjective/epithetic noun for unpalatable questions: ouviste, sua (third p. sing. possessive pronoun) porca, feia e má?! [did you hear me, you swine, ugly, mean person?]. Demonstrative determiner-based collocations (determiner esta + impolite noun/adjective) are again relevant linguistic strategies to direct pointed criticisms (whilst also inscribing a Judgement of Propriety), this time at Marisa Matias: (17) Esta demagoga e mentirosa acusa quem se opõe politicamente como sendo os culpados. Que nojo de baixa política e de carácter. [This demagogue, this liar accuses those who politically oppose her as the culprits. Disgusting low politics and character.] Negative Expressives (especially in the form of imperative-hortative constructions; see Table  11.2) are also relevant impolite devices in the corpus, and are of particular note when applied to Joacine Katar Moreira, who is the target of these acts of impoliteness more than any other SA. Example (16) is already a clear example of how a Negative Expressive is akin to coercive impoliteness and can include allusions to physical harm. Again, the coercive nature of misogynistic language tantamount to hate speech is demonstrated here. The fact that Negative Expressives, directive and coercive in nature, are used to engage with Katar Moreira more than with other SAs merely reinforces a conclusion we have attained

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before concerning the intersection of gender with other social facets— Katar Moreira’s gender is perceived as being intertwined with her provenance and race in order to form discourses of exclusion conducive to de-humanisation and otherisation. This is encapsulated in the following comment: (18) Esta preta qualquer dia aparece pendurada pelo pescoço na Ponte António de Oliveira Salazar!!! [One of these days, this “black woman” is going to be found hanging from her neck at the António de Oliveira Salazar bridge!!!] (Preta is an offensive term to designate “black woman”.) Example (18) is another example of indirecteness insofar as it can be interpreted as an indirect threat. This allows for cancelability and deniability, which is probably why the writer opted for indirectness and fully exploits its advantages. The target is evidently Joacine Katar Moreira’s race and gender, and the violence of the attack is rendered clear by the reference to the “António de Oliveira Salazar” bridge. Salazar ruled Portugal between 1932 and 1968, that is, throughout most of the 1933–1974 dictatorship; the bridge that stands in Lisbon uniting the north and south banks of the Tejo River was indeed built during his rule and it is now called the April 25th Bridge in honour of the revolution which brought dictatorship to an end on that day in 1974. This explanation is offered here so that the full pragmatic impact of indirectness can be appreciated—the indirect threat is further compounded by a reference to a state of affairs when Portugal engaged in ongoing colonisation of countries in Africa and other parts of the world. The reference to “António de Oliveira Salazar bridge” therefore does not obfuscate the hateful pragmatic meaning of the comment but it actually serves to reinforce it. Misogynistic hate speech may remain a slippery concept with many grey areas; however, due to its coercive, discriminatory nature targeting and threatening a Portuguese black woman, and meeting all the five factors enunciated by the NETLANG model, examples (16) and (18) qualify.

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5 Conclusion This study set out to examine instances of hateful language used to represent women public figures in the Portuguese newspaper section of the NETLANG Corpus (2018–2022) motivated by gender, with a view to underpin whether these discursive representations were examples of misogynistic language tantamount to hate speech. So as to distinguish the latter from aggressive language, we followed Ermida’s Five-Factor Model of content, target, purpose, agent and channel (see Chap. 2) whilst noting the coercive, directive nature of misogynistic language with the purpose of excluding women from full participation in public life. A qualitative methodological approach was adopted integrating different research strands, namely the guiding notion of stance-taking anchored in the theory of social actors, elements of the Appraisal framework and impoliteness studies, with a minor quantitative dimension offered to reinforce the qualitative analysis. Particular attention was paid to the linguistic realisations of these discursive devices, such as suffixation, possessivation and collocations based on demonstrative determiners. An important conclusion resulting from the data examination was the intersectionality of gender, meaning that the latter does not stand alone and interacts with several other societal and cultural facets. That is why Judgements of Capacity and Classification based on age were determinants in the representation of Graça Freitas, how oppositional stances towards Marisa Matias and Catarina Martins were adopted on the basis of their ideological views (the former was also recurrently evaluated on the basis of her appearance), how social class interacted with gender in order to mark antagonistic stances against Cristina Ferreira and how Joacine Katar Moreira, due to her race and provenance, coupled with her gender, bore the brunt of the full impact of misogynistic and racist language. Negative Expressives, which convey the coercive nature of hate speech, were articulated with dehumanising classifications conducive to the consistent otherisation of Katar Moreira in her many discursive representations. However, we feel that the impact that hateful language has on all women because they are women cannot be denied, and thus the

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interaction of gender with other social and cultural localities (social class, race, age, geography, etc.) must be balanced against the fact that “violence and hostility have total impact” (Richardson-Self, 2018: 11). The balance between intersectionality and the “total impact” of misogynistic discourse on all women points to a future research avenue warranting in-depth examination. Finally, Waldron (2012) compares hate speech to “slow poison” which accumulates “here and there” with insidious results, and the same can be said of misogynistic language, especially in the context of CMC, where online environments can make participants oblivious to the social harm caused. This study hopes to have offered a contribution highlighting the fact that misogynistic language severely hinders a truly democratic and egalitarian representation and participation of women in the public sphere.

References Assimakopoulos, S., Baider, F. H., & Millar, S. (2017). Online hate speech in the European Union: A discourse-analytic perspective. Springer. Baider, F. (2020). Pragmatics lost? Overview, synthesis and proposition in defining online hate speech. Pragmatics and Society, 11(2), 196–218. Banks, J. (2010). Regulating hate speech online. International Review of Law, Computers & Technology, 24(3), 233–239. Barker, K., & Jurasz, O. (2019). Online misogyny. A challenge for digital feminism? Journal of International Affairs, 72(2), 95–114. Blitvich, P.  G.-C. (2010). The YouTubification of politics, impoliteness and polarization. In T. Rotimi (Ed.), Handbook of research on discourse behaviour and digital communication: Language structures and social interaction (pp. 540–563). IGI Global. Bou-Franch, P., & Blitvich, P. G.-C. (2014). Gender ideology and social identity processes in online language aggression against women. Journal of Language Aggression and Conflict, 2(2), 226–248. Bousfield, D. (2008). Impoliteness in the struggle for power. In D. Bousfield & M.  A. Locher (Eds.), Impoliteness in language. Studies on its interplay with power in theory and practice (pp. 127–153). Mouton de Gruyter.

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12 Negotiating Hate and Conflict in Online Comments: Evidence from the NETLANG Corpus Jan Chovanec

1 Introduction Much of interpersonal interaction between individuals, both in face-to-­ face situations and in displaced contexts such as online and social media communication, involves conflict and the articulation of differing opinions. The structural patterns of arguments and disputes have been recognised in early conversation analysis (Grimshaw, 1990) as well as in sociolinguistics and discourse analysis (Kakavá, 2001). Conflict has many dimensions: structural properties, the communicative strategies of conducting conflict, conflict negotiation and resolution, and the meaning of conflict. While the notion of conflict has been typically treated by linguists in relation to impoliteness or aggression (Culpeper, 2011; cf. Higgins & Smith, 2016 for “belligerence” and questioning styles in TV interviews), the notion of “conflict talk” appears to be coming back on

J. Chovanec (*) Department of English and American Studies, Faculty of Arts, Masaryk University, Brno, Czech Republic e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_12

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the stage as a useful umbrella term covering diverse discursive phenomena (Garcés-Conejos Blitvich, 2018; Evans et al., 2019; Filardo-­Llamas et al., 2022). The chapter addresses the issue of how conflict is negotiated within the architecture of online news forums, adopting a combined socio-critical perspective on anti-social discourse (NETLANG). Drawing on data from the NETLANG corpus (the section on “body shaming”, “physical identity/features/impairments”), it maps three crucial dimensions of conflict talk, namely “structure”—“linguistic realisation”—”meaning” (cf. Kakavá, 2001). The chapter extends the meaning of conflict by considering its sociolinguistic indexicality, that is, how it is related to identity construction, particularly status negotiation and assertion (GarcésConejos Blitvich, 2018). The chapter distinguishes conflicting representations (which tend to be “idea-oriented”: nominations, predications, argumentation, perspectivisation, framing, positioning and intensification/mitigation; cf. Reisigl & Wodak, 2001: 93–95) and aggressiveness, that is, antagonistic interpersonal verbal acts (e.g.  swearwords, namecalling, and uncooperative communicative practices, such as trolling). It is suggested that the latter, as instances of person-oriented communicative acts, have a “prosodic” function, essentially serving the purpose of intensifying and enhancing one’s claim. The findings indicate that conflict has identity-constructing and identity-­affirming functions. Conflict talk solidifies the ingroup’s position/unity (e.g. as a virtual, imagined community). Addressive conflict talk leads to heated debates among the commenters, while non-addressive conflict talk, which is directed outside one’s social bubble (i.e. the absent other), contributes towards forging a mutual harmony within the ingroup, though possibly also stirring further controversy and addressive conflict talk. It appears that some of the heated, conflict-based debates actually serve a ritual function, attesting to the commenters’ enjoyment of argumentation. Arguably, such ritualised forms of conventionalised conflict have a phatic function and can be described as sporting “conflictual phaticity”.

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2 From Hate Speech to Body Shaming and Conflict Talk Since this chapter deals predominantly with conflict talk within the context of the NETLANG project on hate speech, the present section sets out to define the central notions relevant to the study, namely hate speech as the main conceptual point of reference (Sect. 2.1), body shaming as a specific content area for anti-social discourse (Sect. 2.2), and conflict talk as a more general umbrella category that approaches the notion of verbal hate and aggression from an alternative, interactional perspective (Sect. 2.3).

2.1 Hate Speech Hate speech is an elusive phenomenon that takes diverse forms in diverse situations and is subject to much metapragmatic debate as far as its characteristics are concerned. For instance, the current hate speech policy of Facebook (applicable in October 2022) defines hate speech as “a direct attack against people—rather than concepts or institutions—on the basis of what we call protected characteristics: race, ethnicity, national origin, disability, religious affiliation, caste, sexual orientation, sex, gender identity and serious disease”. The concept of an “attack” is defined as “violent or dehumanising speech, harmful stereotypes, statements of inferiority, expressions of contempt, disgust or dismissal, cursing and calls for exclusion or segregation”.1 Clear as the definition may seem at first sight, issues arise in relation to assessing whether a given utterance is hateful in its communicative context, as attested by Facebook’s own explanatory notes on that issue, as well as what happens in cases of a less “direct attack” because hate speech is very often veiled, implicit and covert (Baider & Constantinou, 2020). Although hate speech is characterised by heterogeneity and multiplicity, with various institutions (legal, workplace) and online platforms  Source: transparency.fb.com/cs-cz/policies/community-standards/hate-speech. Last accessed on 17 October 2022. 1

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typically offering different definitions in different countries, such attempts at providing definitions “are an important part of the normative social actions surrounding hate speech” (Millar, 2019: 147), thereby serving as indicators of the larger moral order (Cameron, 2004: 313). While some definitions seek to establish the existence of hate speech as inherent in certain linguistic forms, others place emphasis on the effect caused to the target (e.g. the causing of emotional distress), that is, rely on the target’s subjective introspection. However, hate speech is perhaps more appropriately conceptualised in more sociopragmatic terms as a social practice that is crucially dependent on context. That is, it involves the two pragmatic aspects of any communicative speech act, namely not only the effect of a given utterance on the recipient of the message but also the intention of the speaker (Millar, 2019). Evidently, such an approach is not without its problems either: the labelling of a given act as an instance of hate speech becomes complicated especially where communicators use implicit and covert strategies or, when held accountable for their utterances, deny hateful intentions (Chovanec, 2013, 2021a). At the same time, we can hardly limit hate speech to only when the target feels offended. As Millar (2019: 151) notes, “the perlocutionary effect of being ‘hurt’ is not necessarily enough to be hate speech” because of the problematic issue of speaker intent and its evaluation. When assessing the problematic nature of one and the same utterance (i.e. a linguistic form) occurring in different contexts (e.g. as a joke in an email to a friend, or on one’s public social media page), Millar suggests we can postulate “a hierarchy of intention in that joking about a group is deemed less unacceptable than to directly hurt a member of a group” (151). Such a view makes the identification of hate speech a rather problematic task, let alone even its automatic detection based on AI algorithms. When faced with offensive content that is reported to them by users, various stakeholders, ranging from platform moderators to judicial institutions, are faced with problems locating the meaning—is it “in the text itself, in the assumed intentions of the communicator, in the interpretations of recipients or in a combination of all three?” (Millar, 2019: 160). In this connection, we need to agree with Baider (2020), who urges that since any communicative act is “embedded in idiosyncratic socio-­ cultural norms”, hate speech exists as a contextualised social process that

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forms a continuum rather than a clearly defined and tangible concept. As noted by Millar (2019: 151), hate speech feeds into a range of other concepts: stereotypes ➔ prejudice ➔ racism ➔ discrimination ➔ hate speech ➔ hate crime.2 For analytical purposes, a common problem consists in differentiating between hate speech and offensive language, particularly as far as automatic detection is concerned (Davidson et al., 2017). Within the NETLANG project, Ermida (Chap. 2) distinguishes between hate speech and what she terms “aggressive language” under the overarching concept of anti-social discourse (ASD). Within her model, the analytical framework operates with five central dimensions to provide an analytical tool to identify one form of SID as opposed to the other. The five dimensions are: content, purpose, target, agent and channel. Thus, for hate speech, these dimensions include the following: (1) prejudice about others; (2) intentional dissemination of representations and meanings about others that are discriminatory, demeaning, disparaging (ingroup hegemony); (3) target as a group with social, economic and so on symbolic disadvantage; (4) agent willingly reproduces or replicates hate speech, and tends to identify with a group; and (5) the message is publically transmitted. By contrast, aggressive language differs in (1) its content, in that it is not about prejudice but disagreement, rejection, protest, resentment, frustration, fury, envy, dislike and so on; (2) its purpose, which can be non-intentional (expressive), discursive or identity related, that is, not intentionally disseminated in order to cause harm to a target; (3) it is directed at single individual’s idiosyncrasies and not a group or a member of such a group as its representative; (4) it is produced by anybody regardless of social identity; and (5) it may be transmitted via private channels, including face-to-face communication. While those characteristics provide some general guidance and help in differentiating between the two kinds of anti-social discourse, they are not without their own problems. Thus, for instance, the model could engage the macro-structural architecture provided by the broad sociocultural context (i.e. to go beyond the situational context of a given  Millar takes the linear model over from The Council of Europe’s publication Bookmarks: A manual for combatting hate speech online through human rights education (p. 168). 2

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utterance, which involves the developing pattern of an ongoing interaction in online communication). Although there is ample research on how hate speech is currently defined differently in different legal and political systems (Millar, 2019; Baider, 2020), much less is known about how hate speech exists, potentially, as a chronotope (Blommaert, 2015), that is, what variable spatio-temporal representations it has, as well as what social positionings it indexes (Blommaert & de Fina, 2015) in different places and at different times. While such a sociolinguistic perspective might add a very welcome and promising dimension which could, for instance, explain many cases of hate speech-related misunderstandings (due to social agents’ shifts of scales—see Blommaert, 2015, changes/movements in local/temporal contexts, viral spread and chains of recontextualisation across diverse media, etc.; cf. Chovanec, 2017, 2021a, b), such a theoretical elaboration of the model is beyond the scope of the present chapter.

2.2 Body Shaming Body shaming is one of the categories of anti-social discourse coded in the NETLANG corpus (see Sect. 3), where it is classified as a kind of prejudice. In the literature, body shaming is defined as an action “in which a person expresses unsolicited, mostly negative opinions or comments about the target’s body” (Schlüter et al., 2021: 3). It comes in various formats such as appearance teasing, cyberbullying and trolling and includes some more specific subtypes, such as fat shaming, skinny shaming, pretty shaming, body hair shaming and food shaming. While the phenomenon of body shaming is relatively common across various online media, its classification as “hate speech” is not unequivocal. It is certainly not one of the prototypical realisations of hate speech; body shaming can, for instance, be constituted by various kinds of remarks that cause anxiety to their targets. It could however be classified as a peripheral kind of hate speech, which is on the borderline between hate speech and aggressive language. Also, there is some evidence to show that people tend to classify different forms of prejudice as different phenomena on the hate speech continuum, being more inclined to identify as hate speech those instances of talk that target ethnic groups and sexual minorities. As Davidson et al.

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(2017: 515) note, “[o]ur classifications of hate speech tend to reflect our own subjective biases. People identify racist and homo-phobic slurs as hateful but tend to see sexist language as merely offensive”. The peripheral classification of body shaming among hate speech phenomena has several implications. First of all, the content area of body shaming does not fall within the “protected interests” that some definitions of hate speech cover, unless the offensive acts are related to disability or serious disease. But even then, the target of such ASD is often an individual, rather than a group with “social, economic or symbolic disadvantage”. Arguably, however, the offending agent can indeed target a group, or a specific individual as a member of such a group, particularly where the individual is subject to a stereotypical, prejudicial or discriminatory treatment. Also, acts of body shaming very often intersect with other dimensions of protected interests, such as ethnicity, gender and sex; and as such, they do qualify as hate speech, even if only in a “collateral” way. Body shaming typically targets women; thus, it has a group-related basis because the offending practice can simultaneously become an articulation of sexist misogyny. One of the most acute cases of hate speech that is based on sexism and misogyny but often has a body shaming component is linked to toxic discourses of hegemonic masculinity and incel rhetoric (Dynel, 2020; Praźmo, 2022), whereby men “direct hate towards women for not allowing them sexual access or for providing them with companionship” (Menzie, 2022: 73). The sexualisation of women, related to physical characteristics and body politics, is present not only in the types of femininity represented by various stereotypes revolving around physical characteristics, for example the “Becky” and “Stacy” stereotypes (Menzie, 2022: 75), but also in highly prominent representations of gendered social actors, where women tend to be judged as immoral and dishonest and being preoccupied with physical attractiveness (Heritage & Koller, 2020). The networked hate in this manosphere is directed towards women and alpha males (Chads), and occasionally spills over into actual violence (Lindsay, 2022). Interestingly enough, the body shaming component in incel discourse is not only other-targeted (and thus constituting ASD and potentially hate speech) but also self-targeted (and thus constituting an act of self-humiliation and self-deprecation). As Praźmo

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(2020: 17) notes, incels “consider themselves extremely ugly (‘abominable’, ‘deformed’ and ‘subhuman’ in their own words) and for this reason incapable of attracting any women. It makes them frustrated and resentful and, as a consequence, leads to the fostering of misogynist, toxic views”.

2.3 Conflict Talk Since the aim of the present study is to see how the prejudicial category of body shaming is negotiated in user comments, my focus is less on the structural and micro-linguistic properties of what constitutes this particular form of prejudice, and potentially hate speech, than on the interpersonal aspects of how such a topic is handled in digital conversations between commenters. The perspective adopted here is to approach such debates as a form of “conflict talk” (Kakavá, 2001). As mentioned  in the introduction,  the notion of conflict has been treated in connection with other  analytical  concepts, e.g.,  impoliteness or aggression  in linguistic pragmatics (Culpeper, 2011) and disagreements or belligerence in communication studies dealing with media interviews (Clayman, 2002; Higgins & Smith, 2016). Recently, the notion of “conflict talk” appears to be re-emerging as an umbrella term covering diverse discursive phenomena (Garcés-­ Conejos Blitvich, 2018). That is hardly surprising:  conflict—and the resulting articulation of differing opinions—is involved in much of interpersonal interaction between individuals, both in face-to-face situations and in displaced contexts such as online and social media communication. The structural patterns of arguments and disputes have been recognised in early conversation analysis (Grimshaw, 1990) as well as in sociolinguistics and discourse analysis (Kakavá, 2001). According to Kakavá (2001), conflict has many dimensions: (i) structural properties, (ii) the communicative strategies of conducting conflict, (iii) conflict negotiation and resolution, and (iv) the meaning of conflict. The first of these, structural properties of conflict, have been analysed in relation to conflict episodes in conversation, particularly disagreements and disputes, and such features as overlaps and preference organisation have been identified as

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some of the structural manifestations of conflict (Greatbatch, 1992; Kotthoff, 1993). The second dimension, i.e.,  strategies for conducting conflict, include the specific linguistic means and styles that various participants draw on when engaged in conflict talk. It is in the third dimension, namely conflict negotiation and resolution, that the interpersonal aspect is most clearly present: it is found in a range of the participants’ actions from compromise to withdrawal and stand-off, sometimes appearing as specific cultural dispute routines that call for successful performance (Kakavá, 2001: 658). Finally, the meaning of conflict is similarly culture specific, with some communities engaging in ritual insults or expressing ritualistic oppositional stances in differently direct or explicit ways. Importantly, conflict need not be seen as only negative: it is also a way of both enacting cultural and social conventions and enhancing the bonding between the participants. For example, Schiffrin (1984, cited in Kakavá, 2001: 660) observes, when describing the practices of conflict management among East European Jews, that disagreement “is not an action that threatens social interaction, but instead is a form of sociability”. The above-outlined approach to conflict talk that maps it on the key dimensions of “structure—language—interaction—meaning” provides a comprehensive way of accounting for how conflict is (a) managed within specific genres, (b) performed by means of distinct language and forms of (routinised) behaviour, and (c) interpreted within not only the immediate interactional context but also the broader sociocultural conventions regulating verbal behaviour in a particular community. Evidently, this undertaking is multidisciplinary, drawing on conversation analysis, discourse analysis, sociolinguistics as well as pragmatics, since conflict forms an inseparable part of face concerns and the study of politeness (Sifianou, 2019). Within a broad definition of conflict, there are several parameters that are central to describing the phenomenon systematically, though not necessarily in linguistic terms. Jeffries and O’Driscoll (2019: 2–6) identify four such parameters, namely participation (i.e. who is involved), means (i.e. how it is expressed), object (i.e. what is involved) and spatio-­ temporality (i.e. where and when it occurs). However, where conflict talk involves hate speech and such socially inappropriate discourse as body shaming, a useful interdisciplinary perspective can also be offered by a more critical approach to the study of

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discourse, which has developed a consistent mechanism for identifying, describing and explaining discriminatory discursive practices. To this end, I draw on the discourse-historical approach to critical discourse analysis, which systematically tracks how participants in discourse make micro-level linguistic choices to encode the preferred meanings by means of nominations, predications, argumentation strategies, perspectivisation and intensification/mitigation (Reisigl & Wodak, 2001: 93–95). These strategies, which tend to be idea-oriented, can help us to describe conflicting representations underlying mutual disagreement and disputes among discourse participants, and are explored in the broadest sociocultural and historical context in order to reveal the meaning which such representations hold in a given community. On the other hand, there is a range of more person-oriented strategies that need to be taken into account as well, which have a more interpersonal dimension and involve aggressiveness, verbal antagonism (including the use of swearwords) and conflict (de-) escalation through cooperation or lack thereof. It is this model of understanding conflict that underlies the analytical part of this chapter (Sect. 4).

3 Material and Data The data for the analysis is based on the NETLANG corpus (https:// netlang-­corpus.ilch.uminho.pt/), compiled for the purpose of studying the forms and mechanisms of online prejudice and discrimination.3 While the overall project aim is to identify the linguistic properties of texts (on various language levels) and the discursive-pragmatic features that serve for the expression of discrimination in online communicative situations, the corpus consists of annotated language data in English and Portuguese that are based on publically available texts on YouTube and various online newspapers. The data is annotated for the type of prejudice identified in the user comments accompanying such primary media texts,  The project title is “The Language of Cyberbullying: Forms and Mechanisms of Online Prejudice and Discrimination in Annotated Comparable Corpora of Portuguese and English”. The project and the corpus are run by the University of Minho, Portugal, and sponsored by FCT (PTDC/ LLT-LIN/29304/2017). 3

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and is coded for a number of sociolinguistic variables that are deemed potentially relevant. For the purpose of the present study, the NETLANG corpus was searched for data in the section that identifies the anti-social discourse under the labels of “body shaming” and “physical identity/features/ impairments”. A search of the corpus identified 153 news articles that were published in the online versions of two British newspapers (metro. co.uk, express.co.uk) in November 2019, that is, at the time when the compilation of the corpus was begun. A closer inspection of the 153 articles indicated that the individual articles generated a significantly diverse amount of reactions: while a few articles received hundreds of comments, the vast majority had only a few comments in them. A decision was thus taken to narrow the data set to two of the most commented articles, which received 401 and 397 reactions (Nude Remain Protester Challenges Rees-Mogg To Naked Brexit Debate; Three Dead Over 100 Injured After Smoking Fake Weed Laced With Rat Poison In Illinois), and ten of the less commented articles with 9–15 comments, which resulted in a set of little over 900 reader comments.4 Because of the annotation design, there are some limitations to the data. First of all, the data retrieved from the corpus do not deal exclusively with body shaming. A qualitative analysis of the data indicated that some of the material in the retrieved dataset was also categorised under other types of socially inappropriate discourse, such as gender and ethnicity. Indeed, while there seems to be a relatively common pattern for various categories of anti-social discourse to intersect (cf. Faria’s chapter, this volume), the connection between body shaming and gendered discourse is perhaps little surprising, given the Western cultural tradition of the feminine beauty ideal (Martínez-Lirola & Chovanec, 2012) and the normalising ideology guiding the perception of the female body as “an ideal, youthful and unchanging body shape” (Jeffries, 2010: 94). Online hostility towards women appears to involve common references to such characteristics as “unintelligence, hysteria, and ugliness” as well as extreme  A decision was made to exclude the article that was most commented on (2019 Election live, with 514 comments), because its subject matter was political rather than related to body politics. While some body shaming was present in the comments, the vast majority of the comments deal with other issues that body shaming/physical identity. 4

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sexual acts that the abusers often frame as “correctives” (Jane, 2012: 533). The second limitation involves the fact that while the textual ensemble consisting of a given news article with all of its comments may have been characterised during the annotation process as qualifying for the “body shaming” category, not all of the individual comments actually included an instance of body shaming or anti-social discourse targeting physical features and impairments. That fact, however, does not constitute an obstacle to the aim of the present chapter, since what matters is the overall presence of the discursive practice of body shaming in a given online conversation among commenters, where specific socially inappropriate comments typically become dialogically juxtaposed with other, non-offending comments. Understandably, it is such a juxtaposition of inappropriate and non-­ offensive comments that is at the nexus of conflict: a clash of personal views, representations and ideologies.

4 Analysis This section draws on the NETLANG corpus data to provide a qualitative analysis of several selected issues along the idea-oriented conflict talk, and the person-oriented strategies identified above. To this end, the analysis describes how conflicting representations are deployed and negotiated in the users’ comments (Sect. 4.1), how conflict talk is continued without much further development (Sect. 4.2) and how the conflict gradually escalates by means of more person-oriented actions and instances of verbal abuse (Sect. 4.3).

4.1 Conflicting Representations The category of conflicting representations is an umbrella concept for a series of discursive strategies originating in critical discourse analysis (Reisigl & Wodak, 2001; Wodak, 2001; Reisigl, 2017). This summary category subsumes conflicting representations, conflicting nominations, conflicting predications, conflicting argumentation, conflicting

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perspectivisation (framing, positioning) and conflicting intensification/ mitigation. These are the means through which prejudicial discourse is commonly conveyed and disagreements expressed. One of the central issues of how conflict is expressed includes different representations, which are often juxtaposed in order to bring out the mutual incompatibility of the positions held by the conflicting parties. That situation is illustrated in Example 1, which sets up the basic contrast between the category labels of “professor”, “pathetic drip” and “5-year old child”: (1) This pathetic drip is a professor at Cambridge University? I heard her being interviewed on BBC R4 and she sounded like a 5-year old child. The UK is deeper in the mire than I ever thought. (C-1-4 Naked debate)5 The two representations lie at the opposite ends of categorising individuals in terms of their experience and expertise: on the one end of the continuum, there is the rank of a professor of a prestigious UK university (“a professor at Cambridge University”), on the other end there is the role of an inexperienced child (“a 5-year old child”). These contrasting representations juxtapose the official expert identity of the individual referred to in the news article, where the category of “a professor” is a stable one, and the commenter’s vocal disapproval with such a categorisation by means of their ad hoc categorisation through the labels “pathetic drip” and “5-year old child”. The former representation is used in a predication about the professor as a starting point, thereby revealing the commenter’s sceptical questioning of the official designation and role of the individual. The latter representation appears in a simile, that is, the professor is likened to a child on the basis of the commenter’s claimed experience of having heard her TV interview. The commenter’s extreme categorisation, where the professor is denigrated to what could be seen as the polar opposite of such a rank (i.e. “a 5-year old child”), is legitimised by the reference to the commenter’s first-­ hand experience of the media interview. The underlying conflict is  The coding of the examples indicates the order of the comment in the interaction; C-1-4, for instance, designates a fourth comment submitted in response to the first comment on the news article. 5

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between the commenter’s view and the official representation of the media, which interviews a Cambridge University professor in the role of an expert, and possibly between the commenter and the academic world, as long as we infer the comment involves a possible criticism of the academia being represented by what the commenter (as a representative of the public) sees as an inappropriate representative. On a more general level, the entire post could be interpreted as a comment becrying the decline of the UK, as attested by the final sentence, which is given by the commenter as the conclusion from the fact that the professor—who is so inept in the commenter’s view—is given public voice in the media. Since the rank of a professor implies expertise, whenever a conflict emerges, there is a tendency for it to be based on the opposite characteristic, that is, the absence of education/expertise rather than its presence. That is the basis for the conflicting representation not only in the example above, but also in Example 2, where the concluded lack of education is given as grounds for the delegitimisation of the professor’s views: (2) Guts and stupidity are not the same thing. Running around naked achieves what exactly? And her message was what? What exactly was she saying? Only a rather uneducated uni professor that has no argument would stoop so low. But then she is apparently a remoaner. (C-1-9-4) Here, the commenter first frames their contribution in terms of the general category of “stupidity”, and then poses a series of strategic rhetorical questions airing their non-understanding of the professor’s actions, and serving to imply the oppositional view on the matter. This is conflictual argumentation that leads the commenter to the conclusion denying the professor’s elite expertise (“a rather uneducated uni professor”), who becomes delegitimised on account of being incapable to persuade with the strength of sophisticated argument (“that has no argument”) rather than through a naked protest. Importantly, the commenter also makes clear their conflictual perspectivisation by negatively framing the professor in moral terms (“stoop so low”), and thus delegitimising her (cf. Van Leeuwen, 2008). In a rather surprising twist, the comment closes with a statement that seems to seek some understanding or explanation of the professor’s behaviour: the commenter finds the justification (which

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appears to be ironic) in interpreting the professor’s behaviour in relation to her assumed political orientation (“But then she is apparently a remoaner”). The use of the derogatory label identifying those who are opposed to, and regret, the outcome of the 2016 Brexit referendum in the UK, simultaneously reframes the conflict into the sphere of politics and reveals the commenter’s ideology by indicating their opposite position. Interpreting and recasting the behaviour of the social agent mentioned in the news item as being the external manifestation of a particular political opinion—an opinion that is opposite to the commenter (and thus in conflict with the commenter’s views)—is a form of an argumentative shortcut that ultimately creates an implied contrast between “us” and “them” (Molek-Kozakowska & Chovanec, 2017). In this process, the ingroup is understood to be constituted by those characterized by positive values (the normal/educated/moral/politically enlightened and so forth people) as opposed to those characterized by negative values (the bizarre/stupid/immoral/politically wrong and so forth others), cf. Example 3: (3) Keep to the radio luv! Pure exhibitionism! And she’s part of the loony >remoaners—says a lot! (C-2) As regards the micro-level communicative strategies, this example is also noteworthy in terms of how it switches addressivity. The comment opens with an imperative sentence (“Keep to the radio luv!”), which is seemingly addressed to the professor herself, familiarly referred to by means of the colloquial endearment “luv”, with its demeaning and evidently contextually inappropriate connotations. The readers may infer that the implied meaning is a negative assessment of the female professor’s body and, in view of the dominant ideology of ideal female bodies, its non-presentability in public. In that sense, the comment can be read as an act of body shaming, targeting the professor but reaffirming the ideal female body ideology. That is followed by an exclamatory utterance (“Pure exhibitionism!”), which conveys the commenter’s evaluative stance framed in the discourse of morality, with the implication of the abnormality of such behaviour. The switch then occurs in the final utterance,

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where the commenter refers to the professor in the third person, that is, not addressing her personally but talking about her to the other readers, not only aligning her with a particular political orientation (“remoaners”) but also expressing the commenter’s highly negative stance. Importantly, the negativity is articulated not simply against the professor as an individual person but against her as a representative of the entire group of left-wing supporters (“she’s part of the loony”, cf. the allusion to the common collocation “the loony left”, which has been salient in conflictual British political discourse for decades, at least since The Sun’s prejudicial treatment of social unrests and the trade unions in the 1980s, cf. Fowler, 1991: 11). The statement classifies the professor and attributes a negative quality to her in an act of conflictual predication (“loony”— categorising her beyond normalcy). Last but not least, the curt final adage (“remoaners—says a lot!”) is a self-justificatory statement that appeals to the assumed body of knowledge shared by the readers of the same views and political orientation. Its function is to legitimate the commenter’s conclusion and indicate that no further argumentation is needed because—in the commenter’s specific view—“the truth” has been revealed. Examples 2 and 3, thus, illustrate the underlying incompatibility of different political views. The polarisation underlies another sense of conflict (referred to as “axiological conflict” by Kopytowska, 2015), which relates to the incompatibility of values and norms of the ingroup and the outgroup, thus ultimately contributing to the delegitimisation of the other.

4.2 Extended Disagreements All the examples above illustrate monologic comments that are posted by users as their reactions to the news article they have just read. The underlying contrast and conflict is thus directed towards either the social actor involved in the news event or the media/journalist bringing such a news item. However, the technical affordances of the platforms hosting online reader comments, which have enjoyed extensive attention from linguists (e.g. Ensink, 2012, Kopytowska, 2013, Goodman & Rowe, 2014,

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Johansson, 2015) enable mutual reactions from comments, resulting in dialogic exchanges between users. Two distinct types of comments then have to be distinguished: first-level comments that primarily react to the article, express the readers’ views, and trigger the discussion, and second-­ level comments that are posted as reactions to other comments rather than the news text itself (Weizman, 2015; Chovanec, 2018). It is in such interlinked comments that we find the characteristic online discussions, extended disagreements and interpersonal conflict involving mutual verbal aggression. Example 4 illustrates a brief, though complex, online dialogue between users, where a conflict of opinions arises between the commenters: (4-a) The love of money, the root of all evil (C2, Broke men are making it hard for women to marry) (4-b) That’s a cliche, not a fact. (C2-1) (4-c) never said it was. (C2-1-1) (4-d) So basically, women marry for money. And recently discovered, water is wet. (C-3) First of all, a user makes an initial comment on the news article, essentially summarising the gist of the story (Ex. 4-a). However, to do so, the user quotes a well-known line from the Bible, with a slight and insignificant initial modification (omitting the formulaic “For” from the beginning of the biblical quote and the copular verb “be” connecting the two parts).6 While such quotes provide a shortcut to a form of shared wisdom, they often come as stereotypical statements—as generalisations rather than statements of fact. Indeed, that is what the second commenter points out as the problem with the comment (“That’s a cliché, not a fact”, in Ex. 4-b) in their reaction, that is, a second-level comment. By means of such “conflicting mitigation”, the user modifies the illocutionary force of the first commenter’s observation, questioning its epistemic status. This is a sign of a conflicting view: disputing the first commenter’s observation, the second commenter actually expresses a disagreement.  “For the love of money is the root of all evil: which while some coveted after, they have erred from the faith, and pierced themselves through with many sorrows” (1 Timothy 6:10). 6

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Significantly, however, the first commenter posts a reaction to such a reaction (“never said it was”, in Ex. 4-c), contradicting the previous user and countering that the criticism is misdirected and based on an argumentative fallacy. That concludes the brief conflictual interaction. The next comment (in Ex. 4-d) is again a first-level comment addressing the subject matter of the news article, with the user formulating another generalised conclusion (cf. Ex. 4-a). However, this time the summarisation (cf. the utterance launcher “So basically”), which brings the alleged general truth (“women marry for money”), is complemented by the user’s afterthought (“And recently discovered, water is wet”), which reports a banal fact as if it was a new revelation. Such irony can be read as an indirect comment on the news article itself, that is, that it brings self-evident information and is, thus, useless. There is, thus, a conflict between the perceived newsworthiness of the piece of news on the part of the user (as the recipient of the news) and the media (as the producer of the news). Needless to say, such oppositional positioning with respect to the media is relatively common in online first-level reader comments.

4.3 Conflict Escalation The differing views expressed by users in their comments are not merely expressions of their disagreement with each other but may lead to a more open expression of conflict, escalating with each additional comment posted into the online discussion. Example 5 provides one such extended illustration of a developing conflict, where the subject of the discussion is the difference between “natural” and “synthetic” drugs. While the discussion deals with suggestions for legalising the former and banning the latter, it escalates into personal attacks. Yet, it also demonstrates the capability of the participants to self-police the discussion and identify forms of conflictual behaviour that are considered to be beyond the conventionally understood limits of a relevant online discussion: (5-a) Marijuana is the opium of the people, and you wonder why your corporates are going overseas, I’ll tell you because there sick and tired of the performance of marijuana smoking employees, it’s cool man... (C-58)

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(5-b) You’re joking right...??? Have you ever done opium or have you ever used marijuana for any reason? Probably not or you wouldn’t make that comparison!!! What about drinking a beer, glass of wine or a shot of whiskey? That’s all man-made....My God put me in charge of ALL the plants and animals on the Earth...and that includes your “opium”! So stop infringing on my Civil Rights and get a clue!!! (C-58-1) (5-c) I can see a drug addled brain at work here, I just point out a few home truths, infringing you human wright’s, try helping the homeless that will give you a rewarding high. (C-58-1-1) (5-d) “Wright’s” (C-58-2) The initial conflict develops between the first two commenters, where the first-level commenter expresses an anti-drug view, assuming the authoritative position of someone who knows (“I’ll tell you because…”) and positioning the other participants as ignorant (“and you wonder why”) in what has by now become an extensive discussion already (this is the 58th first-level comment in the discussion). The second commenter (in Ex. 5-b) responds by expressing their disbelief about the seriousness of the other person’s claims (“You’re joking right…???”), rhetorically asking about the other’s personal experience (“Have you ever done opium…”) and providing one’s own answer (“That’s all man-made”). In addition to making his factual comment, the user protects his negative face by somewhat militantly appealing for personal freedom to act. The user does this by formulating an unaddressed imperative (“So stop infringing on my Civil Rights”) and adding a final statement (“and get a clue!”) which constitutes a direct attack on the first commenter’s positive face because it brings into focus their ignorance in the matter. Rather than mitigating the underlying conflict and opting, for instance, for indirectness, as might be the preferred and socially more acceptable face-saving strategy in face-to-face conversation, the user thus actually enhances the conflict. In Example 5-c, the first user then comes back and exacerbates the conflict even further. Speaking from their anti-drug perspective, the user represents the opponent in terms of assumed mental decline (“I can see a drug addled brain at work here”) and underlining the inability to take in what they consider evident facts (“I just point out a few

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home truths”). The post continues in a mocking tone, thus shifting the conflict from idea-oriented to person-oriented strategies (“infringing you human wright’s, try helping the homeless that will give you a rewarding high”). It culminates in an aggressive tease concentrated on the notion of “a rewarding high”, suggesting that such a feeling should be arrived at not as a result of taking drugs but by means of some useful work. At this point, the next reaction takes up the anti-drug commenter’s misspelling, briefly echoing it in inverted commas and adding three smiley emojis (‘“Wright’s” ’, in Ex. 5-d). This is an interesting strategy that is commonly used in online discourse: drawing attention to other people’s spelling mistakes is a strategy of delegitimising their opinions. Such attention to metalinguistic detail implicitly represents the person offending against standard spelling or grammar as lacking education, sophistication or intelligence and brings into play language ideologies that are shared within the community (cf. also Sherman & Švelch, 2015; Heuman, 2020). The superiority achieved by spotting, pointing out and mutually appreciating the spelling mistakes of others contributes to the bonding between those “in the know” and disassociation from the offending person, who thus becomes ostracised. The rejection of the anti-drug commenter who was involved in the verbal dispute as a valid participant in the discussion continues in several subsequent posts contributed by other participants. In addition to the delegitimisation of the commenter’s view on account of his metalingual inaptitude, the other participants justify the exclusion of that conflicting opinion on other grounds as well: (6-a) you have no idea what you are talking about. You should educate yourself before you speak. Ignorance obviously is bliss with you! (C-58-13) (6-b) It’s a fake profile. Don’t feed the troll. (C-58-14) Thus, in Example 6-a, the commenter’s uses the strategy of rationalisation (Van Leeuwen, 2008), referencing the anti-drug user’s alleged ignorance, though not giving any rational or explanatory arguments to support the conclusions. Once again, it is a blatant attack on the positive face of the other user, addressed directly to them (“you have no idea…”;

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“You should educate yourself…”). In Example 6-b, the commenter goes even further, suggesting the others ignore the commenter entirely and not engage in any discussion due to the possibility that they intentionally adopt a conflicting perspective in order to provoke others (“Don’t feed the troll”). To accuse another of being a troll is the ultimate way of excluding them from the discussion and thus enforcing the boundaries of the ingroup (cf. Hardaker, 2010: 224), although paradoxically, one strategy of trolling is actually paying over-excessive attention to the incorrectness or non-standardness of language used by others (Hardaker, 2010), that is, person identified as a troll (in Ex. 6-b) was actually mockingly trolled before (in Ex. 5-d).

5 Discussion of Findings and Conclusion As suggested in the theoretical part of the chapter, the relation between the concepts of anti-social discourse, hate speech, aggressive speech and conflict talk is not straightforward and unproblematic. Hate speech and aggressive speech, as subtypes of anti-social discourse, often involve conflict talk, although the conflict need not be explicitly articulated and can be externalised, for instance, by means of racial slurs, swearing and other forms of verbal abuse (cf. Santana, 2014). Conflict talk is not a necessary prerequisite for anti-social discourse, since hate speech may be externalised as unidirectional and self-contained utterances, rather than involving some sort of dialogical (and, thus, conflictual) opposition. However, neither is conflict talk the superordinate concept for hate speech and aggressive speech: not all conflict talk involves such anti-social discourse: it can be expressed by non-aggressive, non-hateful and other non-inappropriate language. In addition, there are more covert forms of conflict, which could be based, for instance, on structural properties of conversations (e.g. preference organisation) and pragmatic inferences (Gricean, politeness, etc.)—even silence can be contextually interpreted as a sign of underlying conflict. Obviously, not only are some of such forms of conflict neither anti-social nor socially inappropriate but they may actually be the preferred—and thus conventionally appropriate— forms of externally realising an underlying conflict. Arguably, there is a

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need for further studies into the intricate conceptual relations between these terms and notions. In the approach advocated in this chapter, the fundamental differences between the set of the core terms can be summed up as follows. Where the definitions of hate speech tend to reflect social structure/ideology and sociopolitical and cultural contexts, hate speech is also a type of anti-­ social—or socially inappropriate—discourse; consequently, both rely on an external definition of normativity. By contrast, aggressive speech— although also a type of anti-social discourse—emphasises the interpersonal conventions underlying verbal interactions, and is thus less amenable to a description in terms of a general social framework dependent on a broader consensus on the issue of normativity. Finally, conflict talk is often interactional and dialogical, and is not inherently anti-social, although it can often be mapped onto anti-social forms of discourse. Also, it is more inclusive than, for example, doing impoliteness (see also Garcés-Conejos Blitvich, 2018: 121). Elaborating on Kakavá’s (2001) discourse analytical and sociolinguistic approach, we can then propose an integrated framework for the analysis of conflict talk—as well as other forms of anti-social discourse including hate speech and aggressive speech. In this general model, several dimensions need to be taken into account, including the following: (1) The structural properties of the genre in which such a verbal encounter is played out, which includes the technical affordances of online platforms, such as the possibility of users to recursively comment on each other’s comments; (2) The communicative strategies and linguistic forms for performing conflict, which become the carriers of anti-social discourse (including hate speech and aggressive speech); this involves swearing, negative labelling, othering and the discursive strategies of conflictual representation, including nomination, predication, argumentation, perspectivisation and intensification/mitigation;

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(3) The interactional dimension that covers conflict negotiation and resolution; which includes the way the participants handle the emerging conflict, for example if they seek to mitigate it and remove potentially conflictual aspects to protect each other’s face, or if they choose to escalate the conflict, which often entails a shift from idea-oriented to person-oriented strategies and from rational argumentation to emotional self-expression; (4) The meaning of conflict, which is to be assessed both from the perspective of (a) the local context (verbal as well as situational, which includes the current as well as habitual patterns of interaction in the online sphere) and the participants’ understanding of the immediate communicative interaction, and (b) the broader sociocultural context that provides the normative framework within which certain forms of conflict presentation, negotiation and resolution are appropriate. It appears that since conflict talk can be used in order to denigrate and ultimately delegitimise an opponent, it can solidify the unity of the ingroup in opposition to a perceived outsider: it thus has an identify-­ forming and identify-affirming function. Addressive forms of conflict lead to direct disagreements and disputes; such heated discussions give rise to “conflictual hatred”, as distinct from those situations where there is conformity of opinion among the participants and the conflict is directed as someone else, with the group exhibiting what might be called “harmony in hatred”. At times, conflict, aggression and hate can be used in teasing in non-serious ways, that is, they need not be intended to create the feeling of being hurt on the part of the recipient, which is particularly so in cases of ritualised conflict that serves a phatic function. All in all, the investigation of the cluster of such concepts as hate speech, aggressive language and anti-social discourse from the point of view of conflict talk calls for more systematic attention, particularly in the context of the interactional architecture of various online means of communication.

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13 Linguistic Markers of Affect and the Gender Dimension in Online Hate Speech Kristina Pahor de Maiti, Jasmin Franza, and Darja Fišer

1 Introduction Over the last few decades, communication technologies have proliferated, thereby influencing how we communicate, which opened new paths for research in many disciplines: social, political, economic, and linguistic (Tarasova, 2016). In this chapter we will focus particularly on the emotive dimension of communication. According to Ochs and Schieffelin (1989), emotions can be expressed via two main channels: non-verbal and verbal. The non-verbal expressions of affect such as facial expressions, gestures, body orientation, voice pitch and similar have been extensively

K. Pahor de Maiti (*) University of Ljubljana, Ljubljana, Slovenia CY Cergy Paris University, Cergy, France e-mail: [email protected] J. Franza • D. Fišer Institute of Contemporary History, Ljubljana, Slovenia e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ermida (ed.), Hate Speech in Social Media, https://doi.org/10.1007/978-3-031-38248-2_13

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researched by social scientists (see Martinez et al., 2016; Van den Stock et  al., 2007), while the verbal dimension, which also draws attention across scientific domains, has been extensively studied especially in linguistics (Bednarek, 2008: 7–12) where it has been approached from various angles, for example systemic-functional (Martin & White, 2005), cognitive (Palmer & Occhi, 1999), psycholinguistic (Burger & Miller, 1999), syntactic (Dirven, 1997) and so on. Our efforts in this chapter join the latter group since we will be focusing on verbally encoded expressions of emotions, moods and feelings for which we employ the term “affective language”, in parallel to the psychological concept of affect (Hogg et al., 2010). The study of affective language is especially important since it provides a tool to explore the producer’s subjective attitude to the topic (Apresyan, 2018). Studies consistently report gender differences regarding emotional expressiveness, and many studies show that women are more emotionally expressive than men (Parkins, 2012; Ubando, 2016). Fox et al. (2007) analysed expressiveness through emphasis, laughing, emoticons, adjectives and specific topics in instant messaging, reaching similar conclusions. In this chapter, we focus on online hate speech which differs from previously studied discourse types due to its two main characteristics. First, it is discourse produced in an online environment which allows more freedom of expression and potentially less stringent societal expectations regarding appropriate gendered behaviour, especially due to a higher degree of anonymity (cf. Veglis & Pomportsis, 2012), which might disinhibit expression of emotions in men who are generally perceived as emotionally restrained and expected to behave accordingly (LaFrance & Banaji, 1992; Charteris-Black & Seale, 2013). Second, hate speech is a type of discourse that is inherently affective—not only was it found to be more expressive of emotions than non-hateful speech (Franza et al., 2022), but by being linked to the sentiment of hate, it is also revealing of discriminatory perceptions of reality by the speaker (Marques, 2022). Differences in the use of affective language by men and women as observed in other genres might therefore not map faithfully to socially unacceptable comments. Hate speech—which we will call socially unacceptable discourse (SUD) from hereon to encompass all and not just legally prosecutable forms of discriminatory, offensive, hateful, violent or threatening

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speech—is increasingly studied from the perspective of emotions because emotive lexis provides a powerful marker that can be used for SUD detection (Al-Saaqa et al., 2018). Less is known, however, about other linguistic features that convey affect in SUD, especially in relation to the gender of the producers. To gain a better understanding of the role of gender and affect in the production of socially unacceptable comments, we observe linguistic markers in English and Slovene Facebook comments at three linguistic levels, namely typographical, grammatical and lexical. We address two research questions. First, we want to find out whether men and women differ in the quantity of production of SUD comments, and second, we want to explore whether women and men differ in their use of affective linguistic features in SUD comments. Following the findings by Costello and Hawdon (2018) and Parkins (2012), we first hypothesise that men will author more SUD comments than women, and second that women will use more linguistic features conveying affect in their SUD comments than men. The chapter is structured as follows. Section 2 provides an overview of gender-related linguistic markers of affectivity. Section 3 focuses on the description of the data by presenting its annotations and characteristics in relation to the gender variable, and delineates the study design. In Sect. 4, we present the results of the analysed affective linguistic features on three levels and offer an interpretation of expressivity within the gender frame. Finally, Sect. 5 concludes the chapter by pointing out limitations and possibilities for future work.

2 Linguistic Features as Markers of Affect Affect, often called expressiveness, is a linguistic category understood as opposed to neutrality which can be found on all language levels and be expressed through many different linguistic elements (Apresyan, 2018). Affective meanings are also often conveyed implicitly (Dueñas, 2010) and the interpretation of affective linguistic features can be highly context dependent to the extent that even standard use of, for example, punctuation or even lack thereof can be perceived as conveying

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socio-emotional content (Androutsopoulos, 2011; Vandergriff, 2013). In general, however, researchers agree that affective involvement can be marked in the use of single words or different lexical and syntactic constructions as well as through typographical symbols, for example through nouns, adjectives, prepositions, metaphors, exclamatives/interjections, intensifying or reducing structures and so on (Truesdale & Pell, 2018; Argaman, 2010). The studies also show that the higher the number of affective features, the higher is the perceived emotional involvement of the sender (Vandergriff, 2013: 8). For this reason, we limit our analysis to explicit markers of affect. We outline below the linguistic features that were found to be the most relevant for the study of affect in related work. On the typographical level, Parkins (2012) found that full word capitalisation, character multiplication and extensive use of punctuation markers are good indicators of affect. In a dataset of Facebook and Twitter posts, she found that, overall, women are more expressive compared to men, although the results also indicated that for some of the prosodic markers (e.g. character multiplication) the differences were very small. Similarly McAndrew and DeJonge (2011), who studied e-mail correspondence, found that a high frequency of expressive punctuation is more likely to be linked to women. Pahor de Maiti et al. (2022) show that female producers of SUD comments differ from men in their use of typographical impoliteness triggers, such as full word capitalisation or emoticons/emoji. The latter are, in fact, a prototypical and most transparent discourse feature of affect (Wolf, 2000; Provine et  al., 2007; Ahn et al., 2011) which has also been frequently used as a signal for sentiment analysis in computer-mediated communication (Wolny, 2016). On the grammatical level, affect is reflected through the linguistic structure. Morphological elements, such as pronouns, determiners, tense/ aspect, affixes, interjections and so on, have been researched by Ochs and Schieffelin (1989) as elements conveying affectivity. Furthermore, affect is also conveyed through the use of comparative and superlative forms of adjectives and adverbs as shown by Biber and Finegan (1989), who list them among other means of expression of attitudes, feelings, judgements or commitments. Rett (2021), for example, looked at several emotive markers (e.g. alas and fortunately) and found that they crucially contribute to the way the speakers construct their utterances in context.

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Moreover, the use of first-person pronouns has been shown to add to the communication of affect as they transmit pathos, involvement and personal view (Račečič, 2012). Likewise, Fuentes et al. (2021) demonstrated with the analysis of co-occurrences of pronouns and emotion-revealing words that the use of first-person versus third-person pronouns suggested emotional disclosure. Schwartz et al. (2013), who analysed a dataset of 15.4 million Facebook messages, also found that first-person singular forms are more typically used by women. In addition, affect is also conveyed through pragmatic means of expression, such as metaphors, rhetorical questions, mock politeness formulae, and so on, which were studied by Pahor de Maiti et al. (2022) as potential triggers of impoliteness and were found to be more present in comments posted by women. They also found that women have a tendency to use overt rather than covert formulation of opinions (e.g. clearly stating that what is said is an opinion and not phrasing the comment as a fact) which suggests that, at the surface level, male commenters express themselves more assertively and directly compared to the less adversarial female commenters. On the lexical level, affect is most naturally investigated using affective lexis which has been receiving a lot of attention for some time now also in computational linguistics (see Mohammad & Turney, 2010; Schuller & Batliner, 2013; Chiril et al., 2022). All content word classes can work as explicit markers of attitudinal meaning, as shown by Dueñas (2010), who found that adjectives were by far the most often used word class in Spanish academic texts to convey evaluations, while attitudinal adverbs appeared only rarely. Moreover, Schwartz et al. (2013) found that swearing and anger-related words proved highly predictable for men, while women use more words denoting emotions (e.g. excited), and psychological and social processes (e.g. love you, 90%).2 While the share of violent comments is low in both datasets (EN: 5%, SI: 17%), there are three times more violent comments in the Slovene dataset. It should be noted that the unbalanced distribution between violent and offensive comments could at least in part be the result of several factors, such as data collection during the biggest migrant crisis and referendum campaign in Slovenia as well as Facebook’s policy to remove reported hate speech which is probably more proactive for comments in English.3  The FRENK corpus contains several categories of SUD (Violent, Threatening, Offensive, Inappropriate). In general terms, the Inappropriate tag was used when no target of SUD could be specified and the Violent, Threatening tag was chosen over Offensive if the comment included a threat or a call or an allusion to physical violence. For a detailed description of the annotation schema, see Ljubešić et al. (2019). In this study, we are not interested in the fine-grained distinctions between the inappropriate and offensive comments on the one hand and the violent and threatening ones on the other, so we merged them into two major categories Offensive and Violent. 3  The information about the number of deleted comments is not available for our dataset. For current data regarding Facebook content moderation, see https://transparency.fb.com/data/ community-­standards-enforcement/hate-speech/facebook/ 2

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In both datasets, men (EN: 3.8%, SI: 11.7%) contribute twice as many violent comments as women (EN: 1.7%, SI: 5.6%). The chi-square test assessing the relationship between the type of SUD and gender gives a statistically significant result for English and Slovene dataset (EN: X2 (1, N = 2765) = 8.21, p = 0.004.; SI: X2 (1, N = 4056) = 35.90, p < 0.001).4 Not only are men more likely to post violent comments, but their comments are also more likely to be shorter than those written by women which was expected given that violent comments tend to be short prompts inciting violence or directly threatening the target. The Mann-Whitney U test (EN: U = 697343.5, p = 0.00009; SI: U = 1571052.0, p = 0.000002) shows a significant difference between the two genders with regard to comment length.5 In general, as presented in Graph 1, most of the comments in both datasets are relatively short with a median of around 18 words per comment (medians for EN: 17(M), 22(F); SI: 16(M), 20(F)). But while men tend to post shorter comments, they also more often than women posted excessively long comments,that is, over 400 words, and also hold the record for the lengthiest contributions in our dataset (max for EN: 513 words, SI: 1201 words) which alludes to a stereotypical perception of men as a self-assured and outspoken persona (Mills, 2005) (Fig. 13.1).

Fig. 13.1  Distribution of comments by their length (measured in words) per language and gender

 Calculator provided by Stangroom (2022).  Code by Bedre (2021).

4 5

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We note here that while we report the insights about men’s and women’s tendencies regarding their production of SUD comments relative to the total number of comments, we checked beforehand the distribution of unique commenters per gender. We found that the share of unique commenters closely reflects the share of comments as well as tokens produced. In both datasets, one-third of commenters are women (EN: 631 [29.2%], SI: 925 [30.8%]), and they produce a corresponding share of the content in terms of the number of comments (EN: 775 [28%], SI: 1224 [30.2%]) and tokens (EN: 30,342 [29.4%], SI: 42,594 [32.8%]).

3.3 Analysed Linguistic Features The study aims to establish the prevalence of affective linguistic features in the comments written by women and men. To this end, we perform a quantitative analysis of selected linguistic features at the typographical, grammatical and lexical levels as presented in Table 13.3. All automatically extracted results were manually checked and refined when needed, to exclude irrelevant instances.

4 Results In this section, we present the results of the quantitative analysis of typographical, grammatical and lexical linguistic features which introduce affect in the discourse. The results are presented separately for English and Slovene data and are compared between women and men. We provide illustrative examples of the interplay of linguistics markers of affect in SUD comments in Sect. 4.4. The implications of the results are commented in section Discussion and Conclusion.

4.1 Typographical Level At the typographical level, we analysed the use of fully capitalised words (capitalisation), the use of words with at least three consecutive letter

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Table 13.3  List of analysed linguistic features Analytical level

Linguistic feature

Typographical

Full word capitalisation

Explanation: We considered all words that were entirely capitalised by employing a rule-based method. Acronyms (e.g. BBC, EU) were excluded from the analysis. Example (EN): MASSIVE CONDOLENCES to you for being such a wally Example (SI): istospolneže na PSIHIATRIJO [samesexers to LUNATIC ASYLUM]a

Character multiplication

Expl.: We considered all words that included three or more consecutive iterations of a specific letter by employing a rule-based method.b A threshold of three was selected since the criterion of only two iterations returned too much noise (i.e. standard spellings) both for English and for Slovene data (e.g. off (EN); oddaja [a broadcast] (SI)). In addition, we excluded the instances of www when they constituted a URL address. Ex. (EN): Pooooor america … Ex. (SI): Jooooj budaaaalee slovenske pa kaj se jim mesa al kaj. [Ooooh, Slovene iiiidioots they are out of their mind or what.]

Affective punctuation

Expl.: We considered all repetitions of punctuation marksc as well as single uses of exclamation and interrogation marks, ellipsis dots and quotation marksd by employing a rule-based method. The interrogation point and ellipsis dots which are not necessarily indicators of affect were nonetheless included since a manual analysis of a random sample showed their predominant use in sarcastic rhetorical questions and propositions. Similarly, a manual sample check showed that quotation marks are mainly used as a means of emphasis and not to introduce reported speech. Ex. (EN): There is no excuse for the way they dress. No shame and where is the “pride” they so proudly tout??? Ex. (SI): /…/ A se strinjate z to tezo,da ste navaden posiljevalski prasec?!?L.p. [/../ Do you agree with the statement that you are just a rapist scumbag?!?BestRegards]

(continued)

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Table 13.3 (continued) Analytical level

Linguistic feature

Grammatical

Second- and third-degree adjectives

Expl.: We extracted comparative and superlative forms of adjectives by using the morphosyntactic tags available in the corpus. Ex. (EN): Here’s an idea bbc. Stop helping them to get across. Or is your main goal to help them in the hope some terrorists sneak through so you have a bigger story to report on when they start killing innocent people. Ex. (SI): Najboljše da te horde počakamo nekje pa jih fajn naperemo... /…/ [The best would be to wait for these hordes somewhere and beat the shit out of them… /…/]

Second- and third-degree adverbs

Expl.: We extracted comparative and superlative forms of adverbs by using the morphosyntactic tags available in the corpus. Ex. (EN): The pathetic thing is you probably actually genuinely believe that bigoted bile you spout. The sooner your ridiculous religions (all of them) get consigned to the middle ages where they belong the better. Ex. (SI): naj se raje spravijo delat, ne pa da zivijo na nas racun,..vsi ti politiki so paraziti. [they better get to work, instead of living at our expense,..all politicians are parasites.]

First-person pronouns

Expl.: We extracted all pronoun forms of 1st person singular and plural by using the morphosyntactic tags available in the corpus. The very infrequent occurrences of 1st person dual pronouns (