Hot Contention, Cool Abstention: Positive Emotions and Protest Behavior During the Arab Spring 2020030139, 2020030140, 9780190693916, 9780190693923, 9780190693947, 9780190693930

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Hot Contention, Cool Abstention: Positive Emotions and Protest Behavior During the Arab Spring
 2020030139, 2020030140, 9780190693916, 9780190693923, 9780190693947, 9780190693930

Table of contents :
cover
Half title
Series
Hot Contention, Cool Abstention
Copyright
Dedication
Contents
Acknowledgments
1. An Extraordinary Experience
2. Similar States, Opposite Outcomes: Egypt and Morocco
3. Identifying Beliefs and Inferences
4. Tracing Reasoning Processes
5. Hot Contention, Cool Abstention
6. Conclusions
Appendix 1: The Sample
Appendix 2: Beliefs Identified by the Qualitative Analysis
Appendix 3: Z-​Scores for Each Belief
Appendix 4: Minimum Sets of Beliefs
Bibliography
Index

Citation preview

Hot Contention, Cool Abstention

Series in Political Psychology Series Editor John T. Jost Editorial Board Mahzarin Banaji, Gian Vittorio Caprara, Christopher Federico, Don Green, John Hibbing, Jon Krosnick, Arie Kruglanski, Kathleen McGraw, David Sears, Jim Sidanius, Phil Tetlock, Tom Tyler Image Bite Politics: News and the Visual Framing of Elections Maria Elizabeth Grabe and Erik Page Bucy Social and Psychological Bases of Ideology and System Justification John T. Jost, Aaron C. Kay, and Hulda Thorisdottir The Political Psychology of Democratic Citizenship Eugene Borgida, Christopher M. Federico, and John L. Sullivan On Behalf of Others: The Psychology of Care in a Global World Sarah Scuzzarello, Catarina Kinnvall, and Kristen R. Monroe The Obamas and a (Post) Racial America? Gregory S. Parks and Matthew W. Hughey Ideology, Psychology, and Law Jon Hanson and John Jost The Impacts of Lasting Occupation: Lessons from Israeli Society Daniel Bar-​Tal and Izhak Schnell Competing Motives in the Partisan Mind Eric W. Groenendyk Personalizing Politics and Realizing Democracy Gian Vittorio Caprara and Michele Vecchione Representing Red and Blue: How the Culture Wars Change the Way Citizens Speak and Politicians Listen David C. Barker and Christopher Jan Carman The Ambivalent Partisan: How Critical Loyalty Promotes Democracy Howard G. Lavine, Christopher D. Johnston, and Marco R. Steenbergen Disenchantment with Democracy: A Psychological Perspective Janusz Reykowski

Hot Contention, Cool Abstention Positive Emotions and Protest Behavior During the Arab Spring

STEPHANIE DORNSCHNEIDER

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1 Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and certain other countries. Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America. © Oxford University Press 2021 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by license, or under terms agreed with the appropriate reproduction rights organization. Inquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above. You must not circulate this work in any other form and you must impose this same condition on any acquirer. Library of Congress Cataloging-​in-​Publication Data Names: Dornschneider, Stephanie, author. Title: Hot contention, cool abstention : positive emotions and protest behavior during the Arab Spring /​Stephanie Dornschneider. Description: New York : Oxford University Press, 2021. | Series: Series in political psychology | Includes bibliographical references and index. Identifiers: LCCN 2020030139 (print) | LCCN 2020030140 (ebook) | ISBN 9780190693916 (hardback) | ISBN 9780190693923 (epub) | ISBN 9780190693947 Subjects: LCSH: Arab Spring, 2010–​| Protest movements—​Arab countries—​History—​21st century. | Political activists—​Arab Countries—​History—​21st country. | Democratization—​Arab countries—​History—​21st century. Classification: LCC JQ1850.A91 D67 2021 (print) | LCC JQ1850.A91 (ebook) | DDC 909/​.097492708312—​dc23 LC record available at https://​lccn.loc.gov/​2020030139 LC ebook record available at https://​lccn.loc.gov/​2020030140 DOI: 10.1093/​oso/​9780190693916.001.0001 9 8 7 6 5 4 3 2 1 Printed by Integrated Books International, United States of America

To Jos, Lexi, Svenni, and my parents

CONTENTS

Acknowledgments  ix 1. An Extraordinary Experience  1 2. Similar States, Opposite Outcomes: Egypt and Morocco  30 3. Identifying Beliefs and Inferences  50 4. Tracing Reasoning Processes  91 5. Hot Contention, Cool Abstention  104 6. Conclusions  134 Appendix 1: The Sample  147 Appendix 2: Beliefs Identified by the Qualitative Analysis  151 Appendix 3: Z-​Scores for Each Belief  157 Appendix 4: Minimum Sets of Beliefs  163 Bibliography  165 Index  181

ACKNOWLEDGMENTS

I am highly indebted to my interviewees and the people who helped me set up interviews. I would also like to thank Stefan Dantchev, who provided me with invaluable insight and guidance when I wrote the computational model applied in this analysis. I am grateful to the Swiss National Fund, the European Union, and Durham University for providing me with fellowships to conduct this research. I am grateful to Julia Cañas Martinez for helping me prepare visualizations of the reasoning processes, and to Mariana Saad for coding a sample of quotes to examine the reliability of the coding scheme. I am also grateful to my colleagues at the School of Politics and International Relations at University College Dublin, the Georgetown Center for Contemporary Arab Studies, the School of Psychology at the University of Plymouth, and the participants of conference panels hosted by the American Political Science Association, the International Society of Political Psychology, the International Studies Association, and the Middle East Studies Associations for their helpful comments on various presentations of this research. I also thank the participants of a workshop on violent and nonviolent tactics at the Peace Research Institute Oslo for valuable comments on a related working paper. I thank Oxford University Press for the helpful reviews and smooth publishing process. I am especially grateful to John Jost for his great comments. I am also grateful to Abby Gross, Katharine Pratt, Courtney McCarroll, and the anonymous

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reviewers, as well as Dhanalakshmi Narayanan, Gokul Mathiyazhagan, and Alisa Larson. Finally, I  thank Riccardo Bocco, Martha Crenshaw, Marwa Daoudy, Jos Elkink, Jeroen Gunning, Miles Hewstone, Clark McCauley, James Piscatori, David Over, Kristian Skrede Gleditsch, and, last but not least, David Sylvan for valuable feedback.

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An Extraordinary Experience

When President Bin ‘Ali fled Tunisia in January 2011, Leila1 did not know that mass uprisings were happening in a country close by. Like millions of Egyptians, Leila had never cared about politics. “The state was always watching us,” she remembers. “It was very bad. Politicians could have done anything—​creating lies or arresting people. We did not feel connected to politics. We grew up without political education. We never participated in elections. We were completely ignorant, and did not even know the name of our foreign minister. The whole country was asleep.” Leila first heard about the uprisings in Tunisia from a European student. “I thought she must be lying,” she remembers. “There was nothing on the news and nobody was saying anything.” When Leila got home later that day, she turned on the TV. “The Egyptian channel was showing a beautiful woman standing next to the Nile,” she remembers. But when she switched to al-​Jazeera, her heart skipped a beat: Masses of Tunisians were celebrating the departure of Bin ‘Ali. “I could not believe it. It felt like a different world. I felt incredibly happy. My heart was with those people. I felt that this was very good.” It would take Leila another week to understand that the revolution was also coming to Egypt, and a few more weeks to join the mass protests that were happening in Cairo. “My family didn’t allow me to go,” she recalls. “But I went anyway. This was a historic moment. It was very important to participate.” When Leila arrived on Midan Tahrir, President Mubarak announced his resignation. “It was like paradise,” she says. “I had never Hot Contention, Cool Abstention. Stephanie Dornschneider, Oxford University Press (2021). © Oxford University Press. DOI: 10.1093/​oso/​9780190693916.003.0001

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seen Midan Tahrir like this before. People were offering me food, a child offered me an orange. I told them I was only there for the day and not staying in the tents. They told me they didn’t care, and shared their food with me anyway.” In the Moroccan capital Rabat, Ahmed was equally surprised when he heard that mass uprisings were happening in Western Sahara, a disputed territory on the west coast of Northern Africa. “There had always been small protests of 20 to 40 people,” he says. “People were protesting against the corruption of the authorities, and demanding the redistribution of wealth, employment and housing.” However, these protests never spread: “They were always suppressed,” Ahmed remembers. “The mayor was ruling since 1977. He was buying people’s votes. By the time people showed up at the urns, the elections had already been decided.” However, this changed when a family got into a fight with the mayor at the end of 2010. Their quarrel sparked an uprising in which 25,000 people came together to protest against the authorities.2 Ahmed was living in Rabat and not aware of the uprisings. “The state was closing the area to prevent journalists from reporting about the protests. It was not allowed to spread the news.” One evening over dinner, Ahmed began to understand what was happening in Western Sahara. “My family told me about the protests,” he remembers. “I was very angry. Why had I not gone there to participate? I called a friend, and he told me: ‘It is very strange. Everyone is participating.’ Before, there were always a few protestors, but never all the people. Out of the blue, everyone showed up.” After receiving this news, Ahmed traveled south. When he arrived, he found thousands of people on the streets. “This was the real beginning of the Arab Spring,” he says. “It was incredible.” Not only the people living in the Middle East were surprised by the uprisings. Around the world, observers watched in disbelief. “If there was ever to be a popular uprising against autocratic rule, it should not have come in Egypt,” el-​Ghobashy wrote in 2011. “The regime of President Husni Mubarak was the quintessential case of durable authoritarianism.” Politicians had similar thoughts. The German foreign minister said President Mubarak was “a man of enormous

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experience, great wisdom, and with a strong grip of the future,” while Hillary Clinton called the Egyptian government “stable and responsible” (Alexander 2011). British authorities considered Middle Eastern regimes so stable that their intelligence services were cutting resources dedicated to the Arab World (Norton-​Taylor 2012), while the deputy director of the Pentagon’s chief intelligence arm said he had not been aware of signs that the Arab Spring was about to happen: “We missed that” (Dilanian 2012). The Arab Spring also challenged a large body of theories on the stability of Middle Eastern authoritarianism, sparking debates on “Why Middle East Studies missed the Arab Spring” (Gause 2011) or “Why we were surprised” (Goodwin 2011). Numerous academics suddenly questioned the main assumptions of these theories, which had been developed over decades of research. “No question that none of its authors (myself included) predicted the fall of authoritarian regimes witnessed in 2011,” Bellin wrote in 2012 (143). Similarly, Gause noted (2011, 82): “I argued that the United States should not encourage democracy in the Arab World because Washington’s authoritarian allies represented stable bets for the future. On that count, I was spectacularly wrong.”

THE RESEARCH PUZZLE

What suddenly motivated millions of Arabs to mobilize against their rulers? This is the main research question I investigate in this book. A large body of literature has studied the unexpected outbreak of the Arab Spring. This research has examined the politics and structures of authoritarian regimes, such as the use of repression, the military, the security apparatus (e.g., Albrecht, Croissant, and Lawson 2016; Nepstad 2013), the organizational structures and methods used by the oppositional forces, such as prior protest tactics and organizational changes (e.g., Steinert-​Threlkeld 2017; Gunning and Baron 2014), and social grievances of the people, such as poverty or inequality (e.g., Malik and Awadallah 2013; Acemoglu, Hassan, and Tahoun 2017).

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Nevertheless, repressive regimes, protest structures, and grievances had existed in the Middle East for many decades without triggering similar mass uprisings. Moreover, the majority of people who had been suffering from poverty and repressive rulers for numerous decades stayed at home when the Arab Spring broke out in 2010—​even during the height of the protests when millions mobilized. These observations underline the limitations of theories focusing on external factors. In response, I draw on the political psychology literature and investigate the reasoning processes by which people decided to participate in the Arab Spring. Moreover, I study non-​participants in the uprisings and explore how these individuals made decisions to stay at home while millions of their fellow citizens poured to the streets to demand political change. Reasoning processes are a focusing variable that links individuals to external structures. They shed light on differences in behavior that occur in the same environment, such as participation and non-​participation in political protest. Reasoning processes consist of two main components: beliefs and inferences (belief connections). Beliefs are “a set of lenses through which information concerning the physical and social environment is received . . . orient[ing] the individual to his environment, defining it for him and identifying for him its salient characteristics” (Holsti 1962, 245; also see Simon 1985, 298).3 Belief can refer to both “inner states as well as outer realities” (Jervis 2006, 642). Outer realities can include structures (e.g., political institutions, infrastructure, or social welfare systems), events (e.g., state repression, elections, or protests), or external conditions (e.g., the weather or a locality). Inner states can include emotions (e.g., fear or hope), political attitudes (e.g., support for a politician or rejection of a policy proposal), ideological orientations (e.g., liberal or conservative), perceptions of politicians (e.g., confidence or trust), or religious convictions (e.g., beliefs in afterlife). Beliefs offer an analytical framework that can capture different types of factors, which are usually studied by different theories. Adopting this framework promises to contribute knowledge about differences in behavior that cannot be explained by external conditions.

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Beliefs are interconnected with each other by inferences. These inter­ connections are directed (“inferential”)—​meaning that certain beliefs, called “antecedents,” trigger other beliefs, called “consequents” (belief → belief) (Axelrod 1976). Inferences between beliefs can in turn trigger decisions for actions (belief → belief → . . . –​> decision). In the following, I  trace how beliefs about certain factors triggered beliefs about other factors until it became possible for some individuals to decide to protest and for others to stay at home instead. This contributes new knowledge showing how some people mobilized for the Arab Spring, while others remained at home. The analysis shows that decisions to join the Arab Spring were triggered by beliefs about positive emotions4 of hope, courage, solidarity, and national pride, which were themselves triggered by beliefs that mass protests were happening at home, that a revolution happened abroad, and that fellow citizens were sacrificing themselves. By contrast, decisions to stay at home were triggered by beliefs about living in safety, satisfactory living conditions, and state approval. Figure 1.1 gives an overview. The analysis is based on the research design of a double-​paired comparison of protestors and non-​protestors from two countries with opposite protest levels and outcomes—​Egypt and Morocco. In Egypt, the uprisings led to the departure of President Husni Mubarak, whereas the Moroccan King continues to rule until today (see Chapter 2 of this volume for a more detailed outline). To gain knowledge about reasoning processes underlying mobilization in these countries, I  conducted ethnographic interviews. I also became a member of the main Facebook groups through which people mobilized in both countries and identified posts in which individuals commented on their reasons for joining the protests (“Kulana Khalid Sa’id” in Egypt and “Mouvement du 20 Février” in Morocco). I proceeded by coding the individuals’ direct speech for the main components of reasoning processes—​beliefs, inferences, and decisions. First, I identified beliefs by examining the propositional contents of each sentence contained by the interviews and Facebook entries. In this analysis, I  systematically identified any factor mentioned by a sentence, including factors as varied as external conditions, religious convictions,

Social networks (Facebook, existing protest structures)

Beliefs antecedent to protest decisions

Hope, courage, solidarity, national pride

Safety

Relative deprivation (poverty, inequality)

External factors addressed by the literature

Decision to protest

Authoritarian regimes (repression, military, security apparatus)

Beliefs antecedent to decisions to stay at home

Satisfactory living conditions

State approval

Decision to stay at home

Figure 1.1  Key antecedents of decisions. Protest at home

Revolution abroad

Self-sacrifice 

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or emotions. Following the previously described analytical framework, I  coded each of these factors as a certain type of belief. Second, I  identified inferential connections between these beliefs (belief about factor x → belief about factor y). I  identified inferences from linguistic connectors contained by sentences, such as “because” or “if  .  .  .  then.” Finally, I  identified decisions for actions connected to beliefs (belief → belief →  .  .  .  → decision). I  identified decisions from the propositional contents of sentences referring to an individual’s behavior, such as “I went to Midan Tahrir.” This analysis constructed 121 systems of beliefs underlying protest decisions. This sample size may appear large in comparison with ethnographic studies, but small when compared with survey studies. Nevertheless, it constitutes a very large data set for the analysis of reasoning processes. Each belief system includes a great variety of interconnected beliefs. The entire data set contains trillions of combinations of beliefs and inferences—​including beliefs about factors addressed by the mentioned theories, such as repressive state behavior, oppositional forces, and inequality, as well as a large number of factors that are not captured by these theories, such as religious convictions, family relations, or personal priorities. To systematically examine these data, I developed a computer program. The program systematically traces inferences that connect beliefs to decisions about participating in the Arab Spring and identifies key beliefs and inferences underlying these decisions.

THE MAIN ARGUMENT

The main argument I develop from the analysis is that decisions to participate in the Arab Spring were “hot”—​meaning they were based on positive emotions of hope, courage, solidarity, and pride. By contrast, decisions to refrain from mobilizing were “cool”—​meaning they were based on beliefs that life was safe, that living conditions were improving, and that the head of state was acceptable. These findings provide new support for the hot cognition hypothesis (Lodge and Taber 2005) and confirm the psychology

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literature differentiating between “hot” versus “cool” systems of reasoning (Metcalfe and Mischel 1999; Kahneman 2011). The findings also specify political theories that link protest behavior to emotions. Showing that decisions to join the Arab Spring were triggered by positive emotions of hope, courage, solidarity, and pride suggest that emotions can play a different role in mobilization than found by well-​ known theories that have linked protest behavior to negative emotions of frustration (Gurr 2015; for related arguments on the Arab Spring see Lotan et al. 2011; Eltantawy and Wiest 2011) or moral outrage (e.g., Jasper 1998; Miller, Effron, and Zak 2009; for a related argument on the Arab Spring, see Preston et al. 2011). This finding also complements theories that have linked negative emotions of anger to collective action (van Zomeren 2013; Miller et  al. 2009; Tausch and Becker 2012; cf., Van Stekelenburg and Klandermans 2013, 893), political participation (e.g., Valentino et al. 2011), and political behavior more generally (e.g., Huddy, Feldman, and Cassese 2007). The findings are consistent with intergroup emotions theory (Mackie and Smith 2014; Mackie, Smith, and Ray 2008), according to which people’s emotions about their own and other groups best explain intergroup relations. The findings show that emotions of solidarity with fellow citizens, pride about their nation, hope that their efforts would succeed and courage to face the government were more important to decisions to protest against Arab rulers than any other factor examined by the analysis. The findings also show that positive emotions were themselves triggered by beliefs that a revolution had occurred abroad, that protests were happening at home, and that fellow citizens were making self-​sacrifices. This information is not usually available from the literature exploring the direct link between emotions and political behavior. The findings also complement the social identity model of collective action (SIMCA), which outlines the effects of perceived injustice, efficacy, and identity on collective action, including protest behavior (van Zomeren and Postmes 2008). In this model, group-​based emotions including anger constitute “a conceptual bride” connecting group-​based appraisal with action tendencies (506). For example, negative appraisal

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of group-​based deprivation motivates group-​based emotions of anger, which in turn motivates collective action (506). Related research has also pointed to the role of differentiated emotions in collective action against the existing order. Osborne et al. (2018, 245) have linked positive emotions of pride about one’s leaders or political traditions to system-​ supporting behavior. By contrast, negative emotions of anger, distress, and resentment about the economic order are linked to system-​rejecting behavior (245). The findings of this book complement these observations by showing that positive emotions about group effort against existing structures are crucial to explain system-​rejecting behavior. The analysis shows that beliefs that people were rising up against their rulers and sacrificing themselves and that these uprisings were successful in a neighboring country triggered positive emotions of pride, as well as hope, courage, solidarity. These findings suggest that once system-​rejecting behavior is visible, it can motivate others to join through particular positive emotions, adding to existing knowledge about the role of negative emotions of anger. The finding that hope about the protests’ success motivated people to join the uprisings is also consistent with SIMCA’s emphasis of perceived efficacy (conceptualized via strength, a sense of control as well as effectiveness to change a certain situation; see van Zomeren and Postmes 2008, 513). The findings show that of the four positive emotions differentiating decisions of protestors from non-​protestors, hope is related to the largest proportion of decisions by protestors versus non-​protestors. Moreover, the analysis shows that positive emotions of hope and courage can themselves be based on beliefs about successful protest abroad. SIMCA also highlights the role of perceived injustice, and the findings of this book are consistent with this focus. The analysis shows that beliefs about state crimes, such as corruption or censorship, are antecedent to a significantly larger proportion of decisions by protestors than non-​protestors. Of all the beliefs contained by the reasoning processes, state crime is found to be the best differentiator between protestors and non-​protestors. Complementing expectations from SIMCA, according to which state crime would be a trigger of anger, the following analysis

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suggests that state crime triggers disapproval of the head of state along with beliefs that the leader has negative character traits. This book also shows how protests can spread at the level of the individual, which complements existing macro-​level accounts of the diffusion of protest (della Porta 2017; Saideman 2012; Buhaug and Gleditsch 2008). The findings show that beliefs that others are joining the protests can trigger beliefs about feeling positive emotions about the protest behavior and self-​sacrifice of others, which in turn trigger decisions to participate. This information adds depth to studies examining the diffusion of protest across states by identifying mechanisms through which protest may spread in its initial stages of diffusion. The findings about decisions to refrain from protesting speak to theories on system justification. According to this literature, individuals strive to hold positive views about the existing social, political, and economic structures and are reluctant to challenge the status quo even when it does not benefit them (Jost, Banaji, and Nosek 2004). The finding that non-​protestors hold positive views about their rulers (state approval), believe in living in safety, and believe that living conditions were improving is consistent with these theories. The finding that decisions to stay at home were “cool” as opposed to “hot” moreover corresponds to expectations that “system justification alleviates emotional stress” (Solak et al. 2012, 17). Nevertheless, research has also shown that system justification “is observed most readily at an implicit, nonconscious level of awareness” (Jost, Banaji, and Nosek 2004, 881), which is not captured by data constructed from the direct speech of political actors. Studies of different data sets would therefore be needed to confirm the findings of the following analysis. Research on system justification has also examined protest behavior, focusing on “system-​based emotions,” which are triggered by aspects of the existing social, political, and economic structures (Solak et al. 2012). Examples of these emotions are frustration, moral outrage, and anger. Research in social psychology has shown that these negative emotions are motivators for collective action, whereas studies on system justification processes have shown that a “system-​justifying mindset” can decrease both negative emotion and willingness to protest (Jost et al. 2012). The finding

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that decisions to protest were based on emotions, whereas decisions to stay at home were based on safe living conditions is consistent with this research. Nevertheless, the particular emotions that are connected to protest behavior in this study differ (positive emotions of solidarity, hope, courage, and pride as opposed to negative emotions of frustration, moral outrage, and anger). The analysis provides limited evidence in support of grievance explanations of the Arab Spring (e.g., Malik and Awadallah 2013; Achcar 2013; Joffé 2011; Campante and Chor 2012)  and protest more generally (Gurr 1970; Davies 1962; Grasso and Giugni 2016; Rüdig and Kryotis 2014). On the one hand, a significantly larger proportion of decisions by non-​protestors than protestors were based on beliefs about suffering from economic hardship. While contradicting the grievance literature, this finding is consistent with expectations from theories linking political activism to wealthy and educated individuals, who have more time and energy to acquire information about politics (e.g., Rosenstone and Hansen 1993; Finkel 2002). On the other hand, the analysis finds that beliefs about state crime, such as corruption or censorship, motivate a significantly larger proportion of decisions by protestors than non-​protestors. This finding is consistent with the grievance literature, and specifies that protestors were primarily acting against state behavior rather than economic suffering.

HOT VERSUS COOL REASONING PROCESSES

The main finding of this study is that decisions to participate in the Arab Spring were triggered by “hot” reasoning processes, whereas decisions to stay at home were triggered by “cool” reasoning processes. Psychologists have observed such differences for more than a decade, referring to (1) intuitive reasoning processes that involve cognitive shortcuts and affect and (2)  deliberative reasoning processes that involve cognitive effort. Some have called these reasoning processes system 1 and system 2 (Kahneman 2011), while others have called them the hot/​cool system (Metcalfe and

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Mischel 1999). My own terminology follows that of Metcalfe and Mischel, developed in the 1990s. According to Metcalfe and Mischel, reasoning processes can constitute “a hot, emotional ‘go’ system” that is “the basis of emotionality, fears as well as passions—​impulsive and reflexive—​initially controlled by innate releasing stimuli (and, thus, literally under ‘stimulus control’).” Or they can constitute “a cool, cognitive ‘know’ system” that “is cognitive, emotionally neutral, contemplative, flexible, integrated, coherent, spatiotemporal, slow, episodic, and strategic” (Metcalfe and Mischel 1999, 3). Kahneman differentiates between the same types of reasoning processes, although his terminology refers to speed rather than emotions: His book Thinking, Fast and Slow explores “mental life by the metaphor of two agents, called System 1 and System 2, which respectively produce fast and slow thinking” (Kahneman 2011, 13). According to Kahneman “[t]‌he operations of System 1 are fast, automatic, effortless, associative, and often emotionally charged; they are also governed by habit, and are therefore difficult to control or modify. The operations of System 2 are slower, serial, effortful, and deliberately controlled; they are also relatively flexible and potentially rule-​governed” (Kahneman 2003, 1451). Despite applying different terms, the authors agree that there are two types of reasoning processes that can be differentiated by both affect and speed. System 1 is affective and fast, and involved in numerous daily activities, including reading words on billboards, detecting anger in a voice, orienting the source of a sudden sound, driving a car on an empty road, or understanding simple sentences (Kahnemann 2011, 21). System 2 is slow and not affective, and involved in more effortful activities, such as calculating mathematical task or finding solutions to complex problem. Kahneman gives the example of “17 × 24.” To calculate this, a person needs to engage in an effortful thinking that involves System 2. However, System 1 immediately provides a person with information about “17 × 24”—​that it is a mathematical problem, that it involves multiplication, that that the person can most likely solve this problem, or that 12, 609 and 128 are implausible results (20).

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Research in psychology shows that reasoning processes that are fast and involve affect occur very often and much more effortless than complex and deliberative reasoning processes. In the words of Kahneman (2011, 13), “System 1 is more influential than your experience tells you, and it is the secret author of many of the choices and judgments that you make.” The hot cognition hypothesis addresses this phenomenon by assuming that affect plays a crucial role in reasoning (e.g., Taber and Lodge 2016; Mittenzwei et al. 2016; van den Bos 2007; Lodge and Taber 2005; Morris et al. 2003; Redlawsk 2002; Unsworth, Heitz, and Engle 2005; Wyatt et al. 1993). Following Lodge and Taber, “affective charge” can be activated automatically when people hear about sociopolitical concepts that they have evaluated before (Lodge and Taber 2005, 455). Accordingly, “most citizens, but especially those sophisticates with strong political attitudes, will be biased information processors” (455). For example, Taber and Lodge show that “[w]‌hen reading pro and con arguments, participants (Ps) counterargue the contrary arguments and uncritically accept supporting arguments, evidence of a disconfirmation bias” while there is “a confirmation bias—​the seeking out of confirmatory evidence—​when Ps are free to self-​select the source of the arguments they read” (Taber and Lodge 2006, 755). These findings are in line with expectations from the literature on motivated reasoning, according to which “[t]‌here is considerable evidence that people are more likely to arrive at conclusions that they want to arrive at” (Kunda 1990, 480). Research in this field shows that “motivated reasoners may actually increase their support of a positively evaluated candidate upon learning new negatively evaluated information” (Redlawsk 2002, 1021). It also shows that even expert decision makers, such as judges, can be affected by “the same kinds of implicit biases as others,” even though “given sufficient motivation, judges can compensate for the influence of these biases” (Rachlinski 2009, 1195). In a more recent study of “[w]hy people ‘don’t trust the evidence,’ ” Kraft, Lodge, and Taber also observe that ideologues, especially conservatives, are consistently hyperskeptical about scientific evidence (Kraft, Lodge, and Taber 2015).

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Psychologists have observed a similar influence outside the realm of politics as well. As Dan Ariely writes in his blog, “if we’re happy, we may be overly generous. Maybe we leave a big tip, or buy a boat. If we’re irritated, we may snap. Maybe we rifle off that nasty e-​mail to the boss, or punch someone.” Other examples have included foreign trade exchange, or job seeking (discussed in George and Dane 2016, 47–​50). Studies have shown that people who experience positive emotions are more likely to engage in risky behavior in foreign trade exchange, while people with less negative and consistent emotions are more likely to be successful in the job search. Recent studies by psychologists have moreover raised doubts on assumptions that hot cognition is by its nature biased. In a study of hot cognition in major depressive disorders, Miskowiak and Carvalho (2014, 1788) conclude that although “[d]‌ecades of research on ‘hot cognition’ in MDD [major mental disorders] has shown pervasive negative biases in several domains . . . not all aspects of ‘hot’ cognitive processes are biased and distinctions must be made.” Neuroscientists have moreover referred to hot cognition as “affective cognition” and separated “disruptive affective cognition” as a distinct feature connected to mental illness. They also note that since affective cognition is considered to have multiple dimensions, there are no comprehensive ways to assess it yet (Bland et al. 2016). The following study speaks to this literature by identifying hot and cool reasoning processes related to political protest. For example, the findings presented in Chapter 5 of this volume identify a hot reasoning process in which decisions to protest were triggered by emotions of solidarity with other protestors, which were triggered by beliefs about the government attacking the protestors. Another hot reasoning process identified by the analysis shows that decisions to protest were triggered by emotions of hope to be successful, which were triggered by beliefs that a revolution was happening abroad. Another hot reasoning process shows that decisions to join the Arab uprisings were based on emotions of courage, which were triggered by beliefs that other citizens were sacrificing themselves. By contrast, a cool reasoning process identified by the analysis shows that decisions to refrain from participating in the Arab Spring were based on beliefs that living in safety is a priority, which were themselves

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triggered by beliefs that there were violent protests abroad while life at home was safe. As opposed to the aforementioned literature, this study identifies reasoning processes from direct speech and investigates emotions modeled as beliefs, which are experienced consciously (see the section Beliefs and Emotions; also see Chapter 3 for numerous examples). Adopting such an “outside in” approach (Brader and Marcus 2013, 166), this study cannot add knowledge about unconscious affect or unconscious biases. However, it shows that people consciously experience emotions as part of their reasoning processes and that this matters to their decisions. This information is not available from the literature treating affect as an automatic and unconscious element of reasoning (see Kahneman 2011; Gigerenzer and Selten 2002) and confirms expectations from the literature on emotions. Focusing on real actors, this study contributes to the vast body of literature based on experiments that have been conducted in the laboratory. Such experiments differ fundamentally from real-​world settings in which political behavior occurs. Most important, real-​world settings are characterized by uncertainty and potentially significant consequences for the actors’ lives. These characteristics are crucial and were enormously important in the Arab Spring, since protesting against authoritarian rulers involves great risks. This study shows that even under these circumstances hot versus cool reasoning was observable and could differentiate protestors from non-​protestors.

“COOL” COGNITION IN POLITICAL SCIENCE

The findings also contribute to the political science literature on reasoning processes. This literature has mostly focused on the “cool,” deliberative system, which Kahneman calls System 2. The most systematic approach to study deliberate reasoning in political science is provided by rational choice theories, according to which political behavior is based on calculations of costs and benefits. Various applications of this approach address the research puzzle investigated by this book.

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There is a large body of literature applying rational choice theory to study collective action. Lichbach’s (1995) famous The Rebel’s Dilemma argues that the occurrence of social change depends on solving a collective action problem, where it is rational for an individual to refrain from joining groups that challenge the government. In the aftermath of the Arab Spring, Meirowitz and Tucker (2013) examine whether mass protest is likely to become a long-​term component of Middle Eastern politics or remain a “one-​shot deal.” Focusing on citizens’ learning process, Meirowitz and Tucker investigate variation in protest decisions over time, concluding that the Arab Spring may remain a “one-​shot deal.” A related analysis is presented by Blaydes and Low (2012), who extend Przeworski’s (1991) model of democratic transition to the case of the Arab Spring. Building on Przeworski, they model transition as a strategic game between the main political players in a given country. Democracy arises through interaction between civil society elites who subscribe to democratic principles and regime liberalizers who prefer democracy over dictatorship. The success of transition depends on the regime liberalizers’ fear of Islamic civil society actors seizing power rather than the authoritarian elite’s fear of economic redistribution, as in earlier models. These studies focus on the consequences of the Arab uprisings—​the occurrence of transition (Blaydes and Lo 2012)  and the integration of popular protest into Middle Eastern politics (Meirowitz and Tucker 2013). The following study complements this focus by investigating the sources of the uprisings, specifically the decisions of individuals who joined the protests or stayed at home instead. The following study also complements game-​theoretic models more generally by investigating narrative accounts of political actors, whose contents go beyond what can be captured by rational choice approaches. The narratives collected for this research add insight about various factors that are unrelated to costs and benefits—​ most important, positive emotions. They also add insight about particular factors examined by rational choice models, such as the learning process of citizens. Most Egyptian interviewees had discovered new forms of government, which are key to Meirowitz’s and Tucker’s model, for many decades before the uprisings. Although autocratic, the reigns of Sadat and

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Mubarak differed in many aspects (e.g., Walsh 2003), and there was observable variation in the ruling style of Mubarak himself (e.g., Ryan 2001). The older generation of interviewees also remembered the political coup in 1952. Nevertheless, following their narratives, reasoning about these governments did not play a crucial role in decision-​making about their participation in the Arab uprisings. Given similar limitations, numerous political scientists have questioned whether existing rational choice accounts give an adequate picture of revolutions and political transitions: “Can [rational calculus] be discovered if only we apply our understanding of rationality more creatively?” (Varshney 2003, 85). In response to such questions, Kuran (1991) developed an approach that differentiates between public and private preferences to explain the Eastern European revolutions in 1989. “By suppressing their antipathies to the status quo the East Europeans misled everyone, including themselves, as to the possibility of successful uprising,” he writes (7). “In effect, they conferred on their privately despised governments an aura of invincibility. Under the circumstances, public opposition was poised to grow explosively if ever enough people lost their fear of exposing their private preferences.” According to Kuran, revolutions should occur when large numbers of people begin to protest against their rulers. However, this happened at least twice during the years preceding the Arab Spring in Egypt without leading to similar mass uprisings: In 2004, a new protest movement, called Kifaya, emerged. El-​Mahdi writes (2009, 1011): “In 1 year, Egypt witnessed more oppositional demonstrations, rallies, and the organization of nonviolent dissident groups than it has seen in the previous 25 years.” A few years later, Beinin and El-​Hamalawy (2007) reported that “[t]‌he longest and strongest wave of worker protest since the end of World War II is rolling through Egypt.” According to Kuran’s theory, preference falsification moreover works both ways, meaning that after mass uprisings, people are likely to exaggerate opposition to the regime. This makes it even more astonishing that earlier uprisings in Egypt did not lead to a revolution. Other studies of reasoning about mobilization by political scientists have addressed System 2 more indirectly—​for example, by focusing on the

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availability of new information (e.g., Vicari 2013); particular sets of ideas, such as leftist proposals for nuclear disarmament (e.g., Palmer 2002); or flaws in existing explanations for behavior (e.g., Janssen 1991). Although less systematic than rational choice theories, these studies also focus on reasoning that makes sense to the outside observer, rather than reasoning described by the decision maker, which is investigated by the following analysis.

BELIEFS AND EMOTIONS

The analysis presented in this book identifies beliefs that address a large number of factors. Of all these factors, emotions are found to be most relevant to decisions to participate in the Arab Spring. This confirms a large body of literature on emotions in contentious politics (Aminzade and McAdam 2002; Emirbayer and Goldberg 2005; Goodwin and Jasper 2003; Goodwin, Jasper, and Poletta 2009; Perugorría and Tejerina 2013), in politics more generally (Åhäll and Gregory 2015; Ariffin, Coicaud, and Popovski 2016; Brader 2006; Brader and Marcus 2013; McDermott 2004; Nussbaum 2013; Staiger et al. 2010), and in the Arab Spring in particular (Pearlman 2013; Bishara 2015; Maalej 2012). There is a debate about the nature of emotions. Although emotions are considered “central to experience” (Mercer 2010, 1), their study has suffered from a lack of conceptual clarity for decades (Marcus 2003). Over time, the word “emotion” has been applied in different ways, which has created “conceptual and definitional chaos” (Buck in Gross 2014, 5). In the words of Gross (2014, 3), “[o]‌ne of the toughest questions in the field of affective science is one of the simplest, namely: What is an emotion?” (emphasis in original). There is another debate about the relationship between emotions and beliefs, and there is no consensus about how beliefs and emotions are related (e.g., Mercer 2010; Halpern 2012; Paulus and Angela 2012; Reisenzein 2009; also see the Larazus–​Zajonc debate about the relevance of cognition versus affect; Lazarus 1984; Zajonc 1980). Specifically, it remains unclear if emotions are prior to beliefs, or vice versa.

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This study does not aim to provide clarity on what emotions are or what the relationship between emotions and beliefs is. Rather, I use beliefs as an analytical tool to identify the motivations contained by political narratives. Beliefs are a very valuable analytical tool because they provide a systematic approach to investigate political speech. In the following study, each sentence contained by a narrative is coded for beliefs and connections between beliefs. This makes visible the reasoning processes contained by the narrative, including various types of factors that are typically studied in isolation. For example, a protestor might say: “I feel very frustrated because I don’t have enough money to support my family.” An analyst investigating emotions would consider the first part of the sentence, whereas an analyst investigating economic strains would consider the second part. An analyst applying beliefs would consider that this protestor believes both that she feels frustrated and that she is poor, and that the second belief triggers the first belief. Applying these coding procedures adds analytical rigor to the existing literature on emotions during the Arab Spring, which argues that emotions motivated the uprisings by outlining the findings of existing research on emotions together with excerpts from narratives5 that express emotions (Pearlman 2013; Bishara 2015). Analyzing emotions via beliefs makes sense if belief systems are constructed from the actors’ direct speech. As mentioned, direct speech indicates that people believe they feel a certain emotion, observe a structural condition, etc. and that these factors consciously motivate them to act in a certain way. Generations of researchers have analyzed emotions based on verbal expressions (Brader and Marcus 2013, 166; also see Butler, Lee, and Gross 2009, for an example) and show that people often verbalize their emotions, especially when they feel strongly about something.

BELIEFS AND THE ARAB SPRING

What motivated people to mobilize for the Arab Spring? The Arab Spring is a case of contentious politics (Tilly and Tarrow 2015, xiii). Contentious politics are defined as “interactions in which actors make claims bearing

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on other actors’ interests, leading to coordinated efforts on behalf of shared interests or programs, in which governments are involved as targets [as in the case of the Arab Spring], initiators of claims, or third parties” (Tilly and Tarrow 2015, xiii). To explain why contentious politics happen, research has focused on “identify[ing] the common mechanisms and processes  .  .  .  that operate across the range of contentious politics and bring about change” (Tilly and Tarrow 2015, xiii). It has explored factors including the means through which claims are made (e.g., Bennett and Segerberg 2012), spatial aspects in which claims are disseminated (e.g., Leitner, Sheppard, and Sziarto 2008; Sewell 2001), or power structures in which certain actors make claims against others (e.g., Cai 2008; Ekiert and Kubik 1998; Lawoti 2007). Most of the literature on contentious politics focuses on collective aspects. However, research has shown for decades that collective behavior does not occur in an automated fashion (Olson 1965) and that there are individual differences in reactions to external conditions (Abelson 1959; Axelrod 1976; Simon 1985), including the availability of certain means, political structures, state behavior, or economic conditions. In response, this book investigates the reasoning processes by which people decided to join the uprisings or to stay at home instead. To my knowledge, this represents the first systematic investigation of individual mobilization for the Arab uprisings, acknowledging that it is individuals who form the opposition groups or social movements that constitute the main actors who engage in contentious politics. To the best of my knowledge, there are no studies that systematically trace the reasoning processes by which people decide to engage in contentious politics—​even though cognition is widely believed to matter (e.g., Vicari 2013; Weyland 2012; Palmer 2002; Janssen 1991; Taylor 1991). Nevertheless, most analyses of the Arab Spring acknowledge or imply that beliefs, which are a major component of reasoning processes, played a crucial role. In the following paragraphs, I  elaborate on well-​known and non-​ exclusive examples, namely, social media explanations, as well as theories of royal exceptionalism, and economic explanations of the Arab Spring. The goal of this section is not to provide an exhaustive overview of the

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large body of literature on the uprisings. Rather, my goal is to show that beliefs are often assumed to matter, even when they are not analyzed explicitly.

Social Media, Royal Exceptionalism, and Economic Deprivation

According to social media explanations, the new media provided people in autocratic settings with new opportunities to share ideas. This led them to “believe that they can make a difference” (McGarty et  al. 2014, 726; emphasis added). Khanfar, former al-​Jazeera director-​general argues that it was the new media itself that gave people the idea that they should claim their rights (in Sweetland Edwards 2011):  “That was Al Jazeera’s role: liberating the Arab mind. We created the idea in the Arab mind that when you have a right, you should fight for it.” Research also shows that social media drove the nature of political debates during the uprisings. In the words of Howard et al. (2011), “[s]‌ocial media played a central role in shaping political debates in the Arab Spring.” Other studies show that the new media helped unite citizens by making them believe that the uprisings were directly linked to their personal demands. In the words of Marc Lynch (2011b), “[a] decade-​long, media-​fueled narrative of change is why Arabs immediately recognized each national protest as part of their own struggle.” Most research in this field has focused on the organizational role of the new media in the uprisings (e.g., Steinert-​Threlkeld 2017; Wolfsfeld, Segev, and Sheafer 2013; Gerbaudo 2012; Khondker 2011; Eltantawy and Wiest 2011; Stepanova 2011). The main argument is that the new media provided a new means to unite and coordinate citizens in autocratic states. In the words of Bellin (2012, 138), “[s]‌ocial media (Facebook, Twitter, YouTube, cell phones with video feed capacity) and satellite television (al-​Jazeera, al-​Arabiya) together enabled the mobilization of collective action in ways that had been heretofore impossible in repressive settings.” Anderson (2011, 2) furthermore compares the Facebook organizers with leaders of

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civil disobedience movements that dated back an entire century:  “The Egyptian Facebook campaigners are the modern incarnation of Arab nationalist networks whose broadsheets disseminated strategies for civil disobedience throughout the region in the years after World War I.” Although these arguments do not explicitly address beliefs, they imply that the new media could bring people together because they shared beliefs rejecting their rulers. Another body of literature has related the Arab Spring to economic grievances. Malik and Awadallah (2013, 296) suggest that “[t]‌he centrality of the economic question is evident. Arab revolutions were fueled by poverty, unemployment and lack of economic opportunity.” Similarly, Achcar (2013) argues that “socioeconomic factors are the very heart of the Arab uprisings” (5) and that “of all the regions in the Third World, the Middle East and North Africa (MENA) region is the one facing the most severe developmental crisis” (23). In the words of Joffé (2011, 507, 512), “[t]he causes for the insurgency are similar—​they lie in the global economic crisis.” These accounts imply that the Arab Spring was based on beliefs that economic suffering had become unbearable. Joffé (2011) moreover notes that the uprisings were preceded by a sharp rise in food and energy prizes (509). This argument is consistent with relative deprivation theories, according to which such a change can frustrate people’s expectations established over previous years and motivate them to rebel (Gurr 1970). In the words of Davies (1962, 5), “[r]‌evolutions are most likely to occur when a prolonged period of objective economic and social development is followed by a short period of sharp reversal.” Even though Davies refers to decline on a much larger scale and contrasts this decline with prior development, it could be argued that the years preceding the Arab Spring represented a particular type of decline that still created an incentive for people to protest. As Lotan et al. (2011, 1376) have observed, “[t]he demonstrations were an expression of citizens’ frustration over economic issues like food inflation and high unemployment.” Others have argued that governmental responses to economic difficulties exacerbated inequalities and divided Arab societies. Kandil (2012, 197) notes that “the neo-​liberal reforms undertaken more recently

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undermined many of the material and political achievements of the middle class, favoring instead a new class of tycoon capitalists linked to the regime.” He argues that “[t]‌his created extensive dissatisfaction within the middle class, which seized on the opportunity provided by the circumstances of the Arab Spring to demand political change” (197). In this line of argument, beliefs that the upper classes exploited the gains produced by the middle class are assumed to be central to mobilization of the masses. Research on royal exceptionalism has furthermore pointed to the importance of “shared normative beliefs” held by the ruling elites of monarchies (Yom 2014). According to Yom, “beliefs about their historical rarity and dynastic superiority” play a fundamental role to explain why “most ruling kingships refrained from systematic violence and neutralized dissent through nonrepressive means” while “[p]‌residential regimes reacted against protests with mass coercion, which radicalized opposition and mobilized further resistance” (43). Menaldo (2012, 709)  develops a similar argument, which has been supported by numerous other publications (also see Matthiesen 2013; Tétreault 2011; Yom and Gause 2012): “Through the strategic use of constitutions, formal political institutions, Islamic principles, and informal norms, MENA monarchs have ‘invented’ a political culture that has helped introduce a stable distributional arrangement and self-​enforcing limits on executive authority.” Kamrava (2012, 100)  even asserted that this new culture has shifted power from authoritarian republics to monarchies: “To sum up, the stress of the Arab Spring has reignited the need for unity, this time under the auspices not of the fractious and divided Arab, but under the rubric of the GCC.” Although these accounts focus on beliefs held by the ruling elite, they imply that citizens in monarchies embraced these beliefs and responded by refraining from protest, compared with Arabs living in neighboring authoritarian republics.

Avenues for Further Research

While identifying factors related to the Arab uprisings, each of the aforementioned theories raises particular questions about the mechanisms

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by which the Arab Spring unfolded. Economic explanations raise the question how millions of Arabs who had suffered from severe poverty for many decades suddenly decided to rise up against their rulers in 2011. As Bellin noted already in 2004 (139; also see Kuran 2004; Moore 2004), “people are poor; literacy rates are low; and inequality is significant.” So how did they suddenly engage in mass uprisings in 2011? According to theories of relative deprivation, an increase in food and energy prices are key to answer this question. However, the number of people suffering from severe poverty in earlier years was already very large and did not change significantly through the increase of food and energy prices. In Egypt, 20% of the population earned approximately two dollars per day in 2005 as well as in 2011 (Joffé 2011, 509). Moreover, relative deprivation of the poor had remained relatively stable, as shown by a static GINI index between 1992 and 2006, according to which Egypt was the 19th most unequal country in the world (509). The reasoning processes of protestors shed light on this puzzle by showing what factors they considered when deciding to join the uprisings. Were they motivated by beliefs about economic suffering, and, if so, how did these grievances encourage them to join the protests in 2011? What kind of grievances did they experience? Were their grievances related to rising food prices, or did they believe they suffered from standing grievances over inequality? Various analysts have moreover noted that many protestors were “well-​educated” and not from particularly poor backgrounds (Wilson and Dunn 2011, 1250). Even the “face of the revolution in Egypt” (Wael Ghonim) was “exactly the kind of person who was poised to succeed in the Egypt of Mubarak: bilingual, educated at the American University of Cairo, and at home in the global business world” (Gause 2011, 86). What explains the mobilization of individuals who did not suffer from economic grievances? Were they driven by beliefs about grievances held by fellow citizens instead? Were they unsatisfied with autocratic structures? The following analysis adds insight by examining their reasoning processes and identifying the key beliefs underlying their decisions.

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Social media explanations also raise the question why the Arab Spring did not occur earlier, when the new media arrived in the Middle East. The Internet had been available since the 1990s, and Facebook launched an Arabic version for its growing number of users in the Middle East in 2009. Why did the uprisings not happen then? It could be argued that Twitter had arrived more recently, that tweets exploded just before the Arab Spring broke out, or that the major Facebook groups through which people mobilized were created in 2010 and 2011. This is supported by evidence that large numbers of people indeed mobilized through social media (Jost et  al. 2018; Steinert-​Threlkeld 2017; Howard et  al. 2011; also see Soueif 2011). Nevertheless, what remains unclear is how social media united people to join the protests in 2011. What are the beliefs that people shared online, and how did these beliefs motivate them to protest? What were the key beliefs underlying individual decisions to join the uprisings? SIMCA suggests that perceived efficacy is a major factor and perceived efficacy could be evoked by beliefs about being connected to a large group of people online. Nevertheless, existing studies do not show if this is really what happened in the case of the Arab Spring—​there are no analyses tracing the reasoning processes by which individuals mobilized for the uprisings to this date. Finally, theories on royal exceptionalism raise the question of whether people living in monarchies refrained from protesting based on beliefs about their rulers’ superiority and historical rarity, even when authoritarian rulers in neighboring countries were resigning. Existing analyses of royal exceptionalism focus mostly on Arab rulers and do not provide information about the beliefs held by people living in Arab monarchies. The following study provides insight by systematically exploring the belief systems of individuals from a Middle Eastern monarchy, Morocco. This analysis complements theories on royal exceptionalism by showing how individuals reasoned about their monarchs when considering joining the Arab uprisings. Theories on royal exceptionalism moreover raise the question of whether citizens were aware of their rulers’ non-​repressive reaction to the uprisings and whether this contributed to decisions to stay

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at home instead of joining revolts. Did people in monarchies hold beliefs that their rulers were responding to protest in a peaceful way? And were such beliefs related to positive judgments of their rulers, or decisions to refrain from protesting?

MAIN FINDINGS: HOT CONTENTION VERSUS COOL ABSTENTION

The main finding of this book is that contention in the Arab Spring was “hot,” whereas abstention was “cool.” Many interviewees expressed beliefs about positive emotions. In Egypt, many protestors said they felt emotions of courage or pride. “I am not afraid of not returning,” Muhammad wrote on Facebook, when announcing his participation in the Egyptian protests on January 25, 2011. He said that when his two girls grew up, “they will boast that their father played a role in the liberation from oppression and corruption.” Similarly, another protestor wrote “I was ready to die, I was not afraid.” Wael Ghonim, organizer of the main protests in Egypt, said he felt proud of his nationality: “I am a citizen of Egypt. Do you know Egypt? Egypt who challenged and defeated Israel in 1973. . . . Egypt who challenged England. . . . Egypt who expelled the French. Egypt expelled the French. . . . There is no occupier who can take away the identity of the people.” A leader from Morocco’s largest Islamic opposition movement al-​‘Adl wal-​Ihsan, said he started feeling hopeful when following news about mass mobilization in Egypt and Tunisia. “These systems were very strong. They had the police and the state security,” he remembers. But then he saw the presidents of both states resign within a few weeks. “The context was extremely encouraging,” he says. “It seemed easy.” He started to call on members of his movement, so that they would join the protests at home. One member says he decided to protest related to feelings of solidarity with al-​‘Adl wal-​Ihsan. “They [al-​‘Adl wal-​Ihsan] were everywhere in the town where I grew up.” He says: “They are good

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people, they do not use violence. I  like them. I  often participate in protests with them.” In contrast, many non-​protestors said they did not join because of safety in their daily lives or caring about their jobs. “Most of us are very poor,” a vendor in a drug store in Marrakech, says. “We need to improve poverty. We need to improve social justice. I agree with these demands.” However, he continues: “Most people prefer stability, even if they are poor. I work to make money, I like my job.” Across Jama’a al-​Fna’, a vendor selling paper, has similar thoughts. “I was in my shop. I had to be here,” he remembers February 20, 2011, the main day of the uprisings in Morocco. “They [the protestors] want higher salaries. So one demonstration leads to the next. This brings killing, war, and blood. Demonstrations bring us more problems.” In Egypt, a taxi driver said many people were struggling financially. “There is a lot of poverty and corruption,” he said to explain why people were mobilizing. However, when considering joining the uprisings, he wonders:  “If I  stop working, what will happen to my family?” Many non-​protestors also thought that their fellow citizens were not ready for a revolution. Another Egyptian taxi driver says:  “You have to beat the people. Beatings and intimidation are the only means that work. If you tell someone to turn right on this street, he will not do it. But if you beat him, he will do the right thing. People are used to this. This is the only way to have a safe society.” An Egyptian businessman agrees: “It is useless to protest. The people here are bad. We have no system for anything. In Germany, everything is very organized. Look around [points to a truck blocking a street]: what do you see?” The wife of a Moroccan businessman has similar thoughts. When asked about the uprisings in Rabat, she responds:  “This is not a protest. People are asking for jobs. Everyone wants to work for the government. I do not agree with them. They can search for a job anywhere, in a supermarket for example, but instead they ask the government. It is better to look for something else. . . . There are many possibilities for work in this country: You can be a vendor in a shop, you can work in restaurants, or in private companies.”

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OUTLOOK

In the following chapters, I briefly return to Leila’s and Ahmed’s experiences of the Arab Spring. In Chapter 2, I describe my research design (a double-​ paired comparison) and discuss how the uprisings in Egypt and Morocco serve the investigation of my research puzzle. Then I introduce my sample, which I constructed from ethnographic interviews during field research, and an analysis of Facebook. I also comment on various biases and describe how I  interacted with the people who participated in the Arab Spring. In Chapter 3, I show how I analyzed the individuals’ direct speech to identify reasoning processes that motivated their decisions to participate in the protests, or to stay at home instead. First, I briefly introduce Corbin’s and Strauss’s coding procedures, which I applied in this analysis. I then present numerous excerpts from my interviews as well as Facebook entries. I explain, line by line, how I used the coding procedures to identify the main components of reasoning processes from direct speech: beliefs, inferences (direct and indirect), and decisions for actions. In Chapter 4, I present my computer program, which I developed to systematically trace reasoning processes from the beliefs and inferences I  identified from direct speech. In Chapter  5, I  present the findings of the computer analysis. Confirming the literature on hot cognition and emotions more generally, I  show that decisions to mobilize were primarily motivated by beliefs about feeling positive emotions (solidarity, courage, hope, national pride), whereas decisions to stay at home were not motivated by beliefs about feeling emotions and triggered by beliefs about living in safety, improving living conditions, and state approval instead. The final chapter puts in perspective my findings by reflecting on the existing literature on the Arab Spring and political mobilization. I  also discuss policy implications of my findings related to current events in the Middle East. I conclude with the current life experiences of Leila, whose participation in the Arab Spring I have described at the beginning of this introduction.

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NOTES 1. Where appropriate, names of interviewees have been anonymized. 2. https://​en.wikipedia.org/​wiki/​Gdeim_​Izik_​protest_​camp 3. Despite of their subjectivity, the same beliefs can be held by large numbers of people (Converse 1964; Jost 2006). Beliefs about external factors are also verifiable from an external perspective. 4. Applying belief systems as an analytical tool to study the actors’ direct speech, I model emotions as beliefs. I explain this approach in the section Emotions and Beliefs. 5. Pearlman draws on sources including the press, testimonials of particular individuals, photos, or videos. Bishara (2015, 966)  draws on semi-​ structured interviews supplemented with media accounts and observations that express emotions like anger or desperation (e.g., workers “took off their shirts and their undergarments to express their desperation.”)

2

Similar States, Opposite Outcomes Egypt and Morocco

When Leila arrived on Midan Tahrir in February 2011, she felt as if she had entered “paradise.” Hundreds of thousands were celebrating the departure of President Mubarak. They were chanting, “We have brought down the regime,” they were waving the Egyptian flag, they were hugging each other, and they were crying from joy. Millions around the globe watched the celebrations. “[A]‌fter all of these weeks of frustration, of violence, of intimidation . . . the people of Egypt undoubtedly have been heard, not only by the president, but by people all around the world,” al-​Jazeera reported from the square (“Hosni Mubarak,” 2011). In the words of Wael Ghonim (2011), “[t]he Egyptians, the Tunisians, and the rest of the people in the Arab world are proving today that the power of the people is much stronger than the people in power.” In Morocco, people were less cheerful. Ahmed was busy planning the main protests in the capital Rabat, scheduled for February 20, 2011. “I could tell from the faces of the people that they had expectations and felt anxious,” Ahmed remembers. A few days before the uprisings, one of his friends warned him that a false announcement had been circulated about canceling the February 20 protests. “The Makhzen1 wants to ruin everything with a false announcement,” the friend told Ahmed. “We did not Hot Contention, Cool Abstention. Stephanie Dornschneider, Oxford University Press (2021). © Oxford University Press. DOI: 10.1093/​oso/​9780190693916.003.0002

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stop,” Ahmed remembers. “We had to do something. . . . We are against the absolutism of the King, against the nepotism of his helpers.” When a cyber activist offered help, they launched an online campaign to uphold the call for protests on February 20. Eventually, the day turned out to be the biggest Moroccan uprising in people’s memories: Tens of thousands filled the streets of major cities across the country. Instead of stepping down in response to the protests, King Muhammad VI proposed changes to the Moroccan constitution. Later in 2011, there was a referendum approving his propositions. By contrast, President Mubarak, who had been in power since the 1980s, resigned from office. Democratic elections followed, leading to the rise of the Muslim Brotherhood and a new, popularly elected president. Egypt and Morocco display similar features related to opposite outcomes of the Arab Spring. Both were authoritarian. Both had elaborated mobilization structures. And both were suffering from economic hardship. Yet only Egypt experienced protests leading to the fall of the head of state, whereas Morocco remained under the rule of King Muhammad VI. By choosing to compare Egypt and Morocco, this study adopts a most similar systems research design (see Table 2.1). The most similar systems design “concentrates on meaningful systemic differences” (Lijphart 1975, 165)  by studying cases displaying “common systemic characteristics” Table 2.1  A Most Similar Systems Design Egypt

Morocco

 Authoritarianism

Yes

Yes

  Organizational structures for protest

Yes

Yes

Poverty

Yes

Yes

Yes

No

Similar features related to protest by the literature

Different outcomes of the protest   Fall of head of state   Democratic elections

Yes

No

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(Przeworksi and Teune in Tarrow 2010, 234)—​such as authoritarianism, mobilization structures, and poor living conditions. In this design, similar characteristics “are conceived of as ‘controlled for,’ whereas intersystemic differences are viewed as explanatory variables” (Przeworksi and Teune in Tarrow 2010, 234). Most similar systems designs have the advantage of “direct[ing] attention to the ways in which they [the two cases] differ,” which is not easy to achieve otherwise (Gerring in Tarrow, 234). Adopting the most similar systems design referring to Egypt and Morocco allows me to explore various limits of the Arab Spring literature. As discussed in the Introduction, the Arab Spring is often explained by referring to authoritarianism, mobilization structures, or economic hardship. Despite their relevance, these features had existed for a long period of time when the Arab Spring broke out in 2011. Moreover, these features address external conditions, which cannot explain differences in the behavior of individuals (such as participation and non-​participation in the Arab Spring). The most similar systems design also adds to the existing literature including case studies of particular countries, such as Libya (e.g., Prashad 2012), Tunisia (e.g., Alexander 2016), or Yemen (e.g., Knights 2013). It adds analytical rigor by drawing on a “second case,” which “can confirm a tentative finding from a single case” (Tarrow, 2010, 234). In this study, it ensures that the findings are not unique to a country where the uprisings led to the change of government (Egypt) or where they failed to result in such a change (Morocco). This is an important control which remains absent from many studies of the Arab Spring. Finally, as Tilly has written, “some cases are inherently interesting” (Tilly in Tarrow 2010, 234). Egypt and Morocco provide an inherently interesting comparison because they differ so much in the outcome of the Arab Spring but are so similar in so many other aspects. By contrast, comparing countries with similar outcomes of the Arab Spring, such as Kuwait and Jordan, where there was no regime change, or Tunisia and Egypt, where the heads of state resigned, appears to be much less interesting. This study departs from most similar systems designs by exploring psychological rather than systemic factors. In this framework, systemic

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Table 2.2  A Double-​Paired Comparison Protestors

Non-P ​ rotestors

 Authoritarianism

Yes

Yes

  Organizational structures for protest

Yes

Yes

 Poverty

Yes

Yes

Yes

No

Similar features related to protest by the literature

Different outcomes   Participation in protest

factors are represented by the beliefs that people hold about them (e.g., beliefs about suffering from economic deprivation, or beliefs that the state is authoritarian). Connections between systemic factors and the uprisings are investigated by checking if beliefs about these factors can trigger decisions for actions. This study also differs from other applications of the most similar systems design by conducting a double-​paired comparison, rather than a paired comparison (see Table 2.2). This design includes two comparative pairs: (i) Egypt and Morocco and (ii) protestors and non-​protestors. In the following, I first elaborate on the aforementioned similarities between Egypt and Morocco—​authoritarianism, mobilization structures, and poor living conditions—​and discuss the relevance of each of them to the Arab Spring. Then I introduce my research sample. Later chapters investigate the beliefs protestors and non-​protestors held about structural factors, as well as other factors, including emotions and external events.

AUTHORITARIANISM

Many protestors I met in person or observed online confirmed the importance of authoritarianism by commenting on experiences of social and economic oppression. “We are suffering from oppression,” a Moroccan

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protestor says during our meeting in the central station of Rabat. He keeps watching who is behind his back to ensure that nobody can overhear his words, while his daughter is playing with his hands. “Freedom and human rights are suppressed,” he says. “The regime wants to show that it is good to the outside, but in reality, this is not true. It [the regime] builds a system to strengthen its rule, such as the military. . . . In Morocco, corruption and dictatorship are especially severe.” In the years preceding the Arab Spring, he started calling for more fairness at work. Five policemen showed up at his home, where he lives with his wife and children. “My daughter had just turned four,” he remembers. “I went to the police station. They interrogated me: ‘What are you doing?’ They showed me a flyer I had distributed. The following Monday, my employer called me: ‘You do not have to come to work today.’ I asked why, but got no explanation. I drove to work, but they blocked my way into the building.” Another protestor remembers the day before the constitutional referendum in 2011, in which the king proposed reforms responding to the Arab Spring demonstrations: “The authorities threatened the people: ‘If you do not do this, you will have problems, you will not have your papers.’ ” He says: “The violence by the police put a lot of pressure on those in the most populous districts, where there are a lot of people who can’t read or write. The tradition of the Makhzen is to intimidate the people.” Both Egypt and Morocco have been authoritarian states for long periods of time. In Egypt, a military elite has been ruling since a military coup expelled the British in 1952. In Morocco, the Alaouite Dynasty has been ruling the country since the 17th century. Ahmed recalls that elections were already decided once people reached the ballot box, whereas Leila remembers that she never voted before President Mubarak stepped down in 2011. Mobilization against authoritarian regimes has occurred around the world for many decades. In the Middle East, there is a long history of opposition “to fight against tyranny” (Stephan 2009, 1) and “closure of political systems” has been considered a key factor that serves “to stimulate broad demands for regime change” (Durac 2013, 175). Scholars have

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not only linked mobilization in the Middle East to authoritarian states—​ mass uprisings around the world are believed to have happened in resistance against authoritarian rulers (e.g., Goldstone 1991; Goodwin 2001; Ulfelder 2005). Examples include mass mobilization in countries from different regions, such as France (e.g., de Tocqueville 2001; Skocpol 1979), Russia (e.g., Skocpol 1979; Horvath 2011), China (e.g., Cai 2008; Skocpol 1979), America (e.g., Brockett 1991; Cuzan 1991), and Asia (e.g., Slater 2010). Research also suggests that protesting against authoritarian rulers has often been successful, bringing about “more durable and internally peaceful democracies” (Chenoweth and Stephan 2011, back cover). Relating the Arab Spring to authoritarianism, the uprisings in Egypt and Morocco might have been expected from the literature on mass mobilization and may not seem that surprising. However, the timing of the uprisings is a surprise, given the longevity of authoritarianism in the Middle East: Why, after decades of living in a highly authoritarian environment, did people suddenly decide to rise up against their rulers in 2011? And if authoritarianism was a major motivation underlying the uprisings, how did it suddenly encourage people to ask their governments to resign?

MOBILIZATION STRUCTURES

At the beginning of the Arab Spring, both Egypt and Morocco also had elaborated mobilization structures. For decades, rulers had allowed oppositional forces to develop sophisticated channels for protest (often with the goal of monitoring their opposition2). Actors include political parties, such as the Wafd in Egypt and the Justice and Development Party in Morocco; human rights groups, such as the Moroccan Association of Human Rights and the Hisham Mubarak Law Centre in Egypt; and trade unions, such as the Moroccan Workers’ Union and the Egyptian Trade Union Federation. Both countries also have large Islamic opposition movements, such as the Muslim Brotherhood (Egypt) and al-​‘Adl wal-​Ihsan (Morocco), which have nevertheless been banned or partially excluded from political participation.

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People from both countries also had access to the internet, which offered unprecedented opportunities to mobilize. In Egypt, 35.6% of the population (29,809,724) had internet access in 2012, which represents a rise of more than 24 million since 2006 (Internet World Stats 2019a). In Morocco, 49% of the population (15,656,192) had internet access in 2011, representing a rise of more than 14  million since 2005 (Internet World Stats 2019c). In the words of Castells (in Gunning and Baron 2013, 302): Because they [internet networks] are a network of networks, they can afford not to have an identifiable centre, and yet ensure coordination functions. . . . Thus, they do not need a formal leadership, command and control centre, or a vertical organization to distribute information and instructions. This decentered structure maximizes chances of participation in the movement. In Egypt, there were 42  million Facebook users in December 2019 (Internet World Stats 2019b). In 2010, hundreds of thousands joined a Facebook group called Kulana Khalid Sa’id (“We Are All Khalid Sa’id”). The group was created by Wael Ghonim in response to the brutal murder of a 28-​year old Egyptian by the police. Several eyewitnesses reported how Khalid was forced into an entrance of a residential building in Alexandria, where the police started banging his head against the staircase and the wall. Khalid shouted for mercy, but the policemen continued beating him until he was dead (El-​Shaheed 2016). The murder and the subsequently published photos of Khalid’s body caused an outcry among large numbers of Egyptians. In Morocco, there were 18 million Facebook subscribers in December 2019 (Internet World Stats 2019b). During the Arab uprisings, activists created a Facebook page called Mouvement du 20 Février (“Movement of February 20th”).The name referred to the day when the main uprisings in Morocco were scheduled—​February 20, 2011. The movement united many opposition forces across the country, including al-​‘Adl wal-​Ihsan, the Moroccan Association of Human Rights, Attac Maroc, and others. Some members say the originally intended date of the protests was

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February 27, 2011. However, this day was connected with the birth of the Polisario, who have been fighting for independence of Western Sahara, which many activists considered to be “a different fight.” The organizers therefore changed the date to February 20. Like Kulana Khalid Sa’id in Egypt, Mouvement du 20 Février used Facebook to call for the protests. Many protestors believe that Facebook played an important role in their mobilization for the Arab Spring. In his speech at Time magazine, Wael Ghonim (2011), says:  “This revolution, there was no single hero. . . . Everyone in Egypt was a hero. . . . I did not call for the protests. It was angry comments on the Facebook page that called for the protest. . . . All I did was very simple: It was create a new event, write revolution, put 25th of January [the main day of the uprisings], and publish.” Numerous academic researchers agree with this judgment. In the words of Bellin (2012, 138): “Social media (Facebook, Twitter, YouTube, cell phones with video feed capacity) and satellite television (al-​Jazeera, al-​Arabiya) together enabled the mobilization of collective action in ways that had been heretofore impossible in repressive settings.” Researchers also consider existing oppositional structures more generally to explain the uprisings. El-​Ghobashy (2012), for instance, argues that “the organization and daily routines of the Egyptian population had undergone significant changes in the years preceding the revolt, which allowed “the ruled” to take advantage over “the rulers.” “Egypt’s was no cartoon dictatorship that indiscriminately banned protests,” el-​Ghobashy (2011) writes. “For at least a decade before Mubarak’s ouster, Egyptians were doing their politics outdoors. Citizens assembled daily on highways, in factory courtyards, and in public squares to rally against their unrepresentative government.” In 2011, what seemed like “a routine political demonstration calling for reforms” became a “nationwide cry for regime change” at a point in time “when three distinct currents of protest—​labor, professional, and popular—​finally converged” (Ghobashy 2011). According to el-​Ghobashy (2011), it was “that convergence” which “converted a familiar, predictable episode into a revolutionary situation.” Similarly, Gunning and Baron observe (2013, 33)  that Egypt had experienced four “waves of protest” by the time the Arab Spring broke

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out, “each feeding off the previous wave(s), drawing in an ever-​widening pool of activists and producing new tactics.” Like el-​Ghobashy, Gunning and Baron note that the uprisings happened when activists from various groups united, bringing together judges, Muslim Brothers, professional syndicates, or “a new stratum of young activists” (53). In Morocco, “[l]‌ike-​minded Facebook protest groups quickly sprang up among Morocco’s Internet-​savvy, mostly politically unaffiliated twenty-​ something generation,” Maddy-​Weitzman (2012) writes. He observes: By mid-​February, the atmosphere became increasingly charged, and the Moroccan protest movement gained a bit more form with the establishment of the “February 20th Movement,” a cross-​section of young activists running the gamut from previously unaffiliated Facebook users, members of Amazigh associations and various leftist groups, to members of the officially banned but reluctantly tolerated Islamist movement, al-​Adl wal-​Ihsan. Its inaugural February 20 protests sent tens of thousands of demonstrators into the streets across the country and were followed by smaller, ongoing, weekly protests. Relating the Arab Spring to mobilization structures, the uprisings in Egypt and Morocco might have been expected from the literature and may not seem that surprising. Nevertheless, what remains unclear is how these mobilization structures suddenly became a vehicle for unprecedented mass uprisings experienced by both Egypt and Morocco in 2011.

ECONOMIC HARDSHIP

“Life is terrible,” an Egyptian says. “Using the microbus costs two pounds fifty (28 cents) and I always have to stand. It is very bad. Every time I get home, there are people protesting.” Another Egyptian says: “Mubarak didn’t get anything done in 20 years.” She continues: “As a comparison: Someone has a beautiful house, but he never bothers to get water access or to set

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up walls. There are many problems. There are many inequalities between the poor and the rich. Some make two pounds (23 cents) and others 200 pounds (22.54 USD) per day. How is that possible?” A Moroccan says: “We suffer from poverty, illiteracy, and unemployment. These are our social problems, most people live like this.” He says that half of the Moroccan people cannot read or write and that wealth is being abused. Both Egypt and Morocco suffered from economic hardship and widespread unemployment, especially among the youths, when the Arab Spring happened. “Youth employment is the dominant form of unemployment in Egypt, and the most serious kind of youth exclusion,” the Egypt Human Development Report noted in 2010, the year before the outbreak of the Arab Spring. “At least 90% of the unemployed are aged less than 30 years, and many more are affected by underemployment” (United Nations 2010, 6). Between 2008 and 2010, the number of people suffering from poverty rose from 19.6% to 21.6%, and the number of poorest villages reached 1,141, including 1.1  million households with 5.3  million poor people (United Nations 2010, 20, 31). In Morocco, poverty reached 35% in areas with mountains and rough terrain, and 14.5% of the rural population (43% of the Moroccan population) lived in poverty in the year preceding the Arab uprisings (Akhtar 2010). Youth unemployment rose from 17.7% in the 1990s to 20.2% in 2014 (World Bank 2016). For many decades, researchers have argued that poor living conditions can play a role in the outbreak of mass uprisings. Already in the 1960s, Davies (1962, 5)  wrote that “[r]‌evolutions are most likely to occur” if people’s needs are not satisfied, and their expectations for social and economic development are not met. “A revolutionary state of mind requires the continued, even habitual but dynamic expectation of greater opportunity to satisfy basic needs, which may range from merely physical (food, clothing, shelter, health, and safety from bodily harm) to social (the affectional ties of family and friends) to the need for equal dignity and justice” (8). Gurr (2015, 9)  further developed this argument. According to him, “perceived deprivation” relative to what people think they deserve has the power to “galvanize large segments of a political community into action”

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by creating feelings of frustration. Specifically, Gurr suggests that relative deprivation triggers feelings of frustration, which then trigger mass uprisings. In line with Gurr’s theory, Eltantawy and Wiest (2011, 1211) have argued that the uprisings were related to “public frustration and impatience with the regime,” while others have observed that unemployment among the youth in particular played a key role (al-​Momani 2011; Campante and Chor 2012; LaGraffe 2012; Bogaert and Emperador 2011)3 or that economic hardship in general was a major trigger of the uprisings (e.g., Achcar 2013; Malik and Awadallah 2013). Relating the Arab Spring to economic grievances also suggests that the uprisings could have been expected from the academic literature. However, what remains unclear is how economic grievances suddenly motivated millions of Arabs who had been suffering from economic grievances for many decades to rise up against their rulers.

THE SAMPLE

To answer the questions raised above, this study explores the reasoning processes of protestors and non-​protestors from Egypt and Morocco. To collect information, I conducted ethnographic interviews (in Arabic and French). I also analyzed Facebook groups, focusing on entries by particular individuals in response to the main calls for protests in both countries (January 25, 2011 in Egypt and February 20, 2011 in Morocco). To identify reasoning processes from these sources, I first coded the actors’ direct speech for the main components of reasoning processes—​(i) beliefs, (ii) inferences, and (iii) decisions. Then I  conducted a computational analysis that systematically traced the beliefs based on which individuals made decisions for actions. The sample contains 121 reasoning processes. Fifty-​three reasoning processes are related to decisions to protest, and 68 reasoning processes are related to decisions to stay at home. Nineteen reasoning processes related to decisions to protest were identified from Facebook, whereas 102 reasoning processes were identified from ethnographic interviews. In total,

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I interviewed 93 individuals, including men (65) and women (28) from various age groups and socioeconomic backgrounds. Some interviewees explained their engagement in more than one event. I  coded these explanations separately, so that the interview analysis identified 68 non-​ protest and 34 protest decisions in total. Appendix 1 shows an overview. I held ethnographic interviews4 in 2014, when it was possible to conduct field research in Morocco, whereas the situation in Egypt was tense. In Morocco, I approached people in public places in Casablanca, Marrakech, Rabat, and Salé. Public places where I  recruited interviewees include streets, shops, libraries, and coffee shops. Many interviews followed interactions that were part of my daily life, such as locating books in libraries, purchasing medication in pharmacies, or getting groceries on my way home. Other interviews were obtained by approaching individuals in a public place and asking for an interview, following the process outlined in the next section. I also set up interviews with protestors by contacting members of Mouvement du 20 Février, the leading protest movement during the Arab Spring, who invited me to participate in a conference on democratization, where I met other protestors. I also contacted opposition journalists, Muslim leaders, and politicians from the opposition by drawing on the help of researchers I knew abroad. To avoid getting locked into the social networks of particular interviewees, I did not apply snowball sampling, and I took care to speak with women and men, old and young people, and people with different occupations, such as bankers, librarians, donkey riders, housewives, or unemployed people. Given this procedure, the people that are systematically excluded from the Moroccan sample are those who do not access public spaces. This group could include very old individuals, individuals who suffer from health problems that prevent them from going outside, and very rich individuals who did not spend their lives in public places. However, it is very unlikely that this group played a crucial role to mobilization for the Arab Spring—​old or sick people would have been unable to participate in the uprisings, and very rich people depend less on governmental change (indeed, much of the political elite itself belongs to the very rich).

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In Egypt, approaching people in public places was not possible given the tense security situation in 2014. My colleague Giulio Regeni was brutally murdered when conducting related research a few months later. Given these conditions, I relied on an extensive network of contacts I had established during ten years of prior research, including academics, journalists, friends, and friends of friends. For example, I asked an old friend if I could contact his father, or I asked my former Arabic teacher if I could interview her. I also occasionally interviewed people I met while I was traveling, such as a cleaner, taxi drivers, or shopkeepers (examples to follow). I conducted interviews in two big cities, Cairo and Alexandria. As in Morocco, I tried to include people from different backgrounds, ages, and sexes. Therefore, the Egyptian sample consists of a similarly wide variety of individuals as in Morocco, despite the difficulty of recruiting interviewees. Interviews in both countries investigated behavior in 2014 as well as prior behavior during the Arab Spring. Of 121 reasoning processes, only three refer to a change of behavior (from protest to non-​protest). This is consistent with the literature on belief stability (Renshon 2008)  and suggests that the reasoning processes identified from interviews held in 2014 can capture the beliefs and inferences underlying decisions made in earlier years. The Facebook analysis focused on the biggest protest in Egypt and Morocco. I  accessed information by joining the two major Facebook groups that called for protest in both countries, Kulana Khalid Sa’id in Egypt and Mouvement du 20 Février in Morocco. In those groups, I went back in time to the day when the calls for the main protests were made, January 25, 2011 in Egypt and February 20, 2011 in Morocco. Hundreds of individuals responded to the protest calls. Some sent emojis; others recited songs, poems, and idioms; or wrote down expressions of surprise or joy. Some commented on particular experiences or emotions without mentioning decision about participating in the protests. Others said they wanted to join but did not provide information about their motivations. A  few individuals tried to discourage others from protesting. For the analysis, I  only selected answers in which individuals provided long descriptions of their decisions to participate in the protests. Based on this

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choice, I could systematically code Facebook entries for beliefs, inferences, and decisions for actions, the key components of reasoning processes. A problem related to Facebook is the lack of evidence that individuals who said they were going to protest actually joined the uprisings. Nevertheless, social media explanations have shown that large numbers of people learned about the protests online and then decided to join them—​suggesting that many who said they were going to protest online also participated in the uprisings, including the individuals examined by this study.5

Ethnographic Interviews

Ethnographic interviews are “a particular kind of speech event” (Spradley 1979, 55, italics in original). Based on “rapport” between the interviewer and interviewee, they typically resemble “casual, friendly conversations” (Spradley 1979, 58; also see Spradley 1979, Appendix B). Spradley characterized ethnographic interviews by three main features: (i) explicit purpose, according to which the interviewer explains where the conversation is intended to go; (ii) ethnographic explanations, where the interviewee explains what she is asking or doing to make the interviewee feel comfortable (e.g. “I would like to write some of this down”), and (iii) ethnographic questions, which enable the interviewer to collect information about the informants’ language, cultural knowledge, and the meaning of certain terms or experiences (Spradley 1979, 59–​60). My organization of interviews included three stages. First, I introduced myself to potential interviewees and asked if they would have the time to talk with me (stage 1). Second, I  described the research project and asked if the individuals had any questions (stage 2). Third, I posed ethnographic questions or encouraged interviewees to further elaborate on their thoughts by nodding or repeating words (stage 3). Typically, I first asked descriptive questions about the interviewees’ behavior—​were they participating in protest or not? Occasionally, I  knew about this behavior because interviewees had told me in a prior conversation. In these

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cases, I  began interviews by asking “I remember you told me you are engaging in X?” After these introductory questions, I  avoided following up with questions. This encouraged individuals to elaborate on their behavior. I avoided interrupting these elaborations when they paused and waited, nodded, or repeated what they said in the form of a question. In this context, interviewees typically gave lengthy descriptions of their behavior. They often talked about prior behavior—​protestors remembered their first experiences of protesting, and non-​protestors recalled earlier protests they had not joined. If prior behavior was not addressed during an interview, I posed related questions at the end of the interview. In later stages of the interviews, I  also began to ask narrower questions that followed up on particular aspects. For example, one of my interviewees, who was a member of a group called Mouvement Alternatif pour les Libertés Individuelles (MALI; The Alternative Movement for Individual Freedom), mentioned an “abortion ship,” and I asked him to tell me more about that ship. He explained that MALI used the ship to provide women with information on abortion, which is not allowed in Morocco, from a team of experts, including doctors and pharmacists. MALI also organized a hotline for women in need of an abortion, which was displayed on the ship. Another interviewee, who was a non-​protestor from Egypt, used the word “evil” when talking about the Muslim Brotherhood extensively, and I  asked him what he meant by “the evil Muslim Brotherhood.” In response, he described an encounter with two Muslim Brothers who refused to let him pass a bridge across the Nile one night. My interviewee said one of them got out a knife when he tried to pass them, so he ran away. In the final stage of the interviews, I asked if interviewees wanted to add or emphasize any information. Typically, they answered that they had exhausted the subject, but occasionally, individuals returned to a point they had made earlier and considered important, for example, governmental corruption, observations of repression, or the self-​immolation of fellow citizens.

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Interacting with Interviewees

I first met Leila in 2005, when I was looking for a private Arabic teacher. Leila had a degree in literature and taught at a public school. She was interested in teaching from newspapers and Arabic literature. Initially, we met in a culture center downtown Cairo. Later, we met in my flat. As my Arabic improved, we also began visiting her home after finishing class. It took us at least one and a half hours to reach her flat, and she never allowed me to travel there by myself, even during the years preceding the Arab Spring. The neighborhood was a labyrinth of narrow, sandy streets, lined with garbage. Anybody not from there was recognized immediately. When I  last saw Leila in 2014, she told me I  could not visit her anymore because the district had become too dangerous for visitors. When we met, Leila’s dream was to enroll in a PhD program and become a professor of literature at a university. However, she did not have the money to pay for the degree, and she refused to accept support from relatives or friends. Instead, she built a network of students whom she taught Arabic privately. By the time the Arab Spring broke out, she was doing well enough to not only support herself and her mother, but even save some money for trips to Alexandria. Today, she continues to teach, although the numbers of students have declined sharply, forcing her to seek employment elsewhere. I met Ahmed much later, when I traveled to Morocco in 2014. I first contacted him through Facebook and asked if he would be willing to meet with me. He responded within less than ten minutes: “Hello! You are most welcome, we can talk later, I am at work right now.” We met on a sunny evening in front of the main station in Rabat. While crossing the street to find a café, Ahmed introduced himself by saying: “I am not a militant. A militant is someone who is part of a legal organization with political goals. I am not following that path. I am an activist. I act for political goals based on political opinions.” When we sat down and ordered coffee, the first story he told me dealt with the beginning of the Arab Spring in Western Sahara. “It

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was incredible,” he remembered. “I joined, and I  asked others for support. I wrote to the European Union and other external actors.” Ahmed remained at the protest site even when the protests became violent, and people began to flee the town. “Eleven policemen and two civilians died,” he said. “When I heard about these attacks, I stayed. Everyone was leaving. The police arrived and formed a line attacking the people. . . . Now there are systematic arrests and organized repression.” Ahmed watched the youth “cry for freedom and meeting their social and economic demands.” He said it was important for people like him to observe the violence, so that the events could not be forgotten: “I was a witness.” Another interviewee is a well-​known Muslim leader of al-​‘Adl wal-​ Ihsan. I heard about him through my friend, who is a well-​known journalist. I  contacted the leader through Facebook, and he agreed to meet with me within less than two hours. He suggested that we meet in a hotel near the station in Casablanca. The train was filled by a big crowd, which slowly got off and moved into the busy streets of Casablanca. It was hot and the air was dirty, filled with the sound of voices and the noises from traffic. When I entered the hotel, there was suddenly cool air, and the street noise was replaced by soft piano music. There were almost no people: With the exception of one table, the entire restaurant was empty. When the leader arrived, he said that he preferred speaking in Modern Standard Arabic, although he later occasionally switched to French. He introduced himself by saying that he had a doctoral degree and that he had been a political activist since his studies. “All my teachers were leftists. They all encouraged us to protest. The state tolerated this in the 1980s,” he said. “This was my entry into political activism.” In his experience, the Arab Spring first seemed like a repetition of the 1990s. “Suddenly, there was a new international order,” he said. “Tunisia fell within a month. Then Egypt fell within a month. . . . The falling of dictatorships was extremely encouraging. . . . But Morocco is different from Tunisia. . . . Our system is 400 years old.” Another interviewee is a psychologist who has been under state surveillance because of her activism. I  heard about her through

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her boyfriend and contacted her through Facebook. She suggested meeting in a café close to the Hassan Tower, the half-​f inished minaret that was built in the 12th century and never completed. She waited for me outside the building. Remembering her activism, she said she first joined demonstrations to protest for women’s rights. “Living as a woman means living a life of suffering,” she said. During the Arab Spring, she was among the first protestors and spent countless hours on the streets. Together with a handful of activists, she founded the Facebook group MALI. “MALI was the precursor of the revolution,” she said. “It happened quickly—​on Facebook.” MALI contested social norms, such as fasting during Ramadan. “We talked about this to the people, and they were shocked. You go to prison if you do not respect this law,” she recalled. MALI’s first publicized protest was a picnic during Ramadan in 2009. However, the picnic was prevented: When the group arrived at the main station of Mohammadia, they were surrounded by the police. “There were 1,400 policemen,” she remembered. “On horses, in cars, everywhere. It was hallucinatory.” After 45 minutes, she sighed. “Not again,” she said. “There is a man from the secret police watching us. They follow me everywhere, as soon as I  leave the house.” The man sat a few tables behind us. He was tall and bald, wearing jeans and a shirt. He was too far away to hear what we discussed but close enough to observe us. We continued the conversation ignoring the agent. After another hour, a car stopped in front of the café, and the agent got inside. She seemed neither frightened nor intimidated—​“I’m used to this, they have been following every step I make for a long time.” Most of my interviewees in Egypt were old friends, colleagues, or friends of friends. As usual, I spent a few nights with the family of a close friend. When I arrived this time, they pointed out a frame on the wall of the living room: a certificate displaying the amount of money they spent to support the construction of the new tunnel under the Suez Canal. When I told them about my latest research, the husband said: “The Arab Spring was no surprise. There were protests from 2005 to 2010. Mubarak wanted

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his son to become president, but the people did not want that. We are not a monarchy.” About the current situation in Egypt he said: In the West, they always talk about democracy and free elections. We were on our way to democracy when Mursi was elected. The West says we got rid of Mursi in an undemocratic fashion. [But] there were demonstrations every day from November 2012 to June 30, 2013. The country is divided now. We wanted early elections, but Mursi rejected. We wanted the prime minister to step down, but Mursi rejected. We wanted another deputy general, but he rejected. He rejected everything. We were on the brink of civil war. So what happened? The army, the tool of al-​Sisi, came and asked for the wishes of the people to be implemented. They gave Mursi an ultimatum. He rejected. So what had to happen? Another person I interviewed in Egypt was frightened by the behavior of the Egyptian army. When we started talking, she was carrying a towel to clean a room. When someone appeared in front of the window, she suddenly disappeared under a table. “Don’t look at me,” she whispered. “My boss is looking for me.” I  checked my phone and waited until the boss had passed. The woman reappeared from under the table. I asked her about the new government. “Sisi is terrible,” she said. “I know a journalist, who reported on the massacre of Raba’a. She saw how the police shot at the people, from above and from below.” The journalist hid and fled, the cleaner whispered. She glanced around the room. “If you tell this to anybody,” she said, barely audible, “you and I will go to jail.”

OUTLOOK

In this chapter, I have introduced the research design applied in the following study—​ a double-​ paired comparison including protestors and non-​protestors from Egypt and Morocco. I  have moreover introduced the research sample, which consists of 121 reasoning processes identified

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from ethnographic interviews and textual analysis of Facebook. In the next chapter, I  further elaborate on the sample by providing numerous examples that show how beliefs, inferences, and decisions were identified from direct quotes. In Chapter  4, I  first introduce the data analysis by presenting a computational model that systematically traces beliefs and inferences that trigger decisions. Then I  present the findings obtained from applying this model to reasoning processes about the Arab Spring.

NOTES 1. This term is used to refer to the Moroccan governing institution around the king. 2. For example, Springborg (1982, 213) observed: He [Sadat] seemed personally to prefer a system more open than that which had been established by Nasser, although his commitment to democracy did not take precedence over his desire to keep a firm grip on the reins of power. Presumably his calculations were that liberalization would make ruling easier, especially in that as “the hero of the crossing” he had won considerable legitimacy. By elevating himself to the position of supreme arbiter, he could observe the political battles played out beneath him, intervening occasionally to ensure desired outcomes.

3. Note that even voting for opposition parties in prior years has been explained by this logic (Wegner and Pellicer 2014). 4. Ethics approval was issued by the Ethics and Risk Committee at Durham University. I also completed the US National Institute of Health training course “Protecting Human Research Participants.” 5. The importance of Facebook for mobilization is further underlined by the attempts of Arab rulers to censor social media; as previously described, the Facebook account of a Moroccan parliamentarian was hijacked to announce that the protests had been cancelled. In Egypt, the main Facebook group mobilizing for the protests has been suspended since the reign of President al-​Sisi, and Wael Ghonim; its founder, resided in the United States at the time I wrote this chapter.

3

Identifying Beliefs and Inferences

Leila lost many students during the uprisings, which put her livelihood at risk. She continues to feel the negative repercussions until today, but when talking about the Arab Spring, her face still flushed from excitement. Remembering her arrival on Midan Tahrir in 2011, she smiles and raises her voice to exclaim how happy she was in the moment that changed the history of her country. I interviewed Leila in a villa next to the Nile inside the room I was renting at the time. Nobody could see us or listen to our conversation there. We talked for a few hours, touching on many subjects Leila considered when thinking about the Arab Spring. On the one hand, she remembered her youth and commented on how she experienced politics in high school. On the other hand, she remembered the recent past, when President Mubarak had seemed more powerful than ever. By the end of the conversation, we had covered many stages of her life, during which she failed to become politically active—​until the Arab Spring broke out. I interviewed Ahmed in a public café in Rabat, where he often met with his friends. People sitting at neighboring tables could have listened to his story, had they not been preoccupied with their own conversations. Occasionally, Ahmed’s friends passed by. If they pointed to the recorder on the table, he introduced me as a researcher from Germany. We talked for a few hours, during which Ahmed spent most time remembering the protests in Western Sahara, which he considers to be the real beginning of the Arab Spring. We only began talking about other subjects during our next meeting, which took place in the same café and in the same relaxed atmosphere. Hot Contention, Cool Abstention. Stephanie Dornschneider, Oxford University Press (2021). © Oxford University Press. DOI: 10.1093/​oso/​9780190693916.003.0003

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The goal of my interviews with Leila and Ahmed was to identify reasoning processes underlying their decisions to participate in the Arab Spring. Reasoning processes are not immediately visible from direct speech, and I applied qualitative methods developed by Corbin and Strauss (1990) to identify their main components: (i) beliefs, (ii) inferences, and (iii) decisions for actions. I used coding procedures called open and axial coding to construct a coding scheme that consists of belief categories connected by inferences. In this chapter, I  introduce this coding scheme and explain how I constructed it. In the next chapter, I develop a computer program that systematically evaluates the beliefs and inferences identified by the analysis presented in this chapter. The computer program is necessary to conduct the analysis, because the data contain such a large number of beliefs and inferences. Nevertheless, the computer program can only analyze and not identify the beliefs and inferences expressed direct speech. The qualitative analysis presented in this chapter is therefore a crucial part of this research, without which no data would be available.

IDENTIFYING BELIEFS AND BELIEF TYPES

To identify reasoning processes from direct speech, I  draw on Corbin and Strauss’s coding methods. Corbin and Strauss (1990) share basic convictions of political psychologists (e.g., Abelson and Carroll 1965; Axelrod 1976; Simon 1985): They reject determinism and consider political actors “as having, though not always utilizing, the means of controlling their destinies by their responses to their conditions” (419). Consequently, their method is designed “not only to uncover relevant conditions but also to determine how the actors under investigation actively respond to those conditions” (Corbin and Strauss 1990, 419) This makes their method especially appropriate for my study. The two authors develop various techniques to study political behavior, and the following analysis applies two particular coding procedures: Open coding and axial coding. Open coding is “the interpretive process by

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which data are broken down analytically” (Corbin and Strauss 1990, 423). In open coding, parts of direct speech are “compared against others for similarities and differences; they are also conceptually labeled” and “conceptually similar ones are grouped together” (423). In axial coding, “categories are related to their subcategories” to “systematically seek the full range of variation of the phenomena under scrutiny” (423). Axial coding adds a layer of analysis, which eventually allows the researcher to say: “Under these conditions, actions takes this form, whereas under these other conditions, it takes another” (423). To break direct speech into beliefs, I applied open coding. I proceeded by grouping parts of sentences (words, subclauses, main clauses) or entire sentences according to similar and different factors addressed by their propositional contents. In this way, I identified more than one hundred beliefs from hundreds of sentences. Then I  used axial coding to create belief types based on the factors that were addressed by the beliefs. These types generalized what was addressed by beliefs, so that broader factors related to mobilization for the Arab Spring became visible. A detailed overview including examples of quotes is shown by Appendix 2. In total, the coding scheme contains fifteen types of beliefs. These address factors as varied as emotions; state behavior; external conditions; events; capabilities of the protestors, the people, and the state; personal preferences, needs, attitudes towards the state and the people; religion; personality; actions; and non-​state actors. Together, the types include 145 beliefs. The codes for beliefs often include more than one word, such as “strengthArmy,” or “prideNational.” I chose such names because it facilitates the computational analysis presented in the next chapter. I often indicated new words by the use of capital letters. Some codes include adjectives specifying nouns, such as “prideNational,” whereas others include nouns specifying objects, such as “strengthState” or “strengthProtestors.” The coding scheme also captures people commenting on the absence of certain factors, which were assigned codes, such as “NOsatisfaction,” “NOjob,” or “NOstrengthProtestors.” The coding scheme moreover includes contradictory codes, such as “safety” and “NOsafety” or “poorLivingConditions” and “improved

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LivingConditions.” In total, there are 145 codes which contain twenty contradictory pairs. These pairs reflect differences in the beliefs held by individuals. One might assume that contradictory beliefs indicate who decides to protest versus who decides to stay at home. However, the computational analysis, presented in the next chapters, shows that other beliefs are related to differences in decisions for actions (specifically beliefs about emotions). The analysis also shows that contradictory beliefs are held by the same groups of individuals. For example, some non-​protestors hold beliefs about safety while other non-​protestors hold beliefs about a lack of safety. The analysis even shows that particular individuals hold contradictory beliefs. In total, 5 of the 121 reasoning processes underlying protest decisions contained contradictory beliefs. For example, a reasoning process contains a belief that the head of state is making an effort for the citizens, while also containing a belief that the head of state is not making an effort for the citizens. This is possible because the interview from which this reasoning process was identified addresses both the head of state making an effort for the citizens in a context of supporting the poor, as well as neglecting the citizens’ need related to the country’s infrastructure. Another researcher coded a random sample of quotes from the interviews to investigate the reliability of the coding scheme. She coded approximately half of the beliefs (86), with an agreement rate of 85% (73 beliefs). I moreover used the actors’ own vocabulary when assigning codes to ensure that meaning was not lost (see Strauss and Corbin’s “in vivo” codes; Strauss and Corbin 1990, 69).

BELIEFS THAT SPEAK TO THE ARAB SPRING LITERATURE

To describe how I  identified beliefs from direct speech, I  first focus on beliefs that speak to the literature on the Arab Spring, discussed in the Introduction. I then focus on the beliefs that are found to matter most to decisions to join the uprisings, before considering beliefs that are found to be most important to decisions to stay at home.

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Poor Living Conditions

Various beliefs contained by the scheme are of special interest to the Arab Spring literature. Beliefs about poor living conditions, for instance, speak to economic explanations of the Arab Spring. I  identified beliefs addressing poor living conditions from dozens of quotes by protestors and non-​protestors in Egypt and Morocco. I called these beliefs “poor living conditions” and assigned them to the type “external conditions.” I identified a belief that living conditions are poor from Wael Ghonim’s famous call for the January 25th protests. He wrote: “There is poverty and hunger [sentence (S) 1]. I know that 30 million are dreaming of food [S2].” From this quote, I identified a belief about poverty by referring to the words “poverty” and “hunger” (S1) as well as “dreaming of food” (S2). I called this belief “poor living conditions” because of the words “30 million” (S2) and “There is” (S1), which indicate a belief that poverty is spread widely. Some Egyptians responding to Ghonim’s call expressed the same belief. “See what poverty and illness have done to Egypt,” someone proclaimed. I also coded this quote as an expression of “poor living conditions” because of the words “poverty and illness,” which are believed to exist in “Egypt.” I also identified beliefs about poor living conditions from my interviews. As we were talking about my research in a taxi that moved along the Nile, the driver commented on making ends meet. “Living conditions are very difficult [S1],” he said. “Petrol prices are rising [S2].” I  coded these two sentences, which directly followed each other, as “poor living conditions” based on the noun “living conditions,” which are described as “difficult” (S1) and related to the example of rising petrol prices (S2), which pose a great problem for a taxi driver in Cairo. A woman working in a tourist shop, located in a narrow side street in the old town of Marrakech, said: “We have very low salaries in Morocco [S1]. Last year, my whole district had no electricity [S2].” I  also coded these sentences as an expression of “poor living conditions.” I proceeded this way because of the description of salaries as “very low” in the entire country (S1). Moreover, the quote gives an example of an entire district

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being without electricity, which is also a sign of poverty affecting large numbers of people (S2). A young student of engineering, who worked in another tourist shop in Marrakech in his free time, also commented on poverty. When we started to talk, he was too busy to continue the conversation and invited me to return to the shop during his afternoon break. When I returned, we sat down under an umbrella in the backyard. He said: “In the villages, many people have no electricity [S1]. Only the rich have connections [S2].” In my analysis, I coded these sentences as an expression of the belief “poor living conditions” because of the statement that large numbers of people in Morocco are living without electricity (S1). The student moreover contrasted these people with a minority of “rich,” implying that the people without electricity were “poor” instead (S2). At first sight, beliefs about poor living conditions confirm economic theories of the uprisings. Nevertheless, the computational analysis shows that many people who decided to refrain from protesting held beliefs about suffering from poverty—​such as the previously mentioned woman from Marrakech and the taxi driver from Cairo—​and that a significantly larger proportion of non-​ protestors—​ as opposed to protestors—​ held beliefs about suffering from poverty.

Social Media

The beliefs identified by my analysis also speak to social media explanations of the Arab Spring. I  identified a belief representing usage of the new media, including Facebook, Twitter, and al-​Jazeera, which I  coded as “media” and assigned to the belief type “non-​state actors.” Moreover, I identified various beliefs related to collective self-​efficacy, which are also assumed to matter by social media explanations: Beliefs about the revolution in Tunisia, which I called “revolutionTunisia” and assigned to the type “events”; beliefs about the strength of the protestors and people, which I called “strengthProtestors” and “strengthPeople” and assigned to the type “capabilities (self, protestors, people)”; beliefs about the success and skills

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of the protestors, which I called “successProtestors” and “skillsProtestors” and also assigned to the type “capabilities (self, protestors, people)”; and beliefs about feeling hopeful, which I called “hope” and assigned to the type “emotions” (beliefs about emotions are discussed in depth in the following section). In the following paragraphs, I provide examples. An interviewee who participated in the uprisings in Morocco remembered how the Arab Spring first began in Tunisia. “It worked in Tunisia [S1],” he said. “The regime fell [S2]. We were watching them on Facebook [S3].” From this quote, I  identified two beliefs:  a belief about using social media (S3) and a belief that there is a revolution in Tunisia (S1 and S2). I  identified a belief about using social media from the last sentence because it indicates that the speaker used Facebook to follow the protests abroad. I moreover identified a belief that there is a revolution in Tunisia from the preceding sentences because of the words “the regime fell” (S2) related to the events in “Tunisia” (S1). Beliefs that there was a revolution in Tunisia are related to making a difference, and were found to be crucial to decisions to protest in the computational analysis (see Chapter  5), and I  provide further examples of my identification of these beliefs later in this chapter. A protestor I met in Morocco said: “There is the internet, a new vehicle for transmission [S1]. It is more difficult to control [S2]. One can speak more openly [S3]. It has a very large audience [S4].” From this quote, I also identified a belief about using social media because of the reference to the internet (S1). The word “one” (S3) moreover suggests that the speaker is using social media himself, which was also addressed more directly by other quotes, and apparent from how we met—​I sent him a message online. In addition, I identified a belief about the strength of the people from the words “a very large audience” (S4) who can “speak more openly” (S3) while being “difficult to control” (S2). Another interviewee said: “The youths in Tunisia changed the role of the internet [S1]. They made it a means of mobilization [S2]. They did not need strong support [S3].” In this example, I coded the first two sentences as a belief about new media. I proceeded in this way because of the word “internet” related to the actions of the Tunisian youths. I moreover coded

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the last sentence as a belief about strength of the protestors: Given the first two sentences, the last sentence means that the protestors’ use of the internet made them so strong that they did not need support. I also identified related beliefs from my analysis of Facebook entries. An Egyptian responded to the call for protest on January 25th by writing: “It is time to take power from this dictator [S1]. Tunisians were able to do so [S2]. So we can [S3].”1 From the last sentence, I identified a belief expressing hope that change will be achieved (S3). Similarly, I coded the following lines, written by another Egyptian, as a belief about hope that political change will happen: “We will be liberated from oppression. . . . We will not be hurt except for what God destined.” I elaborate more on this particular belief in the following section on beliefs about emotions.

Royal Exceptionalism

The beliefs identified by my analysis also speak to theories of royal exceptionalism. According to these theories, non-​ protestors from Morocco, the monarchy, should have held beliefs about the historical rarity or superiority of their ruler, as well as beliefs about peaceful state responses to protests. However, the analysis instead identifies beliefs about the use of violence to respond to the protests and beliefs about governmental crimes. I elaborate on these beliefs later in this chapter (see the section Beliefs about State Violence versus Crime).

BELIEFS THAT MATTER MOST TO DECISIONS TO JOIN THE ARAB SPRING

Beliefs about Emotions

My analysis identified twelve beliefs addressing emotions. The computational analysis, presented in Chapter  5, shows that four of these beliefs were crucial to decisions to participate in the Arab Spring (but not to

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Table 3.1  Emotions Addressed by Key Beliefs Underlying Decisions to Protest Courage

Hope

Solidarity

National pride

Present protest

Future outcome

Present bonds

Past achievement

engagement in

of protest

with protestors

of nation

spite of risks

decisions to stay at home):  beliefs about hope, courage, solidarity, and national pride. Many researchers consider emotions important because they “initiate and guide goal-​directed behavior,” such as voting for a certain candidate, cooperating with a party, or resisting a government (Williams and DeSteno 2008, 10007; also see Barrett and Campos 1987; Cosmides and Tooby 2000). The finding that decisions to protest were triggered by beliefs about emotions, whereas decisions to refrain from protesting were not confirms this view. The following section elaborates on each of the beliefs about positive emotions that are found to matter to the Arab Spring, namely hope, courage, solidarity, and national pride. Each of these emotions relates to different aspects of a behavior. Table 3.1 offers a broad summary, while the following sections elaborate on the identification of each belief in depth.

Beliefs about Hope

Psychologists have considered hope “a forward-​looking emotion,” which “encompasses optimism and other positive points of view” (Richman et al. 2005, 423). Hope “involves expectation and aspiration for a positive goal, as well as positive feelings about the anticipated outcome” (Halperin and Gross 2011, 1230). It “facilitates goal setting, planning, use of imagery, creativity, and even risk taking.” It can “liberate[] people from fixed—​and limiting—​beliefs” and “motivate[] people to change their situation” (1230).

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Since hope is future-​related, it is experienced in situations of uncertainty. In these situations, individuals may experience hope even when there is no empirical support for a positive outcome. A  student can be hopeful about passing an exam without having passed an exam before. A politician can be hopeful to be elected President even though opinion polls suggest otherwise. The analysis below shows that people were hopeful about the outcome of their protest, even though there was no example of successful mass uprisings in recent memory. When analyzing direct speech, I identified numerous quotes that indicate hope. I coded these quotes as instances of “hope” and assigned them to the belief type “emotion.” When identifying hope from direct speech, I focused on three main aspects acknowledged in the literature: forward-​ looking, optimistic, and risk-​taking. For example, I coded the following quote by an Egyptian protestor as a belief about hope: “If there are 1,000 today, there will be 10,000 tomorrow [S1]. The barrier of fear will disappear [S2].” The forward-​looking aspect of hope is indicated by the use of the future tense (“will”). Optimism is indicated by sentence 1, which says that protests will grow bigger, and by sentence 2, which says that the barrier of fear will disappear. Sentence 2 also addresses risk-​taking by implying that protesting is considered a frightening activity. Similarly, I  coded the following quote by a Moroccan protestor as hope: “It seemed easy [S1]. The context was very encouraging [S2]. I was convinced the demonstrations would succeed [S3].” The words “easy,” “encouraging” and “succeed” express optimism related to the outcome of the protests. The use of “would” in the third sentence moreover establishes a link to an event that was anticipated to happen in the future. When asked about the protests happening in Rabat, another Moroccan said: “I am convinced that totalitarianism will disappear [S1]. For me it is not a question if that will happen, but how that will happen [S2].” This quote also expresses optimism about the outcome of the protests. It moreover contains a forward-​looking component, which is indicated by the use of the future tense (“will”), and it suggests that protesting is a risky behavior by saying that the target is “totalitarianism” (S1). I also coded this quote as hope.

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I previously mentioned two other examples coded as hope: “It is time to take power from this dictator [S1]. Tunisians were able to do so [S2]. So we can [S3].” and “We will be liberated from oppression. . . . We will not be hurt except for what God destined.” The first example expresses optimism by stating that Egyptians are able to do what the Tunisians did (S3). It is directed to the future because it expresses a goal that has not yet been achieved (S1). The quote also refers to risk-​taking because it expresses the goal of removing a “dictator” (S1). The second example also expresses optimism about the outcome of the protests (“We will be liberated”). The forward-​looking aspect of hope is indicated by the use of the future tense (“will”). The quote also addresses risk-​taking by suggesting that protest could be related to being injured (“We will not be hurt except for what God destined”). A protestor from Morocco, who had been a political activist for most of his life, said: “History creates waves of protests, East Europe, Latin America [S1]. I thought that the Arab Spring would come to Morocco [S2].” From this quote, I identified a belief about hope (S2), and an additional belief about historical events (S1). I identified hope from sentence 2 because it expresses optimism regarding the development of the Arab Spring. It also refers to the future by addressing a development that has not yet taken place (“the Arab Spring would come to Morocco”). I furthermore coded the first sentence as a separate belief called “history” because it expresses a belief that protests are no isolated incidents but repeated in the context of history. I assigned this belief to the type “external conditions.”

Beliefs about Solidarity

Solidarity is an emotion that is directed at another person or group of people. Individuals who experience solidarity “like each other and feel psychologically close” (Diefendorff, Morehart, and Gabriel 2010, 123). They “identify” with each other (Abizadeh 2005; Summers-​Effler 2002). Solidarity can occur between very different groups of people. It can exist between friends and family members, as well as between people who do

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not know each other personally. In the words of Abizadeh, there can be “human” solidarity, “cosmopolitan” solidarity, or “European” solidarity (2005, 58). Sociologists have pointed to the importance of solidarity for mobilization for many decades (e.g., Collins 1990; also see Summers-​Effler 2002). My analysis confirms this observation: Beliefs about feeling solidarity with protestors and fellow citizens are found to be one of the most important beliefs triggering decisions to mobilize. Beliefs about feeling solidarity were also found to be unique to protestors and not shared by non-​protestors. I identified solidarity from expressions that indicate psychological closeness and identification with other individuals (groups of individuals). For example, an Egyptian commented on three protestors who had set themselves on fire. The government was calling the self-​ immolators psychopaths, and the Egyptian asked her fellow citizens: “If they were psychopaths, why did they burn themselves in front of the parliament [S1]?” She said:  “This entire government is corrupt [S2]. These self-​immolators were not afraid of death [S3]. Can you imagine that? [S4] Are you going to kill yourselves, too? [S5] Or are you completely clueless? [S6]” From this quote, I identified three beliefs: one about solidarity with the protestors, which I called “solidarity” and assigned to the type “emotion,” another belief that there are people sacrificing themselves, which I  called “self-​sacrifice” and assigned to the type “actions (self, people, protestors),” and a belief that the government is committing crimes, which I called “crime state” and assigned to the type “state behavior.” I coded sentences 3 to 6 as solidarity because they indicate psychological closeness between the speaker and the dead protestors. Not only does the speaker identify with the self-​immolators herself. Rather, she forces her audience to identify with them, too, by asking if they would have done the same (S4 and S5). She also directly addresses her audience by the word “you,” rather than asking “who would have performed this behavior?”—​ strengthening the connection with both audience and self-​immolators (S4 and S5). Moreover, sentences 1, 3, 5, and 6 convey a positive picture of the self-​immolators, which emphasizes psychological closeness between the

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speaker and the protestors. Specifically, the sentences say that the protestors sacrificed their lives for a political purpose: The first sentence introduces the political purpose by stating that the people set themselves on fire in front of the parliament. The third sentence states that the protestors were willing to die for that purpose. The last sentence underlines that the protestors had a purpose by implying that they “have a clue” as opposed to being “clueless” like the people who are not willing to set themselves on fire (S5 and S6). I identified another belief about solidarity from a quote by an Egyptian:  “The police is attacking and beating us [S1]. Any attack will be resisted for the protection of the protestors [S2].”2 The first sentence applies the word “us” when referring to the victims of police violence. This indicates psychological closeness and identification with the protestors. The second sentence furthermore states that there will be a response to state violence (“any attack will be resisted”) to protect the protestors (“for the protection of the protestors”). This expresses more than identification with the protestors: It indicates the willingness to act against the police to protect the protestors—​a very risky behavior that could be life-​threatening. This is a strong indication of psychological closeness and liking.

Beliefs about Courage

Courage is an emotion related to the performance of a challenging behavior or the endurance of a difficult situation. Some analysts assume that courage belongs to “the psychology of fear” (Rachman 1990). In the words of Sinclair, courage is “the quality shown by someone who does something difficult or dangerous, even though they may be afraid” (in Szagun and Schauble 1997, 291). Other psychologists believe that life itself is “reasonably conceptualized as the ongoing experience of stressful circumstances” which “require courage” (Maddi 2006, 306). They have defined courage as “the strength to face stressful situations” (Maddi, 306). In this study, courage is related to participation in protest as a risky behavior, rather than life in general. In this way, I  follow more narrow

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definitions of courage and relate them to a particular type of challenging behavior:  protest. I  identified numerous quotes in which protestors expressed beliefs about feeling courageous. The computational analysis presented in the following chapter shows that these beliefs were unique to protestors and crucial to decisions to join the uprisings. I identified courage from expressions that indicate the willingness to engage in a risky or stressful behavior. For example, an Egyptian responded to the call for the main protest on Facebook by saying: “There has to be a revolution on January 25 against Mubarak and the watchers of the Zionist system [S1]. There must be a force to retreat from oppression [S2]. If we die, we will die to protect our country in search of a decent future for our children [S3].”3 From this quote, I identified courage because it mentions the possibility of dying (subclause, S3) and the willingness to participate in protests in spite of this risk (main clause, S3). The famous call for the January 25th protest also contains a quote expressing courage. In it, Wael Ghonim writes:  “I am willing to sacrifice myself.” Sacrificing oneself indicates an action that is very difficult and stressful. “I am willing” indicates that he nevertheless believes to be ready to perform the action. An Egyptian responded to Ghonim’s call by saying: “Maybe there are infiltrating thugs to prevent the revolution [S1]. When you encounter a thug, don’t be afraid or worried [S2]. Try to hold onto him with the others around you and tie him against an electricity poll [S3].” This quote specifies the situation and also expresses courage:  The first two sentences describes the situation: being confronted with thugs. The last sentence expresses a willingness to confront the situation: fighting the thug by tying him up. Moreover, the main clause of the second sentence comments on the absence of fear, which can also be interpreted as a sign of courage. Another person responded to the call for protest by saying: “I am not afraid of not returning.” This quote also expresses courage. “Not returning” expresses the possibility of dying in the protests, which is also addressed by the call itself. Although the individual does not explicitly link “not returning” to “the protests,” it is clear that he is referring to protests because he is responding to the call for protests. Courage is expressed by the words

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“I am not afraid,” which indicate that the person believes he will participate in the protests in spite of the dangers it involves. Another protestor, who commented on an earlier demonstration, said: “Many were injured [S1]. . . . Freedom is not without blood [S2]. I was ready to die [S3].” I identified a belief about feeling courage from the third sentence, “I was ready to die.” This sentence expresses the individual’s willingness to participate in very dangerous behavior, which could lead to death. The quote moreover contains information about the danger of the action:  sentences 1 and 2 indicate that people who participated experienced a form of violence—​they were “injured” and there was “blood.” The perpetrator of violence is implied by the second sentence, which states that “freedom is not without blood.” The actor resisting people’s freedom in the protests was the government, which is why I coded sentences 1 and 2 as a belief that the state was using violence against the people (“violenceState”). I assigned this belief to the type “state behavior.” Further examples of this belief are provided later in this chapter.

Beliefs about National Pride

Pride is related to actions that have been performed to the satisfaction of a person. Unlike hope, it is not directed at the future, but at the past. And unlike courage, it is not directed at performing a difficult action with the outcome still being uncertain, but at the successful performance of actions. In the words of Williams and DeSteno (2008, 1007), pride is “a positive, self-​conscious emotion arising from achievements that can be attributed to one’s abilities or efforts.” Pride also includes others’ evaluations of one’s action. It is an emotion “generated by appraisals that one is responsible for a socially valued outcome or for being a socially valued person” (Mascolo and Fischer in Williams and DeSteno 2008, 1007). Psychologists have shown that pride is evoked most strongly in situations where there is public praise (Mascolo and Fischer, 1007). Pride can have positive effects on the engagement in behavior, such as participation in political protest. It can be “an incentive to persevere on a

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task despite initial costs” (Williams and DeSteno 2008, 1007). Nevertheless, pride can also support existing inequalities and hierarchies by helping to “establish and maintain social hierarchies, allowing certain individuals to signal positions of strength” (Oveis, Horbert, and Keltner 2010, 618). In this context, it “facilitates the acquiring, sustaining, and signaling of social status” (Cheng, Tracy, and Henrich 2010, 335). In this study, I  find that pride is related to mass mobilization challenging existing hierarchies. It therefore demonstrates the positive role that pride can play in behavior. The protestors I interviewed and analyzed online expressed pride related to their nation (Egypt and Morocco), rather than to their achievements as individuals. Consequently, I have coded their statements as “nationalPride.” The nation is a “political unit” (Gellner and Breuilly 2008, 1), which is used for “classifying groups of human beings” (Hobsbawm 1992, 5). Usually, classification is linked to “territory” and commonalities between people, such as history, language, or habits (5). However, state boundaries often fail to satisfy the principle “that the political and national unit should be congruent” (1). Therefore, the concept of the nation has been closely linked with that of nationalism, which can be defined as the “feeling of satisfaction aroused by [the] fulfillment [of the principle that the political and national unit should be congruent]” as opposed to “the feeling of anger aroused by the violation of the principle” (1). National pride presupposes satisfaction with state boundaries, as achievement as a nation can only be celebrated when a nation is believed to exist. Nations can be essential to the individual’s “social existence” and even to their “individual identification” (Hobsbawm 1992, 5). This makes national pride similar to solidarity, which also involves identification with others. However, solidarity indicates closeness and liking, whereas pride expresses accomplishment and achievement. Moreover, protestors felt solidarity with their fellow citizens and other protestors, whereas national pride addresses a larger group of people. This was also reflected by the vocabulary of the actors when expressing national pride (e.g., “Egypt” or “our country”). I identified national pride from quotes that comment on achievements by the speakers’ home countries, or from expressions of praise for the home country. In his call for protest, Wael Ghonim writes: “I am a citizen

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of Egypt [S1]. Do you know Egypt? [S2] Egypt who challenged and defeated Israel in 1973 [S3] . . . Egypt who challenged England [S4] . . . Egypt who expelled the French [S5].” The first sentence indicates identification with Egypt. The following sentences (S3–​S6) express praise for the country by naming events that are considered great achievements in national history—​military defeats over others. The speaker furthermore repeats the name “Egypt” in every sentences, further emphasizing praise. Many individuals responded to the call by praising Egypt. Someone said: “Let us sing to our country, our country.” I also coded this quote as national pride, since singing expresses praise, and “our country” expresses the speaker’s identification with Egypt. Some individuals responded by writing down lines of poems and songs. One individual said:  “Any star in the sky is submissive to you, free country.” I also coded this sentence as national pride. The sentence expresses praise by using the metaphor of the sky where the country is the most important element to which every star is subjected. The sentence also expresses praise by addressing the country as if it were a person (“to you, free country”). The adjective “free” related to “country” and expresses an achievement worthy of pride in a context of governmental crime and violence (addressed by the main call for protest, see preceding section). The analysis presented in the following chapter shows that beliefs about national pride were crucial to protest decisions and contained by a significantly larger proportion of reasoning processes by protestors than non-​protestors. Nevertheless, beliefs about national pride were not unique to protestors. For example, a young non-​protestor who worked in a mobile shop in Rabat, she said: “I am proud to be Moroccan [S1].” I asked her why, and she said: “The king is the best [S2]. We love him [S3]. He does everything for us [S4].”4

Beliefs about Protests, Revolutions, and Self-​Sacrifices by Fellow Citizens

Apart from emotions, there are other factors that play a major role in decision-​making about the Arab Spring, namely, protests at home,

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revolutions abroad, and self-​sacrifices by fellow citizens. I identified beliefs about these factors from various quotes. For example, an Egyptian woman said: “Four Egyptians have set themselves on fire [S1] to protest humiliation and hunger and poverty and degradation [S2], thinking maybe we can have a revolution like Tunisia [3]‌.” From this sentence, I identified a belief about fellow citizens sacrificing themselves (“selfsacrifice”), as well as a belief that a revolution has happened abroad (“revolutionTunisia”). I assigned the belief that fellow citizens are sacrificing themselves assigned to the type “actions (self, protestors, others)” and the belief that there is a revolution abroad to the type “events.” I furthermore identified an additional belief that living conditions are poor (part 2 of the sentence), which I assigned to the type “external conditions.” I identified the belief that fellow citizens are sacrificing themselves from the main clause about people setting themselves on fire with the goal of protesting against poor living conditions (parts 1 and 2 of the sentence). Specifically, I identified the action described by the main clause as a self-​ sacrifice—​rather than suicide or another behavior—​because the speaker says that the actors’ goal is to protest against poor living conditions (part 2 of the sentence). I moreover identified the people who set themselves on fire as fellow citizens by referring to the word “Egyptians” (part 1 of the sentence). I identified the belief that there is a revolution abroad from the words “a revolution like Tunisia” (part 3 of the sentence). Another example of a direct quote from which I identified a belief about a revolution abroad is contained by the opening paragraphs of this book: “I switched on the TV [S1]. The Egyptian channel was showing a beautiful woman standing next to the Nile [S2]. Then I  changed to al-​Jazeera and saw masses of people celebrating the revolution in Tunisia [S3].” I identified a belief that a revolution is happening abroad from the third sentence about people celebrating, which contains the word “revolution” in reference events that were happening in a neighboring country (“Tunisia”). I  moreover identified a belief about using new media (“media”) from sentences 1 and 3, which indicate that the individual watched satellite TV.

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The opening paragraphs of this book also contain a quote from which I identified a belief that protests are happening at home: “I called a friend and he told me:  ‘It is very strange [S1]. Everyone is participating [S2].’ Before, there were always a few protestors, but never all the people [S3]. Out of the blue, everyone showed up [S4].” I  identified the belief that protests are happening at home from the word “protestors” (S3) in relation to “everyone is participating” and “everyone showed up” (S2 and S4). From the first sentence of this quote, I moreover identified a belief about interacting with a friend (“friends”), which I assigned to the type “non-​ state actors.”

Families and Public Support

Protestors also expressed beliefs about their families, which were found to be antecedents of positive emotions in the analysis. Protestors either mentioned having supportive families, or they simply mentioned that they had a family. I coded both types of quotes as “family,” which I assigned to the type “non-​state actor.” A young man from Morocco said: “My parents have always supported me.” I coded this quote as a belief about his family supporting him (“family”). I identified the same belief from a quote by an Egyptian, who said: “I have two girls, the oldest is four [S1]. When my girls grow up, they will boast that their father played a role in the liberation from oppression and corruption [S2].” As opposed to the protestor from Morocco, this person talks about his children rather than parents—​who are also part of family. I did not introduce codes for beliefs about parents or children in particular to avoid having an even larger number of codes, which would have been related to few occurrences of beliefs. Protestors also expressed beliefs that there was public support for the protests, which were contained by a significantly larger proportion of reasoning processes by protestors than non-​protestors. A Moroccan said: “The protests spread very quickly.” I coded this sentence as a belief that there is public support for the protests (“supportPRO”), which

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I  assigned to the type “capabilities of protestors.” The description that the protests “spread very quickly” means that many more people were joining in a short period of time, which implies that large parts of the population were supporting the protest. Another protestor from Morocco said: “Families protested against the authorities [S1]. Suddenly, tens of thousands joined them [S2].” From this quote, I identified a belief about public support for the protest referring to the assertion that large numbers of people joined (S2) the protests that were started by some families (S1). As described by these examples, the belief that there is public support for the protestors indicates individuals’ beliefs about what others did or believed. Personal support by the individuals for the protest is captured by another belief called “approval protestors,” which I assigned to the type “personal preferences.” I provide further examples of my identification of this belief below (“personal preferences”).

Beliefs about State Violence versus Crime

Individuals also expressed beliefs that the state was attacking the people. I coded these quotes as a belief that the state was using violence, which I  assigned to the type “state behavior.” The computational analysis presented in the next chapters shows that both protestors and non-​ protestors believed that the state was violent. Beliefs about state violence and crime could trigger beliefs about positive emotions of courage to face the state and solidarity with protestors, which were crucial to decisions to protest but not to decisions to stay at home. The examples I present in this section focus on quotes by protestors. A protestor from Egypt said: “Three armored cars of riot police, and tens of hired thugs, and officers came down to terrorize us.” From this quote, I identified a belief about violence by referring to the words “armored car,” “riot police,” “thugs” and “terrorize.” According to the quote, the perpetrator of violence is “the police” and “officers” as well as “hired thugs.” Since the state (or the government more specifically) determines

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the actions of the police and officers, I coded the quote as a belief about state violence, rather than violence by the protestors or other actors. Concerning thugs, the adjective “hired” following the mentioning of “riot police” indicates that thugs were hired to support the police and do not represent an additional perpetrator. Similarly, a protestor from Morocco said:  “The police attacked the protestors as they were leaving.” From this quote, I identified a belief about violence by referring to the word “attack.” The quote also specifies the perpetrator of the attack—​the police. As in the previous quote, I coded this as a belief about state violence because the police obey the commands of the government. In Egypt, many protestors also commented on the brutal murder of Khalid Sa’id, which I described in the previous chapter. As discussed, his violent death is also considered the origin of the major Facebook group through which Egyptians mobilized (Kulana Khalid Sa’id). Protestors also discussed the murder offline:  “We remember the killing of Khalid Sa’id [S1],” someone said. “He is a victim from Alexandria [S2]. They are protesting for him [S3]. May God support them [S4]. It is their right to protest [S5]. There is tyranny [S6].”5 From this quote, I  identified a belief about violence from sentence 1, which addresses the killing of Khalid Sa’id. It is common knowledge in Egypt that Khalid Sa’id was beaten to death by Egyptian policemen, which is why I  coded the belief as state violence, rather than an act of violence perpetrated by another actor. I moreover coded sentence 6 as a belief about state violence. I proceeded this way because of the word “tyranny,” which was used just a few sentences after mentioning the murder of Khalid Sa’id: This suggests that there are more acts of violence against the people perpetrated by the state. By contrast, I did not code sentences as beliefs about state violence if they did not contain words referring to the use of physical force. For example, a protestor from Morocco, who is a well-​known member of the political opposition, said: “We needed force [S1]. The state security tried to prevent the protests [S2]. My Facebook account was hijacked before February 20th [the day of the main protests in Morocco] and somebody

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said the protests had been cancelled under my name [S3]. There was a danger that the youths would be suppressed [S4].” I coded sentences 2 to 46 as a belief about state crimes (“crimeState”), which I also assigned to the type “state behavior.” I proceeded in this way because there was no indication that the person believed the state would use physical force against the people. Rather, the sentences describe a criminal behavior that is nonviolent. This behavior is indicated by the words “hijacked,” “somebody said the protests had been cancelled under my name,” and “suppress.”

BELIEFS RELATED TO DECISIONS TO STAY AT HOME

The computational analysis, presented in the next chapter, shows that beliefs about living in safety mattered most to decisions to stay at home. In the following, I provide examples that show how I identified these beliefs from direct quotes. I also provide examples related to other beliefs that played a minor role in the reasoning processes of non-​protestors: beliefs about personal priorities, positive personality of the head of state, improving living conditions, being employed, and negative personality of fellow citizens.

Beliefs about Safety

I identified beliefs about safety (“safety”) from numerous quotes by non-​ protestors, and assigned these beliefs to the type “external conditions.” A  retired Egyptian said:  “The police protect the system [S1]. If 30 policemen are killed, we must be afraid [S2]. There are more important things than protest: stability and safety [S3].” I coded sentence 3 as a belief that there is safety because of the words “stability and safety.” I identified two beliefs from sentences 1 and 2: A belief that the protestors are violent (“violenceProtestors”), which I assigned to the type “actions (protestors, people, self),” and a belief about state approval (“approvalHeadOfState”),

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which I  assigned to the type “attitudes toward state.” I  coded sentences 1 and 2 as a belief expressing approval of the state because they express support for police actions and fear of attacks on the police. I  moreover identified a belief that the protestors are violent from sentences 2 and 3: Sentence 2 suggests that policemen are being killed, while sentence 3 suggests that protests are a threat to stability and safety. Another Egyptian said: “Western powers have bad intentions [S1]. They have had a plan to divide Egypt into four parts for decades [S2]. We do not want to become like Syria and Iraq [S3].” I coded sentence 3 as a belief that life in Egypt is safe because it expresses a belief that Egypt is not like Syria and Iraq—​which were known as places that were experiencing violence at the time of the interview. I moreover coded sentences 1 and 2 as a belief about the international environment (“internationalEnvironment”), which I assigned to the type “external conditions.” International environment is a rather general code abstracting what is said in sentences 1 and 2. Rather than choosing a narrower code, such as “Western intervention,” I  chose “international environment” because, overall, there were few quotes in which individuals talked about factors outside the Middle East. In general, I tried to keep the total number of beliefs limited and avoided identifying beliefs that occur only once. A Moroccan who did not participate in the protests described the situation in her country in the following words: “Think of this image: there is a monster who lives in a forest filled with animals [S1]. Every day, the monster goes to the forest and eats one animal -​only one, not all [S2]. We need to change the system [S3]. But there is peace [S4]. The most important thing is to have this peace [S5]. Having the monster is better than eternal war [S6]. The monster only eats one per day [S7].” This quote uses a metaphor to describe life in Morocco (S1 and S2). This metaphor indicates that the speaker believes that the existing order, addressed as “the system,” needs to be changed (S3). I  called this belief “needChange” and assigned it to a type called “needs.” The following sentence expresses a belief that in spite of needing to change the system, there is peace (S4). I coded this sentence as a belief that life in Morocco is safe because of the word “peace,” which implies safety.

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What is interesting about this quote is that it expresses a belief that life is safe in spite of the state using violence against the people: The first two sentences compare the system to “a monster” and the people to “animals” who are eaten by the monster one by one. I coded these sentences as a belief that the state is using violence against the citizens (“violenceState”). The comparison between the king and the monster, and the people and animals is established by sentences 3 to 6, which address the current situation in Morocco (“the system”). Finally, the quote expresses a belief that keeping “peace” is more important than changing the system (S6) because it involves only one person dying per day as opposed to “eternal war” (S6 and S7). I coded these sentences as a belief that living in safety is more important than protest (“prioritySafety”) and assigned this belief to a type called “preferences.” Another Moroccan said: “Protests bring war and violence [S1]. We do not like the revolution in Egypt [S2]. We live in peace [S3]. There is no war [S4].” Sentence 3 of this quote is almost identical to sentence 4 of the quote in the previous example. Sentence 4 moreover repeats the same contents (in the negated form, by stating that there is no war). I coded these two sentences as a belief that there is safety because safety is implied by peace and the absence of war. I moreover coded sentences 1 and 2 as a belief that protests are not desirable (“NOsupportProtestors”). I proceeded this way because the first sentence links protest to “war and violence,” whereas the second sentence expresses dislike for mass mobilization in Egypt. When describing life in Morocco, a woman who did not participate in the protests said: “We have the king [S1]. You can see on TV how he is with children [S2]. He is modest, he is a king of the people [S3]. We can move freely on the streets without fear [S4].” When analyzing this quote, I identified sentence 4 as a belief that life in Morocco is safe: The woman believes she can go wherever she wants without feeling afraid, meaning there are no attacks or other events that could threaten her when going outside. The word “streets” suggests that she does not believe safety to be restricted to a particular area or town but related to public space in general. The word “we” rather than “I” indicates that she believes everyone is safe from threats in public. I  moreover identified additional beliefs

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about the personality of the head of state from sentence 1 (see later section Beliefs about Personality).

Beliefs about Personal Priorities and Employment

Non-​protestors also expressed beliefs about their personal priorities as well as about being employed. A young Moroccan woman said: “We have unemployment [S1]. They are unemployed [S2]. They protest [S3]. It is their right [S4]. It is a human right [S5]. It is a civil right [S6]. I studied law [S7]. I have a license in commerce [S8]. I work in a real estate agency [S9]. I am not against protest [S10]. I only have one day off per week [S11]. I want to relax on that day [S12].” I identified a belief about personal priorities from the last two sentences (S9 and S10), which describe how the woman wants to spend her free time: resting. By contrast, the preceding sentences express support for the protestors, which is why I coded them as a belief about approval of the protestors (S10:  “I am not against protest,” and S4–​S6:  “It is their right. It is a human right. It is a civil right.”). In this context, the woman’s comment that she likes to relax on the weekend means that even though she supports the protestors, she has other priorities in her free time. I moreover coded sentence 9 of this quote as a belief about being employed because it describes the woman’s job, while I coded sentences 1 and 2 as a belief that living conditions are poor (“poorLivingConditions”) because they mention unemployment and sentence 3 as a belief that there are protests against the government (“protest”). A non-​protestor from Egypt also talked about her employment and personal priorities when talking about the Arab Spring:  “I had a lot of work for my job [S1]. I was too busy to participate [S2]. Protest is a question of priorities [S3].” I coded sentences 2 and 3 as a belief about personal priorities because they state that the woman was “too busy” to join the Arab Spring, and characterize participation in the uprisings as a “question of priorities.” I  moreover coded sentence 1 as a belief about being employed.

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Another non-​protestor from Morocco said: “The king has a lot of power [S1]. I want the King to have less power [S2]. I want the King to be a representative, like in the UK [S3]. I know everyone at the university [S4]. I have access to the Movement of 20th February [S5]. Movement 20th asked for the King to be a representative [S6]. I have my family here [S7]. I have my job [S8]. I focus only on what is important to me personally [S9].” I coded the last sentence of this quote as a belief about personal priorities because, in the context of the previous sentences, it expresses that his family and job (S7, coded as “family,” and S8, coded as “job”) are more important than the protests organized by Movement 20th (S6, coded as “protests”). His personal priorities are surprising because he also expresses support for the goals of the protests (S3 and S6, coded as belief about approving of the protestors; approvalProtestors”) and mentions that he has access to the protestors (S4 and S5, coded as a belief about interacting with protestors; “interactProtestors”). A young Egyptian mother I interviewed remembered January 25th, the main day of protest in Egypt, in these words: “I had a baby [S1]. I could not leave my baby at home [S2].” I coded sentence 2 as a belief about personal priorities because it states that she could not join the protests because of her baby. I moreover coded sentence 1 as a belief about her family (“family”) because it refers to a family member. Another Egyptian mother had similar thoughts when remembering 2011. She said: “I have a family [S1]. I have children [S2]. During the revolution, they announced there might be a shortage of food [S3]. My main concern was to secure food for my children [S4].” I coded sentence 4 as a belief about personal priorities because it states that her main concern during the Arab Spring was not participation in the protests but securing food for her family. I moreover coded sentences 1 and 2 as a belief about her family and sentence 3 as a belief that protests were happening in Egypt. Other non-​protestors expressed beliefs about their employment. Talking about the protests that were happening in Rabat, a young Moroccan woman said: “I have a job [S1]. I have no reason to join [S2].” I coded the first sentence of this quote as a belief about being employed (“job”), which I assigned to the type “external conditions.” I moreover coded the second

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sentence as a belief that there is no need to protest (“NOneedProtest”), which I assigned to the type “needs.” Another Moroccan also mentioned her work when asked about the protests. “I have work [S1],” she said. “I like my work [S2]. I  am happy [S3].” I coded the first sentence as a belief about being employed. I coded the second and third sentences as a belief about feeling satisfied (“satisfaction”) because of the words “like” and “happy.” These words express that the person believes to feel satisfied with her job (S2), and with her life in general (S3). An Egyptian taxi driver said:  “Muabarak had a nice personality [S1]. He knew what he was doing [S2]. Mubarak was good [S3]. If you need work, you can go and search for it [S4]. There was no unemployment [S5].” I coded sentence 5 as a belief that jobs are available (“jobAvailability”) and assigned it to the type “external conditions.” I moreover coded sentence 4 as a belief about being employed because it asserts that anybody can have a job. This includes the speaker, whose job was also apparent from the situation in which we met (I was sitting in his taxi). Finally, I coded sentences 1 and 2 as a belief about the personality of the head of state (see next section). In Morocco, I interviewed a man selling nuts on the corner of a major street in Rabat, where tens of thousands had protested on February 20, 2011. I asked the man if he had joined the protests that day. He responded: “They were walking on this street [S1]. I was in my shop [S2]. I have to be here [S3].” I coded sentences 2 and 3 as a belief about being employed because they state that he has a job (which was confirmed by the situation in which we met: I interviewed him in his shop). I moreover coded the first sentence as a belief that there were protests in his home country (“protest”), which I assigned to the type “events.” Other non-​protestors expressed beliefs about being employed in relation to beliefs about being ignorant. An Egyptian said: “I did not know anything about January 25th [S1]. I was working in a school [S2]. I was suffering from ignorance (S3].” I coded sentence 2 as a belief about being employed, as it describes the person’s job. I moreover coded sentences 1 and 3 as a belief about ignorance (“ignorance”) because it expresses a lack

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of knowledge. The first sentence relates ignorance to the protests, whereas the third sentence comments on ignorance more generally. A Moroccan had similar thoughts. “There were demonstrations [S1]?” she asked when I mentioned the protests. She said she was not going to participate in ongoing protests, either, commenting on her job search: “I searched for a year [S2]. A  friend of mine told me about this job [S3]. I have this job [S4]. I would like a better salary [S5]. I have to work all the time [S6].” I coded sentence 4 as a belief about being employed because it states that she has a job. I moreover coded sentences 2, 5, and 6 as a belief about making an effort (“effortSelf ”). I  proceeded this way because these sentences express a belief about trying hard to get a job (S1) and a promotion (S6 and S7). Finally, I coded the first sentence as a belief about ignorance because it expresses the absence of knowledge that mass mobilization was happening.

Beliefs about Personality Traits

Non-​protestors also expressed beliefs about the personality traits of the head of state (positive) and fellow citizens (negative). Beliefs that the head of state was a good person were especially important to decisions against joining the Arab Spring (see Chapter 5 of this volume). When describing life in Morocco, a non-​protestor said: “We have the king [S1]. You can see on TV how he is with children [S2]. He is modest, he is a king of the people [S3]. We can move freely on the streets without fear [S4].” I have previously described how I identified a belief that life is safe from this quote. In addition, I identified a belief that the king is a good person (“goodPersonalityHeadOfState”) from sentences 1 to 3. I assigned this belief to the type “personality state.” Sentence 1 indicates a belief that there is a king or head of state. Sentences 2 and 3 describe the personality of the king by commenting on “how he is with children” and asserting that he is “modest” and “a king of the people.” I coded these sentences as a belief that the head of state is a good person, rather than as two separate beliefs that there is a head of

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state and that the head of state is a good person. I proceeded this way to avoid identifying an even larger number of beliefs (see Appendix 2 for an overview of all beliefs) and to avoid identifying beliefs that are held by single individuals. Another example is a quote by an Egyptian taxi driver said: “Muabarak had a nice personality [S1]. He knew what he was doing [S2]. Mubarak was good [S3]. If you need work, you can go and search for it [S4]. There was no unemployment [S5].” I have previously described how I identified beliefs about unemployment from this quote. However, I also identified two other beliefs from this quote—​a belief that the head of state is a good person (“goodpersonalityHeadOfState”), which I  assigned to the type “personality state,” and a belief about approving of the head of state (“approvalHeadOfState”), which I assigned to the type “attitudes toward state.” I identified a belief that the head of state is a good person from the first sentence, which states that the former president has a “nice personality.” Sentence 2 moreover comments on his skills in office, whereas sentence 3 expresses judgment: He was “good.” I coded sentences 2 and 3 as an additional belief about approval of the head of state. I identified opposite beliefs about the character of fellow citizens (“badPersonalityPeople”), which I assigned to a type called type “personality (protestors, people, self),” from various quotes by non-​protestors. The computational analysis showed that these beliefs could trigger decisions to stay at home, although they were not as important as the previously described beliefs. A Moroccan shoe seller, whom I met during a walk in the Old City of Marrakech, pointed at the houses and shops, saying: “Everyone here lives well [S1]. But they do what they want, asking for more salary [S2]. One demonstration leads to the next, and there is killing, war, and blood [S3].” From this quote, I identified a belief that fellow citizens have a bad character from the assertion that “they do what they want” and “ask for more salary” (S2), although they live well (S1) and although their behavior leads to war (S3). I moreover coded sentence 1 as a belief that living conditions are good (“goodLivingConditions”) and sentence 3 as a belief that there is no safety (“NOsafety”).

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Another quote from which I  identified a negative belief about fellow citizens was mentioned in the Introduction. As I was sitting in a taxi, the driver said: “You have to beat the people [S1]. Beatings and intimidation are the only means that work [S2]. If you tell someone to turn right on this street, he will not do it [S3]. But if you beat him, he will do the right thing [S4]. People are used to this [S5]. This is the only way to have a safe society [S6].” In this quote, each sentence expresses a belief that the people cannot behave in a way that is needed for a safe society. Sentences 1 to 3 state that it is necessary to use violence against the people to enforce rules. Sentence 4 states that following the rules is “the right thing.” Sentence 6 states that the rules are the basis of having a safe society.

IDENTIFYING INFERENCES

Inferences are directed relationships between at least two beliefs, where an antecedent triggers a consequent (antecedent → consequent). For example, Belief (B) 1, “I have access to Facebook,” could be an antecedent triggering consequent B2, “I am in touch with a large number of regime opponents.” Consequents of certain inferences can moreover be antecedents of other inferences, so that beliefs are usually connected by indirect inferences (Abelson and Carroll 1965). For example, B2, “I am communicating with large numbers of regime opponents,” could in turn be an antecedent of a consequent B3, “Together we can make a difference,” so that B1 triggers B2, and B2 triggers B3 (B1 → B2 → B3). In this structure, B1 is a pure antecedent, B2 an intermediate belief, and B3 a pure consequent. Indirect inferences between beliefs can in turn trigger decisions for actions. For example, B3, “Together we can make a difference,” could trigger a decision to participate in a political protest, so that B1 triggers B2, B2 triggers B3, and B3 triggers a decision (B1 → B2 → B3 → decision to protest). An example about a decision to stay at home instead could include an antecedent B4, “The government is attacking the citizens,” triggering a consequent B5, “Citizens are dying.” B5 could in turn be an antecedent of B6, “I feel frightened,” so that B4 → B5 → B6.

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B6, “I feel frightened,” could then trigger a decision against protesting, so that B1 → B2 → B3 → decision to stay at home. In this example, B4 addresses a state behavior, B5 addresses an event, and B6 addresses an emotion. Research has shown that inferences can be based on “principles of logic and other normative theories in our reasoning or decision making” (Evans and Over 1996, 7), “personal” aspects that allow individuals to achieve their individual goals (Evans and Over 1996, 7; Simon 1985), “scraps of knowledge” rather than complete information (Oaksford and Chater 2009, 69), “affective charge” that can be activated when people hear about political concepts they have evaluated before (Lodge and Taber 2005, 455), and even erroneous beliefs (Tversky and Kahneman 1971; Kahneman 2011). This study focuses on any type of inferences identifiable through linguistic connectors.

Direct Inferences

I identified a large number of direct inferences connecting beliefs about different factors. In this section, I present examples related to identifying such inferences from (i) logical reasoning, (ii) temporal order,7 (iii) conditional connectors, and (iv) causal connectors. For example, I identified an inference based on temporal order and logical reasoning from an interview with a young Moroccan woman, who was selling key chains in a tourist store on a market in a side street of Marrakech’s Old Town. Table 3.2 shows the inference identified from her direct speech, separated into antecedent and consequent. The sentences contain beliefs addressing two factors:  (i) the head of state making an effort for the citizens (S1–​S4) and (ii) the individual feeling satisfied with her life (S5). The sentences directly followed each other (temporal order), suggesting that the beliefs are related to each other. Logical reasoning moreover provides information about the direction of the relationship: It indicates that the belief about the king’s effort is an antecedent of the belief about personal satisfaction (it would make no

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Table 3.2  Identification of Inference from Temporal Order and Common Sense Antecedent

Consequent

He [the king] does many things (S1).

I am happy with my life (S5).

He helps the poor (S2). He visits hospitals

Belief about personal satisfaction

(S3). He treats children kindly (S4).

(“satisfaction”)

Belief about the head of state making an effort for citizens (“effortHeadOfState”)

sense for a person to believe that the king is making an effort for the citizens because a particular individual who is unknown to the king is feeling satisfied with life). I identified another inference based on a temporal connector and logical reasoning from an interview with an Egyptian woman (see Table 3.3). As in the previous example, I  have divided the quote according to my identification of antecedent and consequent. This sentence contains beliefs addressing two factors: (i) the revolution in Tunisia, which is addressed by the temporal subclause, and (ii) solidarity with other protestors, which is addressed by the main clause. The structure of the sentence indicates that the two beliefs are related to each other. The temporal connector at the beginning of the sentence (“when”) moreover indicates the direction of the relationship: the first belief is an antecedent of the latter. This is confirmed by logical reasoning, according to

Table 3.3  Identification of Inference Based on Temporal Connector and Common Sense Antecedent

Consequent

When I heard about the revolution in

My heart was feeling solidarity with

Tunisia,

the protestors.

Belief that there is a revolution in Tunisia

Belief about solidarity (“solidarity”)

(“revolutionTunisia”)

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Table 3.4  Identification of Inference Based on Conditional Antecedent

Consequent

The internet is like a matchmaker (S1).

I would have never met Mamfakinch

Without it,

[opposition group] (S2).

Belief about using the internet (“media”)

Belief about interacting with protestors (“interactProtestors”)

which the belief that there is a revolution in Tunisia triggers another belief according to which she feels solidarity with the protestors. By contrast, it would make no sense that a belief about feeling solidarity with protestors in another country would trigger another belief that there is a revolution in that country. I identified another inference from the order of a conditional sentence, expressed by a Moroccan journalist. Table 3.4 shows the inference, divided into antecedent and consequent. For reasons of clarity, I have added the preceding sentence. The sentences address beliefs about two factors:  social media usage (S1 and S2) and interacting with protestors (main clause of S2). The second sentence is a conditional sentence, connecting both beliefs to each other, so that the first is an antecedent and the second a consequent. I identified an inference based on a causal connector from an interview with an Egyptian who was working in a travel agency. Table 3.5 shows the inference I identified from his direct speech. As previously discussed, I  separated the quote according to my identification of antecedent and consequent.

Table 3.5  Identification of Inference Based on Causal Connector Consequent

Antecedent

People in the villages are living much

because the government helped them.

better now

Belief about effort by the state

Belief that living conditions are improving

(“effortHeadOfState”)

(“improvedLivingConditions”)

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Table 3.6  Identification of Inferences from Causal Connector, Two Antecedents Consequent

Antecedent

All of the people protested

Because they had been living in

Belief that mass protests are happening

oppression for many years

at home (“protest”)

Belief about state oppression (“crimeState”) And did not like the government. Beliefs about disproval of state (“NOapprovalHeadOfState”)

Like the previous examples, this sentence contains beliefs about two factors: (i) improved living conditions and (ii) effort by the state.8 The sentence moreover contains a causal linguistic connector, which suggests that the second belief about the state making an effort for the citizens triggers the first belief that living conditions are better now. In contrast to the previous example, the temporal order in which this sentence was uttered does not correspond to my identification of antecedent and consequent—​the first half of the sentence expresses the consequent, whereas the second half expresses the antecedent. I identified another inference based on a causal connector from the sentence displayed by Table 3.6. The sentence was obtained from an interview with an Egyptian shop owner. I identified three beliefs from his quote: (i) a belief that there are mass protests, (ii) a belief that the state is oppressing the citizens, and (iii) a belief that the people are not supporting the head of state. The sentence contains a causal connector (“because”), which indicates that the second and third beliefs (connected by an “and” connector) together trigger the first belief.

Indirect Inferences

Decisions for actions are usually triggered by indirect inferences between beliefs (belief → belief → . . . → decision). To identify indirect inferences

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from direct speech, it is not necessary to apply additional analyses, as indirect inferences become visible from the identification of direct inferences. In the following, I provide examples. An Egyptian who worked in a high school said:  “We were suffering from ignorance [S1]. We had been destroyed by ignorance [S2]. We had to change this [S3]. There were a lot of people who wanted to change this [S4].” From this quote, I identified three beliefs and two inferences, shown by Table 3.7. From the first sentence, I identified a belief that there is ignorance. From the second sentence, I  identified a belief that change is needed. From the last sentence, I  identified a belief that the people are united. I moreover identified an inference between the belief that there is ignorance and the belief that change is needed; and another inference between the belief that change is needed and the belief that the people are united. I identified these inferences from the temporal order in which the individual addressed ignorance, the need for change, and the unity of the people, as well as from logical reasoning (it would make no sense to believe that change is needed because the people are united; or that there is ignorance because change is needed). Another Egyptian, who was working in a shop, described the situation prior to the uprisings in 2011 in these words: “People were living like animals [S1]. There was tyranny [S2]. I knew that we had to change this [S3]. When I saw all the protestors, I agreed with them [S4].” From this quote, I identified four beliefs and two inferences, involving one and two

Table 3.7  Identifying an Indirect Inference Connecting Three Beliefs Belief 1 →

Belief 2 →

Belief 3

We were suffering from

We had to change

There were a lot of people

ignorance (S1). We

this (S3).

who wanted to change

had been destroyed by

Belief that change is

this (S4).”

ignorance (S2).

needed (“needChange”)

Belief that the people are

Belief about ignorance (“ignorance”)

united (“unityPeople”)

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antecedents (see Table 3.8). From the first two sentences, I  identified a belief that the government is tyrannizing the people. I  later added this belief to a group of beliefs about governmental violence, which is often associated with tyranny. From the third sentence, I  identified a belief that change is needed. Temporal order suggests that the two beliefs are connected, and logical reasoning suggests that the belief that change is needed is triggered by the belief that the government is violent. From the fourth sentence, I identified two beliefs: From the subclause, I identified a belief that protests are happening, and from the main clause, I identified a belief addressing approval of the protests. Temporal order and logical reasoning suggest that the belief about approval of the protests is triggered by both the beliefs that change is needed and the belief that protests are happening. Note that this inference involves two antecedents. In front of a mosque in Marrakech, I interviewed a donkey rider, who was resting on the pavement. He said: “I ride donkeys, but this work is not good [S1]. Protests are a good thing [S2].” From this quote, I identified three beliefs—​a belief about his employment (main clause of S1), a belief about being dissatisfied (subclause of S1), and a belief about approving Table 3.8  Identifying an Indirect Inference Connecting Four Beliefs Belief 4 →

Belief 5 →

Belief 7

People were living like

I knew that we had to

I agreed with them (S4).

animals (S1). There was

change this (S3).

Belief about approval

tyranny (S2).

Belief that change is

of the protestors

Belief about violence

needed (“needChange”)

(“approvalProtestors”)

by the government (“violenceGovernment”)

Belief 6 → When I saw all the protestors, Belief that protests are happening (“protest”)

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Table 3.9  Identifying an Indirect Inference Connecting Three Beliefs Belief 8 →

Belief 9 →

Belief 10

I ride donkeys (S1).

But this work is not

Protests are a good

Belief about employment

good (S1).

thing (S2).

(“job”)

Belief about

Belief about approval

being dissatisfied

of the protestors

(“NOsatisfaction”)

(“approvalProtestors”)

of protestors (S2; see Table 3.9). I moreover identified two inferences following the temporal order in which the beliefs were expressed: an inference from the belief about his job to the belief about not being satisfied, and another inference from the belief about being dissatisfied to the belief about approving of the protestors. In Egypt, I interviewed a housewife, who said: “Mubarak did nothing [S1]. There were many problems: Some earn 2 pounds a day, and others 2,000 [S2]. How is this possible? [S3] I was against Mubarak [S4].” From this quote, I identified three beliefs and two inferences. I identified a belief that the head of the state is not making an effort from the first sentence, a belief that living conditions are poor from the second sentence, and a belief about disapproval of the head of state from the third sentence (see Table 3.10). I moreover identified two inferences connecting the beliefs in the order they were expressed, according to which the belief that the head of state is not making an effort triggers a belief that living conditions are poor, which in turn triggers a belief about disapproving of the head of state.

DECISIONS

I identified decisions about actions from sentences describing that individuals joined the protest or that they stayed at home instead. Sometimes individuals did not comment on their behavior directly, and I used sentences expressing support or criticism of protesting to identify decisions. In this section, I provide a few examples for both types of decisions.

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Table 3.10  Identifying an Indirect Inference Connecting Three Beliefs Belief 11 →

Belief 12 →

Belief 13

Mubarak did

There were many

How is this possible? (S3)

nothing (S1).

problems: Some earn 2

I was against Mubarak (S4).

Belief that the head

pounds a day, and others

Belief about not approving

of state is not making

2000 (S2).

of the head of state

an effort

Belief about poor

(“NOapprovalHeadOfState”)

(“NOeffortHead

living conditions

OfState”)

(“poorLivingConditions”)

A Moroccan journalist said “We protested for him”—​a statement that I coded as a decision for action. The person he was referring to as “him” was a rapper, Moad Belghouat, who was put in jail after singing for political change. “He was under surveillance,” the journalist says. “He gave a concert and was imprisoned afterwards. We protested for him. I  also wrote an article for him. And I changed the picture of my Twitter account into his photo, saying ‘free Moad.’ ” Another Moroccan, who was approaching retirement, talked about the beginning of the Arab Spring in his country by saying:  “We began protesting for Egypt”—​another statement which I coded as a decision to act. He explained:  “We supported Egypt. The next day, we did not just want to protest out of support. We encouraged young people in Morocco to join our protest.” At the same time, an Egyptian in Cairo was contemplating joining the protests, which had started in her home country. She talked about how she decided to join by referring to a conversation with a close friend: “We were talking on the phone and encouraged each other. We decided to go to Midan Tahrir, even though our families were against it.” From this quote, I identified the main clause of the second sentence “We decided to go to Midan Tahrir” as a decision to protest. In the meantime, another woman was discussing joining the protests in Rabat. She said: “I joined the protests on February 20th”—​a quote which

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I coded as a decision for action as well. She said: “We had discussed this on Facebook. Some were against it. Others were in favor. Many were going to join the march on February 20th. I joined the protests on February 20th.” By contrast, a law student, who spent his free time earning money in a bookshop, said: “We have all freedoms. We have safety. Look at Syria and other countries. We have everything one needs. Why protest?” I  coded the last sentence of this quote as a decision against protesting because it expresses that the individual sees no reason to protest. This was especially surprising because the individual belongs to a group that is often connected with the Arab Spring—​a young student, who has to earn money when he is not studying, and whose prospects of finding employment after graduation are slim. Talking about the Arab Spring, a merchant from Morocco comm­ ented: “Egypt and Tunisia are not related to me.” I coded this as a decision to refrain from protesting in the context of the following sentences, which were uttered in response to my comments on protests in Morocco: “I am a merchant. I have a family: a wife and two boys, and relatives in the South. I do not have any dreams except for work. I split my life between family and work. I work and then I go to my family. I am happy with my life. Life is good. I do not think about politics.” In this context, the sentence “Egypt and Tunisia are not related to me” indicates the merchant’s lack of interest in protest, even if they lead to a revolution. A shoe seller had similar thoughts. When I asked him where he was on February 20, 2011, he replied: “I was in my shop.” I coded this response as a decision not to protest. To explain his behavior, he said: “Everyone here lives well,” he said. “But they do what they want, asking for more salary. One demonstration leads to the next, and there is killing, war, and blood. Demonstrations bring many problems.”

OUTLOOK

In this chapter, I  have shown how I  constructed data from the direct speech of individuals who participated in the Arab Spring, or stayed at

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home instead. In the next chapters, I  show how I  examined these data in the context of a computational analysis. In Chapter  4, I  introduce a computational model developed to trace the beliefs underlying decisions about participating in the Arab uprisings. I outline the contribution of this model to other methods applied to study beliefs and explain how I applied the model to identify reasoning processes from the data described in this chapter. In Chapter 5, I present the findings of the computational analysis.

NOTES 1. I furthermore identified two other beliefs from this quote: a belief that the ruler needs to change (“needchange”; S1), and a belief that there is a revolution in Tunisia (“revolutionTunisia”; S2). As mentioned, beliefs about a revolution abroad were found to be crucial to decisions to protest (see Chapter 5 of this volume). 2. From this quote, I  furthermore identified a belief about the state using violence against the people (S1). I assigned this quote to the type “state behavior.” I identified the belief about state violence from the noun “police” related to the action of “attacking and beating.” Since the police act on behalf of the state, their attacks can be understood as a form of state violence. I elaborate more on this particular belief later in this chapter. 3. From the first two sentences of the quote, I moreover identified a belief that the state is committing crimes. I  identified this belief from the comparison between Mubarak and “the watchers of the Zionist system” (S1) as well as the word “oppression,” which is employed to describe the Egyptian regime (S2). From the second sentence, I furthermore identified a belief that change is needed because this statement expressed the necessity (“must”) of ending state crimes (“There must be a force to retreat from oppression”). I assigned this belief to the type “needs.” 4. From this quote, I also identified a belief that the head of state is making an effort for the people (“effortHeadOfState”), which I assigned to the type “state behavior,” and approval for the head of state (“approvalHeadOfState”), which I  assigned to the type attitudes on the state. I identified the belief that the head of state makes an effort for the people from sentence 4, which contains the word “effort.” Approval for the head of state is expressed by sentences 2 and 3, which address both positive judgment (S2) and a positive emotion about the head of state (S3). 5. Sentences 4 and 5 moreover express support for the protestors, wishing them success (S4) and saying they are rightful (S5), whereas sentence 3 expresses a belief that people are mobilizing against the government. I coded sentences 4 and 5 as a belief about supporting the protestors, which I called “support protestor” and assigned to the type “attitudes protestors,” and a belief that protests were happening at home, which I called “protest” and assigned to the type “events.”

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6. I also identified a belief that the protestors need strength from the first sentence of this quote (“needStrengthProtestors”), which I assigned to the type “needs.” 7. Temporal order by itself can be insufficient: Logical reasoning tells us that although the barometer’s falling is temporally prior to rain, it does not cause rain. 8. Note that this quote refers to improved living conditions in the villages, and to an effort made by the government. However, I  did not code the quote as beliefs about “improved living conditions in the villages” and “effort made by the government.” Rather, I coded it as beliefs about “improved living conditions” and “effort by head of state.” I proceeded this way because there were numerous other quotes addressing living conditions and effort by the head of state (see previous section). This quote was not substantially different from those quotes, even though it was more specific about where living conditions were improving (in the villages) and less specific about who was making an effort for the people (the government).

4

Tracing Reasoning Processes

“I was very enthusiastic,” Leila recalls the moment when she first entered Midan Tahrir during the Arab Spring. “I felt like an important person.” Apart from discussing her feelings at that moment, she comments on numerous other factors in the course of the interview. She refers to corruption and repression. She refers to her family and friends. She mentions Facebook, al-​Jazeera, and the Egyptian media. And she mentions the economic situation and her daily experience of commuting on microbuses. Similarly, Ahmed talks about various factors when remembering the protests in his country. “I have always been anti-​authoritarian,” he says when thinking about his political activism. “Since the first day in school, when my teacher hit me when I was playing.” In the following, he talks about his friends, his family, political leaders, authoritarian structures, the international environment, prior protest movements, and cyberactivism. In the previous chapter, I have shown how I analyzed direct speech to identify the main components of reasoning processes—​beliefs, inferences, and decisions to protest or to stay at home. As suggested by the variety of factors mentioned by Leila and Ahmed, the textual analysis identifies a very large number of beliefs (145 in total). The analysis moreover identifies hundreds of inferences that connect these beliefs to decisions, so that they constitute complex systems of beliefs. Because of this complexity, the reasoning processes that motivate decisions about participating in the Arab Spring are not immediately visible from the data. In this chapter, I  describe a computational model developed to systematically trace the Hot Contention, Cool Abstention. Stephanie Dornschneider, Oxford University Press (2021). © Oxford University Press. DOI: 10.1093/​oso/​9780190693916.003.0004

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reasoning processes underlying decisions to join the uprisings or to stay at home instead.

HOW TO IDENTIFY REASONING PROCESSES FROM BELIEF SYSTEMS

Due to belief complexity, current research has largely abandoned the study of reasoning processes contained by belief systems. As one analyst put it, belief systems are “too large and unwieldy to analyze using standard methods” (Taber 1992, 889). In this book, I  build on early methods (Abelson and Carroll 1965; Axelrod 1976; Taber 1992; also see Hastie and Rawson 2004 on Bayesian network models of belief systems) to develop a computational model that traces inferences antecedent to decisions. In the following paragraphs, I first discuss what this model contributes to existing studies of belief systems, before describing the model in more detail.

Correlations between Similar Beliefs

Many researchers have analyzed belief systems by exploring correlations between beliefs with similar contents. This has provided knowledge about a large range of subjects, including ideology (Converse 1964; Murray, Cowden, and Russett 1999; Jost 2006; Reifler, Scotto, and Clarke 2011), prejudice and discrimination (Baum 2009; Bilewicz et al. 2013; Peffley and Hurwitz 2002), religious fundamentalism (Brandt and Tongeren 2017), right-​wing authoritarianism (Altemeyer 1996; Crowson and deBaecker 2008; Hiel and Mervielde 2002; Mirels and Dean 2006), or immigration (Bloom, Arikan, and Courtemanche 2015; Richey 2010). In foreign policy analysis, considerable research has focused on “the operational code,” which consists of the “philosophical” and “instrumental” beliefs related to policy decisions (George 1969). These analyses have contributed new insight about the psychology of a variety of political leaders, including the Bolshevists (Leites 1951), US presidents and policy advisors (Walker,

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Schafer, and Young 1998; Walker 1977; Schafer and Crichlow 2000; Renshon 2008), Israeli prime ministers (Crichlow 1998), or democratic or presidential leaders more generally (Schafer and Walker 2006; Walker, Schafer, and Young 1998). All of these analyses examine correlations, which identify beliefs that occur together. The following study adds insight to this literature by tracing inferential belief connections, which identify how certain beliefs trigger other beliefs (e.g., Belief [B]‌1, “the state is violent,” could trigger Belief 2, “I am afraid,” so that B1 → B2). This provides information about the reasoning processes antecedent to decisions (belief -​> belief → . . . → decision). Analyses of correlations are also limited to beliefs with similar contents, which constitute only a small subset of belief systems. The data constructed for this study, for example, contain 145 beliefs about 15 types of factors. The following analysis systematically traces inferences that link these beliefs to decisions regardless of their propositional contents.

Systemic Features and Particular Inferences

Researchers have also analyzed structural features of belief systems. Bonham, Shapiro, and Trumble (1979) investigate whether Israeli officials adjusted their beliefs related to the October War, while Pierce (2011) examines the stability of beliefs held by advocacy coalitions between the United States and Israel. Boutyline and Vaisey investigate the structure of beliefs more generally by constructing a direct measure of centrality (Boutyline and Vaisey 2017; also see Abelson 1973 on the structure of belief systems). The following study complements this literature by providing information about inferences antecedent to decisions, which is not available from structural features, such as belief change or centrality. Other researches have examined particular inferences and cognitive shortcuts (heuristics; e.g., Kuran and Sunstein 1999; Lau and Redlawsk 2001; Scholz and Lubell 1998; Simon and Newell 1958). Weyland (2012) argues that the Arab Spring was the result of people drawing “rash inferences” related to the heuristics of availability and representativeness.

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According to this theory, people participated in the uprisings because they “overrated the significance of the Tunisian success, overestimated the similarities with the political situation in their own country, and jumped to the conclusion that they could successfully challenge their own autocrats” (917). Nevertheless, protesting in the Middle East involves very high risks, and deliberations about protest during the Arab Spring could have included those risks. My interviews show that the people who joined the uprisings knew that their behavior could lead to persecution, torture, and death, while jeopardizing the lives of their families. This was especially important in Egypt, where more than one thousand political activists were reported tortured or murdered (individually and en masse) by security forces in 2015 (Dornschneider 2016). Human rights group have been documenting “forced disappearances” for decades, and recent numbers have been rising. During the Egyptian uprisings, 1,200 protestors were made to disappear (Dornschneider 2016). The following study addresses these risks by exploring any inference expressed by the protestors instead of a priori focusing on a particular inference considered important by the researcher. This emphasizes that “we must characterize the political situation, not as it appears ‘objectively’ to the analyst, but as it appears subjectively to the actors” (Simon 1985, 298).

Tracing Inferences

In the last century, various projects were developed to trace inferences that link beliefs to decisions. This research often uses preset constructs that group beliefs and inferences into broader concepts like scripts (inferences) or utility (belief; e.g., Axelrod 1976; Schank and Abelson 1977; also see Taber’s 1992 use of “paradigms” [892], and Abelson’s 1973 “Goldwater machine”). Such preset constructs are often difficult to apply to particular situations and have been connected to “problems of dealing with mundane reality” (Carbonell 1978, 28). Scripts, for example, address the question, “How do people know what behavior is appropriate for a particular situation?”—​for instance, what

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to order in a restaurant or how to behave in a restaurant more generally (Schank and Abelson88k 1977, 36). Yet, what is appropriate in a given situation can differ from individual to individual. Consider a situation where two students fail the same exam for the second time in a row. One of the students could consider it “appropriate” to return to the desk and study even harder than before, while the other might consider repeated failure as a sign that it is “appropriate” to study another subject or to drop out of school. Related to the Arab Spring, we are furthermore confronted with an extraordinary situation that did not correspond to people’s prior experiences. There was no “script” about how to behave in this particular situation. What was “appropriate” had to be decided by the individuals as they were suddenly exposed to this new situation. As previously described, there were significant differences in the way in which individuals responded to this situation, which I explore in the following analysis.

Automating the Analysis

Rather than applying predefined concepts, the following analysis traces inferences identified from the direct speech of individuals. To achieve this, the analysis develops a program that formalizes belief systems as monotone Boolean circuits. Monotone Boolean circuits implement logical rules that have the same structure as inferences connecting beliefs (belief antecedent → belief consequent). They are analogous to human reasoning processes and therefore especially suitable for this study (Livnat and Pippenger 2006, 3200; also see:  McDermott 1982; Wos et al. 1984). Monotone Boolean circuits are often applied in computer science to explore theoretical questions related to computational complexity, but they are also useful to solve practical problems related to automated reasoning, for example, the construction of robots (e.g., Douglas, Bachelet, and Church 2012; Jaynes 2003; Crama and Hammer 2014). To my knowledge, they have not yet been applied to trace reasoning processes from

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belief systems, even though there are automated analyses of processes as well as belief systems more generally.

Formalization

Formally, a Boolean circuit C consists of input terminals, gates, and an output terminal (Livnat and Pippenger 2006, 3200). Each gate implements a Boolean function, whose inputs and outputs are “true” or “false.” The value of the output terminal depends on the preceding gates, which in turn depend on the input terminals. Input terminals receive their values from the environment. A Boolean circuit does not include cycles. A monotone Boolean circuit does not include negation. In the following formalization, gates represent inferences, input terminals represent pure antecedent beliefs, and output terminals represent decisions. The value of the output terminal shows if decisions can be reached: It is either “true” (decision reached) or “false” (decision not reached), depending on the preceding gates that indicate if certain beliefs are held (“true”) or not (“false”). For reasons of clarity, I have only given examples of inferences with single antecedents in the previous chapters. However, inferences can also have multiple antecedents, namely disjunctions and conjunctions (see Dujmović and Larsen 2007). Disjunctions indicate that one or another belief can trigger a third belief. For example, B1, “I am in pain,” or B2, “I am very happy,” can trigger B3, “I am about to cry” (B1 ˅ B2 → B3). Conjunctions indicate that at least two beliefs together trigger another belief. By itself, B1  “Peter is putting on his shoes” could trigger B3  “Peter is about to leave the house.” Together with B2  “Peter is carrying a megaphone”, however, B1 could trigger B4  “Peter is on his way to a protest” (B1 ˄ B2 → B3). The following analysis includes both inferences with single and multiple antecedents. These are differentiated by two types of gates contained by Boolean circuits: and-​gates, which represent conjunctions, and or-​gates, which represent disjunctions. Any gate can represent inferences with single

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antecedents, and this study applies or-​gates to model inferences with single antecedents. Figure 4.1 visualizes an example to explain how monotone Boolean circuits can be used to trace reasoning processes from belief systems. Specifically, the figure shows a monotone Boolean circuit representing

A = false

C = false 

Mass protest at home

State violence

D = true

or

Strength of protesters

E = false

F = false

Courage

Decision to Protest

E = true

F = true

Courage

Decision to Protest

and

B = true Revolution in Tunisia

A = false

C = true

Mass protest at home

State violence

D = true

or

Strength of protesters

and

B = true Revolution in Tunisia

Figure 4.1  Tracing reasoning processes from monotone Boolean circuits.

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a simple belief system motivating an individual to participate in the Egyptian Arab Spring. There are three input terminals, A (mass protests at home), B (revolution in Tunisia), and C (state violence). If given as input, they are “true.” Otherwise, they are “false.” There are two or-​gates related to D (strength of the protestors) and the output terminal (decision to protest). D is true if A or B is true. Otherwise, it is false. The output terminal is true if E (courage) is true. Otherwise it is false. E is related to an and-​gate. It is true if both C and D are true. Otherwise, it is false. To trace reasoning processes underlying decisions, I assert inputs and observe the value of the output terminal. For example, given belief B (revolution in Tunisia), the output terminal is false. This example is shown by Figure 4.1A. By contrast, the value of the output terminal is true, given beliefs B (revolution in Tunisia) and C (state violence). This example is shown by Figure 4.1B. The figure also shows how B and C motivate a decision to protest: B (revolution in Tunisia) triggers D (strength of protestors), and D subsequently triggers E (courage) along with C (state violence). E activates the decision. Applying monotone Boolean circuits to model belief systems adds to previous automated studies of belief systems. Rather than exploring reasoning processes as if-​ then statements (Taber 1992; Abelson and Carroll 1965), Boolean circuits represent belief systems as graphs, whose interconnections are easier to identify and readily analyzable by various algorithms. Boolean circuits also contribute to studies of reasoning processes that include graphs but no standard method to analyze them (Axelrod 1976).

Computational Model

To apply Boolean circuits, I  wrote a computational model in Python. The model represents Boolean circuits as two-​dimensional lists. Groups of Boolean circuits are represented as three-​dimensional lists. Terminals and gates are represented as one-​dimensional lists. Each list consists of at least two elements: a belief and an identifier. The beliefs represent the

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semantics of the system, whereas the identifiers represent the structure of the system. Specifically, input terminals are indicated by “in,” and-​gates by “and,” and or-​gates by “or.” Inferences involving only one antecedent are represented as or-​gates. The structure of each inference is indicated by the order of the one-​ dimensional lists. Belief consequents are located in positions 0 (first items of lists), followed by the identifiers in position 1 (second items of lists), and the antecedents (remaining items). Input gates have no antecedents following the identifiers.1 This is an example: [[“protest”, “in”], [“solidarity”, “or”, “protest”], [“strengthPRO”, “or”, “solidarity”], [“vioG”, “or”, “protest”], [“courage”, “and”, “vioG”, “strengthPRO”], [“P-​E-​2011”, “or”, “courage”]] This circuit represents the reasoning process of a person who decided to join the Egyptian Arab Spring protests in 2011. The first list represents an input terminal that contains a belief that protests are happening (“protest”). The second list represents an inference, where the belief that protests are happening triggers solidarity (“solidarity”). The following list represents an inference in which solidarity triggers another belief that the protestors are strong (“strengthPRO”). The next list represents an inference in which protests trigger another belief that the government is engaging in violence (“vioG”). The next list represents an and-​inference in which the beliefs about governmental violence and the protestors’ strength together trigger courage (“courage”). The final list represents an inference where courage triggers a decision to join the protests (P-​E-​2011).

ANALYSIS

The following analysis proceeds in three main steps. The first step of the analysis provides an overview of the beliefs contained by the data, before the following steps identify key inferences by which subsets of these beliefs trigger decisions to participate in the uprisings or to stay at home.

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Belief Overview

First, the model counts all the beliefs contained by the data. This is necessary because the belief systems identified by the qualitative analysis contain a large number of beliefs. In addition to counting beliefs, I apply the model to differentiate between four different types of beliefs: 1. Beliefs that are included by different proportions of reasoning processes of protestors versus non-​protestors, 2. Beliefs that are included only by reasoning processes of protestors, 3. Beliefs that are included only by reasoning processes of non-​protestors,  and 4. Beliefs that are shared by both groups. I identified the first type of belief by two-​proportion z-​tests. I identified the remaining belief types by functions that differentiate between beliefs that are unique to reasoning processes of protestors versus non-​protestors. Appendix 3 lists the findings for each belief contained by the data.

Beliefs Connected to Decisions

Second, I analyze which beliefs trigger decisions. This analysis isolates the beliefs that constitute the inferences antecedent to decisions. Specifically, I use the program to identify minimum sets of beliefs that trigger a decision in each belief system contained by the data. Identifying minimum sets of beliefs is computational problem, for which no efficient algorithm is known (or even believed to exist in the worst case). Related to my data set, I was nevertheless fortunate solve this problem by two functions that recursively process belief systems, formalized as Boolean circuits. To identify a minimum set that triggers the decisions to stay at home, I wrote a function that asserts single random beliefs. To identify a minimum set that triggers the decisions to protest, I wrote a function that asserts a set

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of five pure antecedent beliefs that have the highest frequencies. The minimum sets are show by Appendix 4.

What Do Minimum Sets Look Like?

The minimum sets contain a subset of the beliefs that trigger a decision in each belief system contained by the data. As such, each belief contained by a minimum set need not be contained by each belief system; however, each belief system contains at least one belief of the minimum set, and that belief can trigger a decision (usually several beliefs trigger a decisions together). Figure 4.2 shows a simple example, where the beliefs of a minimum set are marked in bold: [3, 4, 7]. Each system contains a subset of the minimum set:  The system on the left contains B4, the system in the center contains B3, the system on the right contains B3 and B7. In all systems, the beliefs contained by the minimum set activate a decision for action: In the system on the left, B4 triggers B5 and B6, which in turn trigger the decision. In the system in the center, B3 triggers B5 and B6, which in turn trigger B7 and B8, which in turn trigger a decision for action. In the system on the right, B7 triggers B9, which triggers a decision along with B3.

Reasoning Processes Antecedent to Decisions

Minimum sets differentiate beliefs that trigger decisions from other beliefs that fail to do so. In this way, they isolate the beliefs that play a major role in decision-​making, which significantly reduces the number of beliefs to analyze. Once minimum sets are known, we can trace the reasoning processes by which they activate decisions. In the following analysis, I  produce graphs that visualize these reasoning processes for each belief system contained by the data.2 From these graphs, I identify the key inferences antecedent to decisions to join the Arab Spring or to stay at home instead. This analysis focuses on

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Figure 4.2  Example of a minimum set. 1

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the direct antecedents of decisions (belief → decision) and the beliefs that trigger these antecedents (belief → belief → decision). The graph analysis also identifies key inferences antecedent to decisions based on the results of the z-​tests. These tests identify the beliefs that are antecedent to the most different proportions of decisions by protestors versus non-​ protestors. The graphs visualize how these beliefs trigger decisions. In the next chapter, I present the findings.3

NOTES 1. Inputs can be inferred in backward reasoning. I use identifiers for reasons of clarity. 2. I used the Python language software package NetworkX to create graphs. 3. Rather than presenting graphs produced by the program, I  provide simplified graphs that apply natural language (as opposed to code) and that isolate the beliefs that are found to matter most. These beliefs are either contained by a significantly different proportion of reasoning processes of protestors versus non-​protestors, or they trigger a much larger number of decisions than the remaining beliefs by a direct inference.

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When Leila first heard about the Arab Spring, she felt intense happiness. Nevertheless, she spent days reasoning about whether she should join the protests or stay at home instead. She thought about risks. She frequently experienced sexual harassment when commuting on microbuses or the underground. Would joining the protests on Tahrir Square expose her to similar experiences? She also thought about the security forces. For years, she had seen their army trucks appear when people were gathering for demonstrations. She knew that protestors often “disappeared” or went to jail. When she first heard about the Arab Spring, Leila hesitated for a while. Even though she was very excited about what was happening, numerous days passed by until she decided to join. When Ahmed heard about the uprisings in Western Sahara, he was similarly moved. He wanted to join the protests right away. But like Leila, he reasoned about whether he should participate or stay at home: Was the story about the protests right? Was it really such a big event, or just a small incident that would be over before he arrived there? Was his help needed? Ahmed learned about the protests in Rabat, where no mass mobilization was happening at the time. He called a friend in Western Sahara, and asked him what was happening. When the friend told him about large numbers of people pouring to the streets and about the danger of the police shutting down the uprisings, Ahmed decided to travel. Upon his arrival, he found the largest protests he had ever seen in his life. Hot Contention, Cool Abstention. Stephanie Dornschneider, Oxford University Press (2021). © Oxford University Press. DOI: 10.1093/​oso/​9780190693916.003.0005

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Although Leila and Ahmed felt very excited, both took time to deliberate about whether to participate in the Arab Spring. In the end, both made the same decision and joined the uprisings. In this chapter, I present how Leila, Ahmed, and dozens of other individuals like them reasoned about the protests, eventually deciding to participate in the Arab Spring or to stay at home instead.

FINDINGS RELATED TO THE ARAB SPRING LITERATURE

The computational analysis identifies various beliefs that speak to the existing literature on the Arab Spring (Appendix 3 lists the findings for each belief identified by the analysis).1 Consistent with social media explanations, the analysis shows that protestors hold beliefs related to their ability to make a difference:  They feel hopeful about the outcome of the protests, and believe the protestors to be strong. Hope is one of the best differentiators between protestors and non-​protestors (z-​score: 5.74, p-​value:  0.000). It is antecedent to twenty-​three of fifty-​three protest decisions as opposed to one decision to stay at home. Strength of the protestors also differentiates protestors from non-​protestors (z-​score: 2.30, p-​value: 0.021). Nevertheless, it is only antecedent to four protest decisions as opposed to zero decisions to stay at home. Protestors also hold beliefs about Facebook, which could be expected to trigger beliefs about hope and strength. Nevertheless, the analysis shows that Facebook is antecedent to similar proportions of decisions by protestors and non-​protestors (z-​ score: 0.75), which emphasizes the need to explore other factors. The analysis also speaks to economic explanations of the Arab Spring by showing that protestors and non-​protestors hold beliefs that living conditions are poor. The z-​test moreover shows that, contrary to expectations from the literature, a significantly larger proportion of reasoning processes by non-​protestors include beliefs that living conditions are poor (z-​score: 2.79, p-​value: 0.005). The analysis also shows that a significantly different proportion of reasoning processes by protestors includes courage (z-​score: 3.74, p-​value: 0.000) and solidarity (z-​score: 3.53, p-​value: 0.000),

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which complements theories, according to which economic hardship triggers protest through negative emotions of frustration. Finally, the analysis speaks to theories on royal exceptionalism. As opposed to what is expected from these theories, the analysis does not identify beliefs related to the rulers’ historical rarity or dynastic superiority in relation to the decisions of non-​protestors. The analysis does not identify beliefs about peaceful governmental responses to the protests, either (e.g., constitutional change, dialogue with the opposition, or the introduction of food subsidies). By contrast, it identifies beliefs about violent state responses to the protests, which are shared by similar proportions of reasoning processes by protestors and non-​protestors (z-​score:  1.87). Nevertheless, the analysis also identifies positive beliefs about the state that differentiate non-​protestors from protestors, notably approval of the head of state (z-​score: 4.24, p-​value: 0.000) and state effort (z-​score: 2.53, p-​value:  0.011). The following sections examine if and how these beliefs motivate decisions to stay at home.

DECISIONS TO PROTEST

The computational analysis identifies numerous other beliefs connected to protest decisions by direct and indirect inferences. The next sections present the key beliefs underlying these decisions.

Key Antecedents of Protest Decisions

The graph analysis first examines the beliefs that are directly connected to protest decisions (belief → decision). It shows that four beliefs about positive emotions are direct antecedents of a surprisingly large number of decisions to mobilize. These beliefs represent hope, courage, solidarity, and national pride. The graphs show that nineteen of thirty protest decisions by Egyptians and ten of twenty-​three protest decisions by Moroccans protestors (29 of

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53 in total) are based on direct inferences from these emotions. Although this finding refers to only 55% of the protest decisions, it is very surprising because the reasoning processes underlying these decisions contain beliefs about fifteen types of factors (see Appendix 2) and trillions of combinations of beliefs and inferences (direct and indirect). Given these data, the probability of a single type of belief, such as beliefs representing positive emotions, to trigger a decision by a direct inference is only 0.07 (1 out of 15). The probability that 50% of the protest decisions are triggered by a certain type of belief through chance is almost zero.2 The remaining twenty-​four graphs visualize a large variety of direct antecedents to protest decisions. The most frequent direct antecedent in these graphs represents interaction with people who are already protesting. This belief triggers six of fifty-​three protest decisions through a direct inference. The second most frequent direct antecedents in the graphs address criminal state behavior, disapproval for the head of state, prior protest behavior, and negative personality of the head of state. Each of these beliefs triggers three of fifty-​three protest decisions through a direct inference. The third most frequent direct antecedents visualized by the graphs trigger two of fifty-​three protest decisions through a direct inference. These beliefs address inherited power, support for protests, need for political change, and lack of alternative to protest. The remaining direct antecedents visualized by the graphs trigger only one protest decision. The computational analysis also provides information about differences in the beliefs that constitute the reasoning processes of protestors and non-​ protestors. It shows that the reasoning processes of protestors include significantly more beliefs about solidarity, courage, hope, and national pride than the reasoning processes of non-​protestors (z-​scores range from 2.14, p-​value: 0.032 [pride] to 5.74, p-​value: 0.000 [hope]). Moreover, it shows that criminal state behavior, a direct antecedent of three protest decisions, is the belief that best differentiates between the reasoning processes of protestors and non-​protestors (z-​score:  5.99, p-​value:  0.000):  The reasoning processes of protestors include significantly more beliefs about this behavior (38 occurrences as consequents in 53 reasoning processes) than

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the reasoning processes of non-​protestors (12 occurrences as consequents in 68 reasoning processes).

Antecedents of Positive Emotions

The analysis shows that there are three key beliefs that trigger positive emotions (belief → belief → decision):  self-​sacrifice by fellow citizens, the revolution in Tunisia, and protest at home. These beliefs are included by a significantly larger proportion of reasoning processes by protestors than non-​protestors (z-​scores: 2.59, p-​value: 0.010 [self-​sacrifice], 2.27, p-​ value: 0.023 [revolution], 2.04, p-​value: 0.041 [protest at home]). The analysis identifies four additional beliefs that trigger positive emotions, which are nevertheless contained by similar proportions of reasoning processes of protestors and non-​protestors. These beliefs address state violence (z-​ score: 1.87), successful prior protest at home (z-​score: 1.62), self-​support (z-​score: 1.55), and families (z-​score: 0.8). Figure 5.1 gives an overview, before the following sections provide examples for the beliefs that are included by significantly more reasoning processes of protestors than non-​ protestors. Most examples refer to antecedents of hope, which was the emotion that best differentiates protest decisions from non-​protest decisions (z-​score: 5.74, p-​value: 0.000), followed by courage (z-​score: 3.74, p-​value: 0.000), solidarity (z-​score: 3.53, p-​value: 0.000), and national pride (z-​score 2.14, p-​value: 0.032). Protest at Home Figure 5.2A shows a reasoning process in which a belief that protests are happening at home triggers a belief representing hope. Hope in turn triggers a decision to protest. Figure 5.2B visualizes another reasoning process, in which a belief about protest at home triggers a belief representing hope. Together with a belief about the family, hope then triggers a belief representing courage. Courage subsequently triggers a decision to protest. Figure 5.3 shows a reasoning process in which a belief about protest at home triggers a belief representing national pride. Along with beliefs about

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Protest at home

Revolution abroad

Self-sacrifice 

Successful prior protest 

Hope, courage, solidarity, national pride

Decision to protest

State violence

Self-support

Family

Figure 5.1  Antecedents of positive emotions.

state violence, protest at home moreover triggers a belief representing solidarity. Together, national pride and solidarity then trigger a decision to protest. Revolution Abroad Figure 5.4 shows reasoning processes in which a belief that a revolution happened abroad triggers a belief representing hope. Hope in turn triggers decisions to protest, as visualized by Figures 5.4A and 5.4B. Both reasoning processes contain additional direct antecedents to decisions to protest, namely need for unity among the people (Figure 5.4A) and need

(a) Protest at home

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Courage

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Family

Figure 5.2  Protest at home, hope and courage.

Protest at home

National pride

Decision to Protest

Solidarity

State Violence

Figure 5.3  Protest at home, solidarity, and pride.

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(a) Revolution abroad

Decision to protest

Hope

Need for unity among people

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Revolution Abroad

Decision to Protest

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Strength of Government Need for change

Figure 5.4  Revolution abroad and hope.

for political change (Figure 5.4B). Both antecedents are included by significantly larger proportions of reasoning processes by protestors than non-​ protestors (z-​scores:  2.58, p-​value:  0.010 [need for political change] and 1.99, p-​value:  0.047 [need for unity among the people]). Together with positive emotions, need for change triggers five protest decisions through a direct inference (as visualized by Figure 5.4B; it triggers two additional protest decisions through a direct inference including beliefs about interaction with protestors; see Figure 5.11). Need for unity among the

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people triggers 1 protest decision through a direct inference (visualized by Figure 5.4A). Figure 5.4B visualizes a reasoning process in which hope is triggered by a belief about a revolution abroad along with another belief about governmental strength (z-​score:  0.17). Governmental strength is contained by similar proportions of reasoning processes of protestors and non-​ protestors (two reasoning processes of protestors versus three reasoning processes of non-​protestors). Figure 5.5 shows a reasoning process in which a belief about a revolution abroad triggers a belief representing solidarity together with a belief about prior protests. Solidarity then triggers a protest decision along with a belief representing modesty. Prior protest is included by similar proportions of reasoning processes of protestors and non-​protestors (z-​ score:  1.62). It occurs in ten of fifty-​ three reasoning processes of protestors, and in six of sixty-​eight reasoning processes of non-​ protestors). Modesty occurs in three reasoning processes by protestors

Revolution abroad

Solidarity

Decision to protest

Prior protest

Modesty

Figure 5.5  Revolution abroad and solidarity.

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as opposed to zero reasoning processes of non-​protestors (significant z-​score: 1.99, p-​value: 0.047). Sacrifice by Fellow Citizens Figure 5.6 shows reasoning processes in which beliefs that fellow citizens are sacrificing themselves trigger beliefs representing courage. Courage in turn triggers decisions to protest, as shown by Figures 5.6A and 5.6B. Figure  5.6A shows a reasoning process in which a belief about self-​ sacrifice triggers a belief about courage as well as a belief that political change is needed. Together courage and need for change then trigger a decision to protest. Figure 5.6B moreover includes a belief representing moral outrage, which triggers the protest decision together with courage. (a) Selfsacrifice by fellow citizens

Courage

Decision to protest

Need for political change

(b) Selfsacrifice by fellow citizens

Courage

Moral outrage

Figure 5.6  Self-​sacrifice and courage.

Decision to protest

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Moral outrage occurred in three of the fifty-​three reasoning processes by protestors as opposed to zero reasoning processes by non-​protestors (significant z-​score: 1.99, p-​value: 0.047). In each reasoning process, it was a direct antecedent of the decision along with a belief representing a positive emotion. State Violence and Successful Prior Protest The graphs identify additional antecedents that trigger positive emotions (see Figure 5.1). As opposed to the beliefs discussed so far, these antecedents are included by similar proportions of reasoning processes by protestors and non-​protestors. Nevertheless, they provide valuable information about the reasoning processes underlying protest decisions by adding information about the antecedents of positive emotions. The following paragraphs address two antecedents that speak to the literature on the Arab Spring: state violence and successful prior protests at home. State violence is included by eleven of fifty-​three reasoning processes of protestors and six of sixty-​eight reasoning processes of non-​protestors (z-​ score: 1.87). Successful prior protest is included in two of fifty-​three reasoning processes of protestors and zero of sixty-​eight reasoning processes of non-​protestors (z-​score: 1.55). State violence is a factor that has been addressed by theories that focus on fear as a major factor deterring people from participating in protest (see Introduction, this volume). This analysis complements these accounts by showing that state violence could also be related to protest decisions by evoking courage. Successful prior protest is a factor that speaks to social media explanations, which emphasize that protestors believed they could make a difference. Consistent with this literature, this analysis finds that successful prior protest is related to protest decisions. The analysis specifies existing accounts by identifying a mechanism through which this belief could motivate protest: It shows that successful prior protest can evoke hope, which in turn triggers decisions to protest. Figure 5.7 shows reasoning processes in which beliefs about state violence trigger beliefs about courage along with beliefs about the protestors’

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State violence

Courage

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Courage

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National Pride

Figure 5.7  State violence and courage.

strength (z-​score: 2.30, p-​value: 0.021) and national pride (z-​score: 2.40, p-​value: 0.016). Figure 5.8A shows a reasoning process in which a belief about successful prior protests triggers a belief about hope, which in turn triggers a protest decision. Figure 5.8B shows that a belief about successful prior protests are so strong that they can trigger a belief about hope even in the presence of another belief about unsuccessful prior protest.

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(a)

Success of prior protest

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Figure 5.8  Successful prior protest and hope.

Other Antecedents of Protest Decisions

The graph analysis identifies two direct antecedents of protest decisions that play a major role in addition to positive emotions: State crime and interaction with protestors. State crime is antecedent to thirty-​eight of fifty-​three protest decisions and twelve of sixty-​eight decisions to stay at home. It has the highest z-​score of all beliefs (significant z-​score:  5.99, p-​value: 0.000). In three reasoning processes, it is a direct antecedent of protest decisions. Interaction with protestors is antecedent to eight of fifty-​ three protest decisions and four of sixty-​eight decisions to stay at home (z-​score: 1.68). In six reasoning processes, interaction with protestors is a direct antecedent of a protest decision. Interaction with Protestors and State Crime Figure 5.9 shows a reasoning process in which interaction with protestors is a direct antecedent of a protest decision. The figure also visualizes the

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Approval of protesters

Interaction with protesters

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Figure 5.9  Interaction with protestors.

antecedents of interaction with protestors. It shows that interaction with protestors is triggered by approval for the protestors, which is in turn triggered by disapproval of the head of state. Disapproval of the head of state is based on a belief representing a personal experience. Approval of protestors is contained by four of fifty-​three reasoning processes of protestors and ten of sixty-​three reasoning processes of non-​protestors (z-​score: 1.22). Disapproval of the head of state is contained by eight reasoning processes of protestors and 4 reasoning processes of non-​protestors (z-​score: 1.68). Personal experience is contained by one reasoning process related protest and zero reasoning processes related to non-​protest. Figure 5.10 shows another example of a reasoning process in which interaction with protestors is a direct antecedent of a decision to protest. In this reasoning process, interaction with protestors triggers the decision along with a belief representing state violence. The figure also provides information about antecedents of interaction with protestors and state

Public support for protest

State violence

Decision to protest

Family

Interaction with protesters

Figure 5.10  Interaction with protestors and state violence.

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Family

Interacting with protesters

Decision to protest

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Need for political change

Figure 5.11  State crime and need for change.

violence: It shows that interaction with protestors is triggered by a belief about the family, while state violence is triggered by a belief about public support for protest. Support for protest is related to a significantly larger proportion of decisions by protestors than non-​protestors (significant z-​ score: 3.02, p-​value: 0.003). Figure 5.11 shows a reasoning process in which interaction with protestors triggers a protest decision in combination with a belief representing state crime. The belief about state crime triggers a belief that political change is needed, which is a direct antecedent of the decision to protest along with interaction with protestors. Figure 5.12 shows a reasoning process in which state crime is a direct antecedent of a protest decision along with a belief about protest at home. The figure also provides information about an antecedent of state crime, namely the re-​election of the head of state (z-​score: 0.81). Figure 5.13 shows an example in which state crime triggers a protest decision indirectly. In this example, state crime triggers disapproval of the head of state. Moreover, passing power to a family member triggers disapproval of the family member. Together disapproval of the head of state and the family member inheriting power trigger a decision to protest. Passing power to a family member is contained by significantly more

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State crime

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Figure 5.12  State crime and protest at home.

reasoning processes of protestors than non-​protestors (z-​score: 1.99, p-​ value:  0.05). It is connected to three of fifty-​three decisions to protest as opposed to zero of sixty-​eight decisions to stay at home. Similarly, disapproval of the family member inheriting power is connected to three protest decisions and zero decisions to stay at home (z-​score: 1.99, p-​value: 0.05).

State crime

No approval of head of state

Decision to protest

Passing power to family member

Figure 5.13  State disapproval.

No approval of family member

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Poverty

State crime

Negative personality head of state

Decision to protest

Violence abroad

Figure 5.14  Negative state personality.

Figure 5.14 shows another example in which state crime triggers a protest decision indirectly, through a belief that the head of state has a bad character. This belief about the head of state in turn triggers a decision to protest. Negative personality traits of head of state are connected to three decisions by protestors but not related to decisions by non-​ protestors (z-​score:  1.99, p-​value:  0.05). The figure also identifies two antecedents of state crime: Poor living conditions and violence abroad. Poor living conditions is connected to fourteen of fifty-​three decisions by protestors as opposed to thirty-​five of sixty-​eight decisions by non-​ protestors (z-​score: 2.79, p-​value: 0.005). Violence abroad is related to two protest decisions versus ten decisions to stay at home (z-​score: 2.00, p-​value: 0.046).

NON-​P ROTEST

Key Antecedents of Decisions to Stay at Home

Most decisions to stay at home are not triggered by direct inferences from a particular type of belief, such as beliefs representing emotions. The antecedent that best differentiates decisions to stay at home from decisions to protest is a belief addressing safety (significant z-​score: 5.33,

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p-​value:  0.000). This belief is connected to twenty-​eight of sixty-​eight decisions to stay at home as opposed to zero of fifty-​three decisions to protest, and it is a direct antecedent of decisions to stay at home in seven reasoning processes. In the remaining reasoning processes, safety triggers other key antecedents of decisions to stay at home: satisfaction with life, approval of the head of state, improving living conditions, and absence of need for protest. Satisfaction is included by twenty-​seven reasoning processes of non-​protestors as opposed to one reasoning process of protestors (significant z-​score: 4.89, p-​value: 0.000). It is a direct antecedent of fifteen decisions. It is the second best belief differentiating the reasoning processes of non-​protestors from protestors. Approval of the head of state is contained by twenty-​two of sixty-​eight reasoning processes of non-​protestors as opposed to one of fifty-​three reasoning process by protestors (significant z-​score:  4.24, p-​value:  0.000). It is also a direct antecedent of fifteen decisions to stay at home. Improving living conditions is a direct antecedent of 4 decisions. It is contained by twelve reasoning processes of non-​protestors as opposed to two reasoning process of protestors (significant z-​score: 2.78, p-​value: 0.005). Absence of need for protest is a direct antecedent of seven decisions to stay at home. It is included by eight reasoning processes of non-​protestors as opposed to zero reasoning processes of protestors (significant z-​ score:  2.58, p-​value: 0.010). Other antecedents that are included by significantly more proportions of reasoning processes by non-​protestors than protestors address the following factors: employment, positive personal traits of the head of state, disapproval of protest, self-​priority, and negative personality of fellow citizens. Employment is antecedent to twenty of sixty-​eight decisions to stay at home compared with zero of fifty-​three decisions to protest (significant z-​score: 4.32, p-​value: 0.000). Positive personality of the head of state is antecedent to twelve decisions to stay at home as opposed to zero decisions to protest (significant z-​score: 3.22, p-​value: 0.001). Disapproval of protest is antecedent to eleven decisions to stay at home versus zero decisions

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to protest (significant z-​score: 3.07, p-​value: 0.002). Self-​priority is antecedent to eight decisions to stay at home compared with zero decisions to protest (significant z-​score: 2.58, p-​value: 0.010). Negative personality of fellow citizens is antecedent to seven decisions to stay at home as opposed to zero decisions to protest (significant z-​score: 2.41, p-​value: 0.016). The following sections describe how these antecedents trigger decisions to stay at home. Non-​Protest and Safety Beliefs about safety trigger decisions to stay at home either directly or indirectly. Beliefs about safety are so strong that they can trigger decisions to stay at home even in the presence of antecedents that support protest, such as need for political change, approval for protest, or poor living conditions. Figure 5.15 shows examples. Figure 5.15A shows excerpts from reasoning processes where safety is a direct antecedent of decisions to stay at home. Figure 5.15B shows reasoning processes in which safety triggers beliefs that living conditions are improving, which in turn trigger decisions to stay at home. Figure 5.15C shows a reasoning process in which safety triggers a belief about approving of the head of state in the presence of another belief that living conditions are poor. Approval of the head of state in turn triggers a decision to stay at home. Figure 5.15D visualizes a reasoning process where safety triggers a decision to stay at home in the presence of a belief representing approval for the protests. Approval for the protests is antecedent to ten of sixty-​ eight decisions to stay at home as opposed to four decisions to protest (z-​score: 1.22). Figure 5.15E shows a reasoning process in which safety triggers a decision to stay at home despite another belief that political change is needed. Need for political change is connected to a significantly larger proportion of decisions by protestors than non-​protestors (z-​score: 2.58, p-​value: 0.01). Safety and Satisfaction In six reasoning processes, beliefs about safety trigger beliefs representing satisfaction. In other reasoning processes, satisfaction triggers decisions

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(a)

Decision to stay at home

Safety

(b)

Safety

Improving life conditions

Decision to stay at home

Approval of head of state

Decision to stay at home

Priority of safety

Decision to stay at home

(c)

Safety

Poverty

(d)

Safety

Approval of protest

Figure 5.15  Non-​protest and safety.

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(e)

Safety

Priority of safety

Decision to stay at home

Violence abroad Need for political change

Figure 5.15 Continued

through direct inferences (15 of 21 reasoning processes). Figure 5.16 shows examples. Figure 5.16A visualizes a reasoning process in which safety triggers satisfaction, which in turn triggers a decision to stay at home. Figure 5.16B shows a similar reasoning process, in which satisfaction triggers a decision to stay at home along with a belief representing hopelessness about the success of the protests (included by similar proportions of reasoning processes by non-​protestors and protestors; z-​score:  1.00). Figure 5.16C shows a reasoning process in which safety and violence abroad together trigger satisfaction, which then triggers the decision to stay at home along with a belief that living conditions are improving. Figure 5.16D visualizes a reasoning process in which safety triggers satisfaction, which in turn triggers the decision to stay at home, along with beliefs about disapproval of the head of state, lack of success of prior protest (included by similar proportions of reasoning processes by non-​protestors and protestors; z-​score: 0.52), and disinterest in politics (included by similar proportions of reasoning processes by non-​protestors and protestors; z-​score: 1.80).

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(a)

Safety

Satisfaction

Safety

Satisfaction

Decision to stay at home

(b)

Decision to stay at home

Hopelessness

(c)

Safety

Satisfaction

Violence abroad

Decision to stay at home

Improving living conditions

Figure 5.16  Safety and satisfaction.

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(d)

Safety

Satisfaction

Disapproval of head of state Decision to stay at home No success of prior protest

Lack of interest in politics 

Figure 5.16 Continued

Satisfaction and Employment Employment is another important antecedent of satisfaction. In total, beliefs representing employment trigger twenty decisions by indirect inferences. Fourteen reasoning processes involve beliefs representing satisfaction. Figure 5.17 shows an example in which a belief about employment triggers a belief representing satisfaction. Satisfaction then triggers a decision along with two contradictory beliefs that represent approval for both the head of state and the protestors. Employment and Self-​Priority Beliefs about employment also trigger beliefs representing self-​priority, which also differentiates decisions to stay at home from decisions to protest.

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Employment

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Satisfaction

Approval of head of state

Decision to stay at home

Approval of protest

Figure 5.17  Employment and satisfaction.

Self-​priority is contained by eight reasoning processes of non-​protestors as opposed to zero reasoning processes of protestors (z-​score:  2.58, p-​ value:  0.010). Figure 5.18 visualizes two examples. Figure 5.18A shows a reasoning process in which employment triggers self-​priority along with and a belief that protests are happening at home. Figure 5.18B shows a similar reasoning process in which beliefs about employment and about the family trigger self-​priority. In most reasoning processes, self-​priority is a direct antecedent of a decision to stay at home (5 of 8 reasoning processes). It is triggered by beliefs about employment (3 reasoning processes), satisfaction (1 reasoning process), and the family (1 reasoning process). Figure 5.18B shows an example that includes employment. Figure 5.19 shows examples for the family and satisfaction. Figure 5.19A visualizes a reasoning process in which satisfaction triggers self-​priority, which then triggers the decision. Figure 5.19B illustrates a reasoning process in which a belief about the family triggers a belief about self-​priority, which in turn triggers the decision.

(a)

Employment

Self-priority

Decision to stay at home

Self-priority

Decision to stay at home

Family

(b)

Employment

Protest at home

Figure 5.18  Self-​priority, employment, and family. (a)

Satisfaction

Self-priority

Decision to stay at home

Family

Self-priority

Decision to stay at home

(b)

Figure 5.19  Self-​priority, satisfaction, and family.

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Positive Beliefs about the State Approval of the head of state is among the most important antecedents of decisions to stay at home. It has the third highest z-​score of beliefs that differentiate decisions to stay at home from decisions to protest (significant z-​score:  4.24, p-​value:  0.000). In most reasoning processes, it is a direct antecedent of decisions. To learn more about these reasoning processes, the following examples include antecedents of approval of the head of state. I  have previously described reasoning processes in which safety triggers approval of the head of state. This section focuses on other antecedents, which are included by a significantly larger proportion of reasoning processes of non-​protestors than protestors. These antecedents represent effort by the head of state, improving living conditions, prior change of power, and positive personality of the head of state. Figure 5.20 shows examples of reasoning processes including each of these beliefs.

Effort by head of state

Approval of head of state

Decision to stay at home

Improving living conditions

Approval of head of state

Decision to stay at home

Prior change of head of state

Approval of head of state

Decision to stay at home

Good personality of head of state

Approval of head of state

Decision to stay at home

Figure 5.20  State approval.

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Effort by the head of state is included by nineteen of sixty-​eight reasoning processes of non-​protestors compared with five of fifty-​three reasoning processes of protestors (significant z-​score:  2.53, p-​value:  0.011). Improving living conditions is included by twelve reasoning processes of non-​protestors as opposed to one reasoning process of protestors (significant z-​score: 2.78, p-​value: 0.005). Prior change of power is included by sixteen reasoning processes of non-​protestors versus one reasoning process of protestors (significant z-​score: 2.35, p-​value: 0.019). Positive personality of the head of state is included by twelve reasoning processes of non-​protestors versus reasoning process of protestors (significant z-​ score: 3.22, p-​value: 0.001). Disapproval of Protest Disapproval of protest is another belief that is antecedent to a significantly larger proportion of decisions by non-​protestors than protestors. In most reasoning processes (6 of 11), disapproval of protest is a direct antecedent of decisions to stay at home. The beliefs that trigger disapproval of protest in these reasoning processes address violence abroad, negative personality traits of fellow citizens, personality traits ascribed to oneself, personal experience, satisfaction with life, and approval of head of state. Figure 5.21 provides visualizations of these reasoning processes. Violence abroad is contained by ten reasoning processes of non-​protestors as opposed to two reasoning processes of protestors (z-​score:  2.00, p-​ value: 0.046), negative personality traits are included by seven reasoning processes of non-​protestors as opposed to zero reasoning processes of protestors (z-​score:  2.41, p-​value:  0.016), and personality traits ascribed to oneself and personal experience are included by similar proportions of reasoning processes of non-​protestors and protestors (z-​scores: 0.37 and 0.89, respectively). Fellow Citizens and Non-​Protest Beliefs that fellow citizens have negative personality traits trigger eight decisions to stay at home through indirect inferences. As shown by Figure 5.21B, they are an antecedent of disapproval of protest, which

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Violence abroad

Disapproval of protest

Decision to stay at home

Negative personality of the people

Disapproval of protest

Decision to stay at home

Personality of self

Disapproval of protest

Decision to stay at home

Personal experience

Disapproval of protest

Decision to stay at home

Satisfaction

Disapproval of protest

Decision to stay at home

Approval of head of state

Disapproval of protest

Decision to stay at home

Figure 5.21  Disapproval of protest.

triggers decisions to stay at home through direct inferences. Beliefs that fellow citizens have negative personality traits also trigger other key antecedents of decisions to stay at home, including a belief that there is no need for protest, which is a direct antecedent of seven decisions to stay at home (Figure 5.22A; also discussed later in the text). Moreover, they trigger safety, the most important antecedent of decisions to stay at

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(a) Negative personality of fellow citizens

Absence of need for protest

Decision to stay at home

Safety

Decision to stay at home

(b) Negative personality of fellow citizens

Positive personality of head of state

(c)

(d) Negative personality of fellow citizens

Protest

Violence by protestors

Decision to stay at home

Figure 5.22  Fellow citizens.

home protest at home (Figures 5.22B and 5.22C). Surprisingly, they also trigger beliefs that protests are happening at home, which have earlier been discussed as an important trigger of positive emotions and protest decisions. Figures  5.22C and 5.22D show how beliefs that protests are happening at home can trigger decisions at home when combined with beliefs representing safety and effort by the head of state (Figure 5.22C) and violence by the protestors (Figure 5.22D), a belief contained by similar proportions of reasoning processes by protestors and non-​protestors (z-​score: 0.89).

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Safety

No need for protest

Decision to stay at home

Satisfaction

No need for protest

Decision to stay at home

Approval of head of state

No need for protest

Decision to stay at home

Figure 5.23  Lack of protest need.

Absence of Need for Protest

Absence of need for protest is contained by eight reasoning processes of non-​protestors as opposed to zero reasoning processes of protestors (z-​ score: 2.58, p-​value: 0.010). It mostly triggers decisions to stay at home by direct inferences (7 of 8 reasoning processes). Its antecedents are safety, satisfaction, and approval of the head of state (in addition to negative personality traits discussed in the previous section). Each of these antecedents plays a major role in the reasoning processes by non-​protestors. So far, I have mainly discussed satisfaction and approval of the head of state as direct antecedents of decisions. Figure 5.23 provides examples in which these beliefs trigger decisions by indirect inferences via the belief that there is no need to protest.

NOTES 1. The following findings build on the analysis presented in Dornschneider (2019). 2. (53 choose 29) * (1 /​15)^29 * (14 /​15)^24 = 1.16e-​20

6

Conclusions

When Leila first heard about the Arab Spring, she thought someone was telling her a lie: “I could not believe it. It felt like a different world.” Ahmed could not have imagined mass uprisings happening in his country either: “It was incredible.” Similarly, Middle East analysts were surprised by the uprisings. Their field of research faced a crisis because it had focused for so long on explaining the stability of Arab authoritarianism. From an academic perspective, the Arab Spring could have appeared less surprising. Due to their internal logic, mass uprisings tend to catch the world by surprise, Timur Kuran (1991) had written decades earlier. “Our jaws could not drop any lower,” he quoted a European radio journalist who commented on the revolutions of 1989 (Kuran 1991, 7). As in the Arab Spring, “[wi]se statesmen, discerning diplomats, and gifted journalists were also caught off guard” (Kuran, 8). And as in the Arab Spring, academics were publishing books such as The Withering Away of the Totalitarian State  .  .  .  And Other Surprises (Kirkpatrick 1992), conceding that they had underestimated the decline of Communist states (Kuran 1991, 7). Following Kuran, the surprise of the Arab Spring lies in the nature of mass mobilization itself—​and not in the insufficiency of the academic literature that preceded it. Kuran’s (1991) explanation for the surprise of mass uprisings is that they are preceded by “preference falsification.” According to this concept, governments are “privately despised” but publically accepted until enough people express their true preferences, and it becomes Hot Contention, Cool Abstention. Stephanie Dornschneider, Oxford University Press (2021). © Oxford University Press. DOI: 10.1093/​oso/​9780190693916.003.0006

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safe for citizens to reveal their private preferences in public. However, this argument cannot explain why previous large-​scale protests in particular Arab countries did not lead to similar uprisings, or why the majority of the Arab population failed to participate in the mass protests. The analysis presented in this book has adopted the framework of belief systems and traced the reasoning processes by which people decided whether or not to participate in the Arab Spring. Unlike preference falsification, the analysis is not based on the assumption that people mobilized because of certain underlying preferences that they had formerly concealed. Rather, it considers any type of factor raised by a participant in the uprisings, including preferences and other internal factors, such as emotions or religious convictions, as well as external factors, such as the practices of authoritarian governments or existing mobilization structures. Modeling these factors as beliefs, it then traces how reasoning about these factors triggered decisions about actions. The findings show that decisions to participate in the Arab Spring were “hot”—​meaning they were triggered by positive emotions of hope, courage, solidarity, and pride—​whereas decisions to stay at home were “cool”—​meaning they were triggered by cognitive assessment about safety, satisfactory living conditions, and state approval. These findings speak to Kuran’s (1991) argument that mass uprisings are surprising by their nature. Hot cognition can often be characterized by speed or, in the words of Kahneman (2011), “thinking fast.” This suggests that decisions to join mass uprisings may happen more quickly than other types of decisions that are not based on hot cognition. This could explain why mass uprisings unfold so quickly and observers are surprised by how quickly they spread, as noted by Kuran many years ago.

GENERALIZABILITY

The study is based on 121 individuals from two countries in the Middle East. As such, it cannot account for the spread of uprisings in every country, which had very different trajectories. Nevertheless, this study contributes

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new insight by systematically tracing complex reasoning processes underlying the uprisings. The computer program presented in Chapter  4 traces inferences preceding decisions about actions from trillions of combinations of beliefs, which addressed factors as varied as emotions, external events, structural conditions, religious convictions, state behavior, state capabilities, the people’s behavior, the people’s capabilities, personality traits of state actors and the people, personal preferences, and self-​assessment. Given this complexity, it is unlikely that the analysis overlooked crucial factors related to the uprisings. The study is based on a double-​paired comparison that involves comparison groups that are not typically included in other analyses. To ensure that the findings captured the specific characteristics of protest decisions, I  contrasted the reasoning processes of protestors with the reasoning processes of non-​protestors. To ensure that the findings are not unique to a particular setting, I included individuals from two countries with very different protest levels and political outcomes. Some individuals were from Egypt, where the uprisings involved millions of people and led to the fall of the president, and other individuals were from Morocco, where mobilization levels were much lower and did not result in the resignation of the head of state. The finding that an usually large number of protestors from such different contexts displayed signs of hot cognition, whereas a large number of non-​ protestors from these contexts displayed signs of cool cognition suggests that similar patterns might be observed in other settings as well. Ultimately, the question of generalizability depends upon empirical research. Images available from mass media suggest that people participating in the Arab Spring in other countries could indeed have been motivated by hot cognition, especially by positive emotions of solidarity and national pride: During the uprisings in Tunisia, video footage showed people expressing solidarity with fellow citizens by embracing each other when gathering on the streets. Some of them were waving the Tunisian flag, displaying their pride in their country. Similar pictures emerged from Yemen, where people were marching side by side to call for the resignation of regime leaders. During the demonstrations, they chanted slogans

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exclaiming that their country belonged to them. By contrast, no similar images emerged from countries that were more or less excluded from the Arab Spring at the time, such as Algeria and Palestine. Images from other mass uprisings tell a similar story. Photos from the 1989 revolutions in Eastern Europe show people coming together to take down parts of the Berlin Wall. This act of solidarity was supported by a call for unity between West and East Germans. Images of the first moments when people from both sides united on the wall show them hugging each other and crying from joy. Photos taken before the eruption of violence on Tiananmen Square also indicate feelings of solidarity among the participants:  One photo shows a young student wearing a T-​shirt declaring “My life is yours” (Taylor, 2014). Later pictures show protestors holding up photos of the people who sacrificed their lives in the massacre on Tiananmen Square. Emotions of solidarity are also clearly visible in contemporary protest movements. Images from demonstrations by the Black Lives Matter movement show white people carrying signs that explicitly state “solidarity.” The findings of the present analysis are also consistent with other academic studies. Dawson’s (1994) Behind the Mule showed that feelings of “linked fate” have united African Americans politically, in spite of being economically polarized. According to Dawson, “it was much more efficient for African Americans to determine what was good for the racial group than to determine what was good for themselves individually” (10). Although Dawson does not examine mass uprisings as a particular type of political behavior, his findings underline the importance of people’s sense of belonging to a particular in-​group, as expected from social identity theory. The finding that solidarity with participants in the Arab Spring was crucial to decisions to join the uprising is consistent with these expectations. In the case of the Arab Spring, the in-​group moreover consisted of people from a historically disadvantaged majority rather than minority. Showing that even individuals from such a large group, which is more anonymous and prone to anti-​social behavior according to theories of deindividuation (Zimbardo 1969; Postmes and Spears 1998; Vilanova et al. 2017), decided to join based on solidarity with the protestors highlights

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the scope of this emotion and the strength of politicized identity (van Zomeren and Postmes 2008). The findings also speak to Putnam’s (2000) Bowling Alone, which makes an argument that individualism is related to low engagement in civic life. Individualism is “a calm and considered feeling which disposes each citizen to isolate himself from the mass of his fellows and withdraw into the circle of family and friends; with this little society formed to his taste, he gladly leaves the greater society to look after itself ” (de Tocqueville in Putnam 2000, 24). Because of this, individualism leads to a decline in “social capital” and in “the ways in which our lives are made more productive” (14). By contrast, “bonding social capital” becomes an important ingredient of civic life because it is a factor “undergirding specific reciprocity and solidarity” (22). The findings of this book are consistent with this argument, because the analysis shows that decisions to engage in political behavior were motivated by social ties, particularly solidarity with the protestors, national pride, courage to face the government, and hope that the protests would succeed. Adding to Putnam’s study of US democracy, the findings of this book suggest that bonding social capital can play a crucial role in authoritarian settings as well. The Middle East is an especially interesting comparison to the United States because it is a setting where “’cooperation’ is stressed more strongly . . . than ‘individualism’ ” (Tang and Ibrahim 1998). Following Putnam, this could be an indicator of social capital, which can result in wide-​ranging political activity, such as the Arab Spring.

POLICY IMPLICATIONS

The surprise inherent to mass uprisings underline the difficulty of anticipating the Arab Spring—​which confirms insights obtained from the large body of literature on authoritarian stability in the Middle East preceding the uprisings. Nevertheless, the surprising arrival of mass

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uprisings also emphasizes that authoritarian stability may disappear very suddenly and unexpectedly. At the time of writing, Western politicians had resumed talks about supporting “stable” authoritarian Arab rulers, ignoring lessons from the Arab Spring quoted at the beginning of this book. In 2017, President Trump declared that the new Egyptian President al-​Sisi was “a fantastic guy” (Beauchamp 2016) and that “safety seems to be very strong” (Revesz 2017). Meanwhile, the German government published a press release entitled “Merkel:  Germany Has an Interest in Stability in Egypt” (Die Bundesregierung 2017). In the document, Merkel touted economic support for the Egyptian regime, echoing the words of her foreign minister, who told economic and military leaders, “You have an impressive president” (Die Zeit 2016). Similarly, the British government reported that their Prime Minister Teresa May had “recognized that Egyptian prosperity and stability was vital to the region.” May said she wished “to open a new chapter in bilateral relations” and “looked forward to meeting [al-​Sisi] in the future” (UK Government 2016). Western talk about stability is accompanied by military aid: The United States is sending Egypt alone 1.3 billion USD on an annual basis, while the German government announced arms exports worth 19 million Euro in 2016, and the British government approved 84  million pounds for military equipment in 2015 (Curtis 2016). In the words of the Guardian, there is an “18bn arms race” that is “helping to fuel Middle East conflict” (Beaumont 2015). The behavior of Western rulers overlooks lessons from the Arab Spring “that the power of the people is much stronger than the people in power” (Ghonim 2012) contradicts concessions by leading experts on the Middle East discussed in the Introduction. It also strengthens states that have been using violence against political activists in the aftermath of the uprisings. In Egypt alone, there have been hundreds of reports of human rights violations—​such as mass killings of protestors (Amnesty International News 2016), enforced disappearances (Amnesty International Research 2016), systematic torture (al-​Nadeem Center for Rehabilitation of Victims

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of Violence 2016), and systematic persecution of political opponents (Freedom House 2016). The findings presented in this book suggest that state violence could motivate new protests against the government. The analysis has shown that beliefs that the government is attacking citizens can trigger solidarity with the victims and shore up courage to face the state, which can in turn trigger decisions to participate in mass uprisings. More generally, the analysis has shown that beliefs about governmental attacks can trigger “hot cognition,” which played a crucial role in the mobilization of the individuals studied in this book. Insofar as hot cognition is characterized by speed, my findings suggest that, in the presence of governmental violence against citizens, new waves of nonviolent protests could unfold very suddenly and seemingly unexpectedly—​surprising politicians, activists, and researchers alike. In an earlier research project, I  found that the belief that the state is attacking citizens was the main motivation for decisions to engage in political violence targeting the state (Dornschneider 2016b; Dornschneider and Henderson 2016). This finding suggests that current governmental attacks could also trigger new forms of violence against Arab rulers. Many Arab states that are currently violently repressing their citizens are indeed experiencing new violent attacks. In Egypt, violence against the state has been rising since al-​Sisi became President, according to START’s Global Terrorism Database. News reports suggest that “the number of people killed in jihadist attacks actually went up between September 2015 and May 2016” (Beauchamp 2016), while the number of attacks on the Egyptian state in the first months of 2015 was “equal” to the same time frame in 2014, “continuing to remain at a high rate” (Nader 2015). In 2017, the Irish Department of Foreign Affairs and Trade, warned that “[t]‌here is a heightened threat of terrorist incidents in Egypt at this time and security incidents can happen without warning” and that “[a] growing association between local extremist groups and Da’esh has heightened the risk of attacks against Westerners and Western interests in Egypt” (Department of Foreign Affairs and Trade 2017).1 These observations are in line with expectations that state violence can motivate violent resistance.

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My earlier research project also examined nonviolent resistance outside the context of mass uprisings. The findings confirm that nonviolent resistance can be motivated by beliefs about state violence—​although decisions to participate in nonviolent resistance on a smaller scale may be differentiated from decisions to engage in political violence by additional beliefs about low chances for success, availability of alternative means, and negative consequences of engaging in violence. In contrast, this book has shown that decisions to join mass uprisings during the Arab Spring involved beliefs that others were sacrificing themselves, participating in large-​scale protests at home, and creating a revolution abroad. These beliefs triggered positive emotions of solidarity, courage to face the government, hope for success, and national pride, all of which triggered decisions to participate in the uprisings. These findings underline the observation that for mass uprisings to spread, both beliefs that government is attacking citizens and beliefs that fellow citizens are resisting these attacks are crucial. At the present historical moment, we are not seeing new mass uprisings in the countries affected by the Arab Spring. In spite of high levels of state violence, citizens in the Arab world are not visibly acting against these attacks. This is consistent with expectations from theories on the repression-​dissident nexus according to which the effect of state violence on resistance is described by an inverted U-​shaped curve. Accordingly, state violence has a positive effect on resistance until it reaches a certain level, at which resistance begins to decline (e.g., Muller and Weede 1990). Nevertheless, research has also shown that the inverted-​U hypothesis may represent the effect of repression on resistance less adequately than the alternative backlash hypothesis, according to which dissidents increase their activity when faced with increasing levels of repression (e.g., Francisco 1995). The backlash hypothesis has been supported by evidence from various coercive environments (e.g., the German Democratic Republic, Czechoslovakia, and the Occupied Palestinian Territories; see Francisco 1995), but there is no related evidence from Egypt, Syria, or other Arab countries that have used severe violence against their citizens in recent years.

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The findings of this book make visible reasoning processes in which repression motivated participation in the Arab Spring by triggering emotions of courage. At the time of the outbreak of the uprisings, repression levels in the Middle East were also high, and mass uprisings were considered to be unlikely. Nevertheless, people began to mobilize when a Tunisian fruit vendor set himself on fire in protest against the police, who confiscated his cart. This was a seemingly random self-​sacrifice, which followed more than a dozen self-​immolations in other Arab countries. But today, we remember this act as the spark that triggered the uprisings, motivating friends and neighbors to pour into the streets, to sacrifice themselves, and to inspire millions of others to join them. Although this book cannot explain or predict when such triggering events will occur, it shows that once state attacks happen and people visibly oppose these attacks, uprisings can spread quickly through emotions of solidarity, courage, hope, and pride.

MILLIONS OF ARABS NEGATIVELY AFFECTED

Millions of Arabs have been adversely affected in the aftermath of the Arab Spring. These negative consequences underline the potential for new uprisings, although we are not seeing self-​sacrifices and new forms of mobilization at the moment. In many Middle Eastern countries, economic growth has stagnated (World Bank 2015). Tourism and related sectors, such as agriculture, have declined, which means that large numbers of Arabs have lost their jobs, have little or no work or have had to find different jobs. In Egypt, Leila was forced to look for a new source of income. Most of her students left the country, while no new students were arriving. When we last spoke, she was spending her days standing in front of a classroom of fifty small children. She said that instead of teaching Arabic, she now spent her time trying to get the children’s attention and prevent them from walking all over the classroom. One of her major concerns was the lack of furniture—​there were insufficient supplies chairs and tables for all the children. Leila herself was never able to sit down during the whole day she

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spent in class. When she returned home in the evening, she felt exhausted and needed to sleep to recover from the long day at school and hours of commuting between her home and workplace. She said she would like to return to teaching Arabic, but there were not enough students, and she would not be able to survive on the money earned by teaching them. People’s lives have not only been affected by dire economic prospects. New levels of state repression and violent attacks have made the daily conduct of life much more dangerous. In Cairo, commuting became so much of a challenge that companies developed a traffic app with a bomb scare hashtag to inform citizens about violence in the streets. Apart from danger on the streets, participating in political activism has become even riskier than it was under President Mubarak. Before the Arab Spring, there were some opportunities to protest against the regime. Now rulers are more aware of potential consequences of revolt and try to suppress opposition before it spills onto streets. Even popular prominent Egyptians are not safe from being arrested. Khaled ‘Ali, a well-​known human rights lawyer, was arrested and sentenced to three months in prison in 2017 for what was said to be a “committing a scandalous public act” (Human Rights Watch 2017a). Another victim is Mahmoud Abu Zeid (known as Shawkan), a journalist who took photos of governmental violence in Raba’a Square. He was detained without a charge, and Amnesty International warned he could be sentenced to death (Amnesty International Campaigns 2016).2 Human Rights Watch (2017b) notes that Egyptians are experiencing “intensifying repression of basic freedoms.” “Websites blocked, activists charged with terrorism” was the subtitle of its report, according to which there were two governmental campaigns arresting 190 activists, adding to tens of thousands of political prisoners who are already in Egyptian jails, where they are “routinely tortured” by the security forces. Some Egyptians managed to flee the country before the authorities came after them. In 2014, ‘Emad Shahin, who was working as a professor at the American University in Cairo, bought a plane ticket at the airport and left his home, leaving behind his wife and children. A  few months later, he was sentenced to death in absentia. “The court never specified the crime that I supposedly committed or produced a shred of evidence

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H ot C ontention , C ool A bstention

against my culpability,” he wrote (Shahin 2015). “On the same day that I was condemned to death, the court handed down the same fate to Morsi and more than 100 others in another case, including one Palestinian man who has been in an Israeli jail since 1996,” Shahin wrote. “Of course, he couldn’t possibly have committed the crime—​organizing a 2011 prison break—​for which he stood accused.” The court also sentenced to death two Palestinians who had already died, according to reports by Hamas. The situation has also become more dangerous for foreigners conducting research in the Middle East. The brutal murder of Giulio Regeni, most likely through the Egyptian security forces (Walsh 2017), shocked the academic community. Regeni was working on his PhD thesis, when he was abducted and brutally tortured to death. People around the world signed petitions for the investigation of Regeni’s murder. In the United Kingdom, where Regeni had been enrolled in a PhD program, more than ten thousand people signed a petition demanding that the British government ensure that a credible investigation take places. Around 150,000 individuals signed another petition on Change.org to “let the truth emerge” and “let the perpetrators pay for their crime.” Italy sent a delegation to Egypt. The European Parliament passed a resolution in which it condemned the circumstances of Regeni’s death, and Italy’s best investigative journalists arrived in Cairo to search for the truth. However, the case remains unresolved, despite evidence that includes an eyewitness report of video footage that shows how Regeni was stopped and led away by two civilian security agents (Fahim, Youssef, and Walsh 2016). To Egyptians, this is no surprise—​they are used to the “disappearance” and torture of political opponents. When commenting on Regeni’s death, many say: “He died like an Egyptian” (Walsh 2017). Despite these frightening circumstances, Leila felt optimistic about the future when I  last spoke with her. After decades of rule by Mubarak, it often still seems like a miracle that the president was ousted. For almost forty years, Leila, who has never left her country, was used to waking up in a place where everything appeared to depend on one person. His face smiled down at her from posters when she commuted to work, his security vehicles blocked the way when there was a political gathering, his police

Conclusions

145

shut down traffic in the city center when he needed to drive through, and his TV channels showed how he visited ill people, inaugurated hospitals and military academies, or discussed Egypt’s economic successes—​which never reached Leila, her neighbors, or anyone else she knew. Thinking that this person, who seemed so powerful for so many years, was forced to step down by the people still seems unimaginable to Leila. And to think that she participated in this event still makes her proud. Until recently, a photo from 2012 stood out on her Facebook page: It shows her lifting a finger to expose ink—​a sign that she participated in the 2012 elections, when she voted for the first time in her life. In her everyday life, Leila quickly forgets about that happy moment. But every now and then, her memories of the uprisings come back. Life is always hard in Egypt, she says. But who knows, the next revolution might be just around the corner.

NOTES 1. By contrast, politicians have made opposite statements:  President Trump has praised al-​Sisi for taking “a very tough approach,” which “has gotten the terrorists out” (Curtis 2016). “They were having tremendous problems before, tremendous problems,” Trump said. “He has done a very good job” (Ibid.). 2. Mahmoud Abu Zeid was released from prison in 2019.

APPENDIX 1 The Sample

Gender

Age Group

Occupation

Place

Behavior

Number

Male

30–​50

Engineer

Rabat

Protest

1

Male

30–​50

Employee

Rabat

Protest

2

Male

>50

Politician

Rabat

Protest

3

Male

>50

Journalist

Rabat

Protest

4

Male

30–​50

Muslim leader

Casablanca

Protest

5

Male

20–​30

Journalist

Casablanca

Protest

6

Male

30–​50

Academic

Rabat

Protest

7

Male

30–​50

Journalist

Rabat

Protest

8

Male

20–​30

Student

Marrakech

Protest

9

Female

20–​30

Psychologist

Rabat

Protest

10

Male

50

Housewife

Alexandria

Protest

17

Male

>50

Retired

Alexandria

Protest

18

Female

30–​50

Teacher

Cairo

Protest

19

Female

30–​50

Academic

Cairo

Protest

20

Male

30–​50

Banker

Cairo

Protest

21

Male

30–​50

Merchant

Cairo

Protest

22

Male

20–​30

Employee

Cairo

Protest

23

148

A ppendix 1

Gender

Age Group

Occupation

Place

Behavior

Number

Female

30–​50

Employee

Cairo

Protest

24

Male

20–​30

Employee

Cairo

Protest

25

Male

30–​50

Merchant

Alexandria

Protest

26

Male

30–​50

Employee

Cairo

Protest

27

Male

30–​50

Employee

Alexandria

Protest

28

Female

30–​50

Journalist

Cairo

Protest

29

Male

20–​30

Employee

Cairo

Protest

30

Female

20–​30

Student

Cairo

Protest

31

Facebook

Protest

32–50

Male

20–​30

Student

Marrakech

Non-​protest

1

Male

30–​50

Donkey rider

Marrakech

Non-​protest

2

Female

20–​30

Employee

Marrakech

Non-​protest

3

Female

50

Merchant

Rabat

Non-​protest

30

Male

20–​30

Manager

Rabat

Non-​protest

31

Male

30–​50

Employee

Marrakech

Non-​protest

32

Female

20–​30

Employee

Marrakech

Non-​protest

33

Male

>50

Employee

Rabat

Non-​protest

34

Female

>50

Employee

Rabat

Non-​protest

35

Male

30–​50

Journalist

Rabat

Non-​protest

36

Male

30–​50

Businessman

Cairo

Non-​protest

37

Male

20–​30

Employee

Cairo

Non-​protest

38

Male

30–​50

Banker

Cairo

Non-​protest

39

Male

50

Employee

Cairo

Non-​protest

43

Male

20–​30

Employee

Cairo

Non-​protest

44

Male

20–​30

Employee

Cairo

Non-​protest

45

Male

30–​50

Employee

Cairo

Non-​protest

46

Male

30–​50

Banker

Cairo

Non-​protest

47

Male

>50

Retired

Alexandria

Non-​protest

48

Female

>50

Housewife

Alexandria

Non-​protest

49

Female

30–​50

Teacher

Cairo

Non-​protest

50

Female

30–​50

Academic

Cairo

Non-​protest

51

Male

30–​50

Employee

Cairo

Non-​protest

52

Female

30–​50

Employee

Cairo

Non-​protest

53

Male

30–​50

Policeman

Cairo

Non-​protest

54

Male

30–​50

Unemployed

Cairo

Non-​protest

55

Female

>50

Employee

Cairo

Non-​protest

56

Female

>50

Cleaner

Cairo

Non-​protest

57

150

A ppendix 1

Gender

Age Group

Occupation

Place

Behavior

Number

Male

30–​50

Academic

Cairo

Non-​protest

58

Male

30–​50

Pharmacist

Cairo

Non-​protest

59

Male

>50

Employee

Cairo

Non-​protest

60

Male

20–​30

Employee

Cairo

Non-​protest

61

Male

20–​30

Employee

Cairo

Non-​protest

62

APPENDIX 2 Beliefs Identified by the Qualitative Analysis

Belief Type

Belief

Quote

Emotions

hope

“We will be liberated from oppression.”

courage

“I am not afraid of not returning.”

solidarity

“For the protection of the protestors we will resist any attack.”

prideNational

“I am a proud Egyptian.”

modesty

“I am overrated, I do not deserve to be in this position.”

moraloutrage

“I cannot bear this feeling of insult.”

satisfaction

“I am happy with my life.”

NOsatisfaction

“I am not happy with my life.”

curiosity

“We were very curious to see what was happening.”

NOhope

“We lost our hope.”

fear

“I am afraid.”

surprise

“I was very surprised; I could not believe it.”

a

State behavior crimeState

“The government is very corrupt.”

effortHeadOfState

“The king does everything for us.”

violenceState

“Three armored cars of riot police, tons of hired thugs, and officers came down to terrorize us.”

protectArmy

“The army came to protect the wishes of the people.”

violencePriorState

“Hassan II killed the entire opposition.”

passPowerSon

“Mubarak wanted his son to be president.”

violenceSon

“Gamal [Mubarak] was very violent.”

crimeArmy

“The army took over power against the wishes of many.”

crimePriorState

“By the time the people showed up at the urns, the elections had already been decided.”

NOeffortHeadOfState

“The king makes no effort for the people.”

isolatedPriorHeadOfState

“The previous president had the problem that nobody supported him.”

152

A ppendix 2

Belief Type

Belief

Quote

External conditions

safety

“We can move freely on the streets without fear from attacks.”

employment

“I have a job.”

poorLivingConditions

“There is poverty and hunger. I know that 30 million are dreaming of food.”

improvedLivingConditions

“People in the villages have better salaries now.”

violenceAbroad

“Look at the violence in Iraq.”

unityPeople

“Everyone is participating.”

ignorance

“We did not even know the name of our own foreign minister.”

NOemployment

“There is unemployment.”

NOsafety

“It is very unsafe.”

jobAvailability

“If you need work, you can go and look for it. There is no unemployment.”

goodLivingConditions

“Everyone here lives well.”

NOimprovementLiving Conditions

“The economy is getting worse.”

safety

“We can move freely on the streets without fear from attacks.”

deteriorationPolitics

“The political situation is getting worse and worse.”

improvedPoliticalSystem

“It is getting better. There is a political system again.”

freedom

“The boundaries of freedom are wide here.”

diversity

“We are an amalgam of different ethnicities.”

NOfreePress

“Investigative journalism is dead.”

freePress

“There is equality between opposition newspapers and governmental newspapers.”

NOunityPeople

“The country is divided now.”

history

“Our system is 400 years old.”

InternationalEnvironment

“Suddenly, there was a new international order.”

NOkingdom

“We are not a monarchy.”

NOsystem

“There is no system. Look at this street!” [points at roadworkers digging up the street]

NOalternative

“There was no alternative.”

possibilityProtest

“I thought about protesting for the first time.”

conspiracy

“In Egypt, they showed a pretty woman in front of the Nile. On al-​Jazeera, they showed the protests.”

uncertainty

“But one does not know what will happen next.”

humanNature

“This is human nature.”

A ppendix 2

153

Belief Type

Belief

Quote

Events

protest

“There were protests every day.”

revolutionTunisia

“It worked in Tunisia. The regime fell.”

changeHeadOfState

“Mohammad V followed his father.”

experiencePersonal

“I have a friend who was there. She told me.”

election

“We had many elections.”

resistanceArmy

“The army resisted the President.”

self-​sacrifice

“Four Egyptians have set themselves on fire to protest.”

interactProtestors

“I have access to the Movement of 20th February.”

NOpriorProtest

“I had never protested in my entire life.”

protectProtestors

“The people protect the protestors.”

effortSelf

“I make a big effort for myself all the time.”

NOsuccessPrivateLife

“I cannot manage to find a husband.”

effortPeople

“The people work very hard.”

obediencePeople

“The people do what they are told to do.”

NOeffortPeople

“Unfortunately, most people make very little effort.”

violenceProtestors

“I was harassed by the protestors.”

crimePeople

“The people are bad. There was a woman who accused a housekeeper of something he had not done. She talked to a friend in the police and arranged for the housekeeper to be arrested.”

priorProtest

“I had often participated in protests with them.”

needchangeState

“It is time to take power from this dictator.”

NOneedProtest

“I have no reason to join.”

needunityPeople

“The people must unite against the regime.”

needcourage

“Let us overcome the barrier of fear.”

needsolidarity

“We need solidarity with the protestors.”

needchangePeople

“You need to change the people, not the politicians.”

needstrengthProtestors

“We needed force.”

supportProtestors

“The protests spread very quickly.”

strengthProtestors

“Many people joined them from the desert. 25,000 protested from all categories of society.”

NOstrengthProtestors

“They [protestors] were always suppressed.”

successProtestors

“The protestors were successful.”

NOresultProtests

“They [the government] ignored us completely. None of our demands was met. We did not achieve anything.”

strengthPeople

“The thugs are afraid of entering their [the people] middle.”

NOsupportProtestors

“The people do not support the protests.”

Actions, protestors, people, self

Needs

Capabilities, protestors, people, self

154

Belief Type

Capabilities state

Personality, state

A ppendix 2

Belief

Quote

skillsProtestors

“The protestors are very knowledgeable.”

NOskillsProtestors

“Fevrier 20 are amateurs, but it is not their fault. They have no experience.”

NOskillsPeople

“Our people have no skills.”

supportSelf

“I made a big effort to educate myself.”

ageHeadOfState

“Mubarak was very old.”

superiorityArmy

“The army is the best thing in Egypt.”

strengthArmy

“Our army is very strong.”

strengthPriorHeadOfState

“Sadat won the war.”

NOskillPriorHeadOfState

“He was very unprofessional. He should have made changes earlier.”

skillsHeadOfState

“He knows what he is doing.”

strengthHeadOfState

“The king is very strong.”

strengthState

“Morocco is a very strong dictatorship.”

strengthNation

“Egypt is very strong.”

goodPersonalityHead OfState

“He is modest, he is a king of the people.”

badPersonalityHeadOfState “He is not a good person.”

Personality, protestors, people, self

Non-​state actors

badPersonalitySon

“Gamal [Mubarak] has a very bad personality: He drank alcohol and had a famous singer kidnapped.”

badPersonalityPriorHead OfState

“Hassan II was a very bad person.”

NOpersonalityPriorHead OfState

“He had absolutely no personality.”

interestPolitics

“For the first time in my life I became interested in politics.”

badPersonalityPeople

“You have to beat the people. Beatings and intimidation are the only means that work. If you tell someone to turn right on this street, he will not do it.”

NOinterestPolitics

“I am not interested in anything related to politics.”

goodPersonalitySelf

“It is my nature to stay away from trouble.”

goodPersonalityPeople

“The people are good; they do not use violence.”

personalitySelf

“It is against my personality.”

friends

“I have my friends.”

family

“I have my family.”

media

“We were watching them on Facebook.”

A ppendix 2

155

Belief Type

Belief

Quote

Religion

religiousExtremism

“We have religious extremism.”

NOreligion

“This is not Islam.”

godsMight

“God will protect us from injury.”

religion

“We have our religion.”

Attitude, state approvalHeadOfState

Attitude, protestors Preferences

“al-​Sisi is great.”

NOapprovalSon

“We did not want Gamal [Mubarak] to become president.”

approvalPriorHeadOfState

“Mubarak was good.”

NOresponsibilityCrimeHe adOfState

“The bribes were made by others, by ministers and family, not him.”

NOapprovalPriorHead OfState

“Mohamed V could not be like his father.”

NOapprovalHeadOfState

“We do not like him.”

NOapprovalPriorHead OfState

“Nobody liked him.”

NOapprovalProtestors

“I do not agree with them.”

NOimportanceIndividual

“My participation [in the protests] was not necessary.”

approvalProtestors

“May God support them.”

self-​priority

“I focus only on what is important to me personally.”

NOself-​priority

“I am not so important.”

prioritySafety

“There are more important things than protest: stability and safety.”

This type includes “emotion-​related traits” (see Lerner et al. 2015).

a

APPENDIX 3 Z-​Scores for Each Belief

Belief

Protestors

Non-​Protestors

Z-​Score

hope

23/​53 (0.43)

1/​68 (0.01)

5.74***

courage

10/​53 (0.19)

0/​68 (0.00)

3.74***

solidarity

9/​53 (0.17)

0/​68 (0.00)

3.53***

prideNational

7/​53 (0.13)

2/​68 (0.03)

2.14*

modesty

3/​53 (0.06)

0/​68 (0.00)

1.99*

moraloutrage

3/​53 (0.06)

0/​68 (0.00)

1.99*

satisfaction

1/​53 (0.02)

27/​68 (0.40)

4.89***

NOsatisfaction

0/​53 (0.00)

3/​68 (0.04)

1.55

curiosity

1/​53 (0.02)

0/​68 (0.00)

1.14

NOhope

4/​53 (0.08)

9/​68 (0.13)

1.00

fear

9/​53 (0.17)

4/​68 (0.06)

1.96

surprise

1/​53 (0.02)

1/​68 (0.01)

0.18

crimeState

38/​53 (0.72)

12/​68 (0.18)

5.99***

effortHeadOfState

5/​53 (0.09)

19/​68 (0.28)

2.53**

violenceState

11/​53 (0.21)

6/​68 (0.09)

1.87

protectArmy

0/​53 (0.00)

7/​68 (0.10)

2.41*

violencePriorState

1/​53 (0.02)

8/​68 (0.12)

2.05*

passPowerSon

3/​53 (0.06)

0/​68 (0.00)

1.99*

violenceSon

1/​53 (0.02)

0/​68 (0.00)

1.14

crimeArmy

0/​53 (0.00)

1/​68 (0.01)

0.89

crimePriorState

3/​53 (0.06)

8/​68 (0.12)

1.16

NOeffortHeadOfState

1/​53 (0.02)

0/​68 (0.00)

1.18

isolatedPriorHeadOfState

0/​53 (0.00)

1/​68 (0.01)

0.89

safety

0/​53 (0.00)

28/​68 (0.41)

5.33***

158

A ppendix 3

Belief

Protestors

Non-​Protestors

Z-​Score

employment

0/​53 (0.00)

20/​68 (0.29)

4.32***

poorLivingConditions

14/​53 (0.26)

35/​68 (0.51)

2.79**

improvedLivingConditions

1/​53 (0.02)

12/​68 (0.18)

2.78**

violenceAbroad

2/​53 (0.04)

10/​68 (0.15)

2.00*

unityPeople

5/​53 (0.09)

1/​68 (0.01)

2.00*

ignorance

3/​53 (0.06)

2/​68 (0.03)

0.75

NOemployment

0/​53 (0.00)

1/​68 (0.01)

0.89

NOsafety

2/​53 (0.04)

3/​68 (0.04)

0.17

jobAvailability

0/​53 (0.00)

4/​68 (0.06)

1.80

goodLivingConditions

4/​53 (0.08)

7/​68 (0.10)

0.52

NOimprovementLivingConditions 0/​53 (0.00)

1/​68 (0.01)

0.89

deteriorationPolitics

0/​53 (0.00)

3/​68 (0.04)

1.55

improvedPoliticalSystem

4/​53 (0.08)

3/​68 (0.04)

0.73

freedom

1/​53 (0.02)

0/​68 (0.00)

1.14

diversity

0/​53 (0.00)

1/​68 (0.01)

0.89

NOfreePRESS

1/​53 (0.02)

2/​68 (0.03)

0.37

freePRESS

0/​53 (0.00)

1/​68 (0.01)

0.89

NOunityPeople

6/​53 (0.11)

4/​68 (0.06)

1.08

history

8/​53 (0.15)

9/​68 (0.13)

0.29

InternationalEnvironment

4/​53 (0.08)

9/​68 (0.13)

1.00

NOkingdom

2/​53 (0.04)

0/​68 (0.00)

1.62

NOsystem

0/​53 (0.00)

1/​68 (0.01)

0.89

NOalternative

3/​53 (0.06)

2/​68 (0.03)

0.75

possibilityProtest

1/​53 (0.02)

0/​68 (0.00)

1.14

conspiracy

1/​53 (0.02)

2/​68 (0.03)

0.37

uncertainty

1/​53 (0.02)

2/​68 (0.03)

0.37

humanNature

1/​53 (0.02)

2/​68 (0.03)

0.37

protest

26/​53 (0.49)

21/​68 (0.31)

2.04*

revolutionTunisia

18/​53 (0.34)

11/​68 (0.17)

2.27*

changeHeadOfState

4/​53 (0.08)

16/​68 (0.24)

2.35*

experiencePersonal

0/​53 (0.00)

1/​68 (0.01)

0.89

election

2/​53 (0.04)

1/​68 (0.01)

0.81

resistanceArmy

1/​53 (0.02)

2/​68 (0.03)

0.37

self-​sacrifice

5/​53 (0.09)

0/​68 (0.00)

2.59**

A ppendix 3

159

Belief

Protestors

Non-​Protestors

Z-​Score

interactProtestors

8/​53 (0.15)

4/​68 (0.06)

1.68

NOpriorProtest

1/​53 (0.02)

0/​68 (0.00)

1.14

protectProtestors

1/​53 (0.02)

0/​68 (0.00)

1.14

effortSelf

0/​53 (0.00)

3/​68 (0.04)

1.55

NOsuccessPrivateLife

0/​53 (0.00)

1/​68 (0.01)

0.89

effortPeople

0/​53 (0.00)

1/​68 (0.01)

0.89

obediencePeople

0/​53 (0.00)

1/​68 (0.01)

0.89

NOeffortPeople

0/​53 (0.00)

1/​68 (0.01)

0.89

violenceProtestors

0/​53 (0.00)

1/​68 (0.01)

0.89

crimePeople

0/​53 (0.00)

1/​68 (0.01)

0.89

priorProtest

10/​53 (0.19)

6/​68 (0.09)

1.62

needchangeState

14/​53 (0.26)

6/​68 (0.09)

2.58**

NOneedProtest

0/​53 (0.00)

8/​68 (0.12)

2.58**

needunityPeople

3/​53 (0.06)

0/​68 (0.00)

1.99*

needcourage

1/​53 (0.02)

0/​68 (0.00)

1.14

needsolidarity

1/​53 (0.02)

0/​68 (0.00)

1.14

needchangePeople

0/​53 (0.00)

1/​68 (0.01)

0.89

needstrengthProtestors

2/​53 (0.04)

2/​68 (0.03)

0.25

supportProtestors

12/​53 (0.23)

3/​68 (0.04)

3.02**

strengthProtestors

4/​53 (0.08)

0/​68 (0.00)

2.30*

NOstrengthProtestors

3/​53 (0.06)

0/​68 (0.00)

1.99*

successProtestors

2/​53 (0.04)

0/​68 (0.00)

1.62

NOresultProtests

4/​53 (0.08)

7/​68 (0.10)

0.52

strengthPeople

2/​53 (0.04)

0/​68 (0.00)

1.62

NOsupportProtestors

1/​53 (0.02)

0/​68 (0.00)

1.14

skillsProtestors

1/​53 (0.02)

0/​68 (0.00)

1.14

NOskillsProtestors

2/​53 (0.04)

4/​68 (0.06)

0.53

NOskillsPeople

0/​53 (0.00)

1/​68 (0.01)

0.89

supportSelf

1/​53 (0.02)

0/​68 (0.00)

1.14

ageHeadOfState

3/​53 (0.06)

0/​68 (0.00)

1.99*

superiorityArmy

0/​53 (0.00)

2/​68 (0.03)

1.26

strengthArmy

0/​53 (0.00)

1/​68 (0.01)

0.89

strengthPriorHeadOfState

0/​53 (0.00)

1/​68 (0.01)

0.89

NOskillPriorHeadOfState

0/​53 (0.00)

1/​68 (0.01)

0.89

160

A ppendix 3

Belief

Protestors

Non-​Protestors

Z-​Score

skillsHeadOfState

0/​53 (0.00)

1/​68 (0.01)

0.89

strengthHeadOfState

4/​53 (0.08)

7/​68 (0.10)

0.52

strengthState

2/​53 (0.04)

3/​68 (0.04)

0.17

strengthNation

1/​53 (0.02)

3/​68 (0.04)

0.77

goodPersonalityHeadOfState

0/​53 (0.00)

12/​68 (0.18)

3.22**

badPersonalityHeadOfState

3/​53 (0.06)

0/​68 (0.00)

1.99*

badPersonalitySon

1/​53 (0.02)

0/​68 (0.00)

1.14

badPersonalityPriorHeadOfState

1/​53 (0.02)

0/​68 (0.00)

1.14

NOpersonalityPriorHeadOfState

0/​53 (0.00)

1/​68 (0.01)

0.89

interestPolitics

1/​53 (0.02)

0/​68 (0.00)

1.14

badPersonalityPeople

0/​53 (0.00)

7/​68 (0.10)

2.41**

NOinterestPolitics

0/​53 (0.00)

4/​68 (0.06)

1.80

goodPersonalitySelf

0/​53 (0.00)

1/​68 (0.01)

0.89

goodPersonalityPeople

1/​53 (0.02)

3/​68 (0.04)

0.77

personalitySelf

0/​53 (0.00)

2/​68 (0.03)

0.37

friends

2/​53 (0.04)

0/​68 (0.00)

1.62

family

10/​53 (0.19)

17/​68 (0.25)

0.80

media

6/​53 (0.11)

5/​68 (0.07)

0.75

religiousExtremism

1/​53 (0.02)

0/​68 (0.00)

1.14

NOreligion

1/​53 (0.02)

0/​68 (0.00)

1.14

godsMight

1/​53 (0.02)

0/​68 (0.00)

1.14

religion

1/​53 (0.02)

0/​68 (0.00)

1.14

approvalHeadOfState

1/​53 (0.02)

22/​68 (0.32)

4.24***

NOapprovalSon

3/​53 (0.06)

0/​68 (0.00)

1.99*

approvalPriorHeadOfState

0/​53 (0.00)

4/​68 (0.06)

1.80

NOresponsibilityCrimeHeadOfS tate

0/​53 (0.00)

1/​68 (0.01)

0.89

NOapprovalPriorHeadOfState

0/​53 (0.00)

1/​68 (0.01)

0.89

NOapprovalHeadOfState

8/​53 (0.15)

4/​68 (0.06)

1.68

NOapprovalPriorHeadOfState

0/​53 (0.00)

1/​68 (0.01)

0.89

NOapprovalProtestors

0/​53 (0.00)

11/​68 (0.16)

3.07**

NOimportanceIndividual

0/​53 (0.00)

1/​68 (0.01)

0.89

A ppendix 3

161

Belief

Protestors

Non-​Protestors

Z-​Score

approvalProtestors

4/​53 (0.08)

10/​68 (0.15)

1.22

self-​priority

0/​53 (0.00)

8/​68 (0.12)

2.58**

NOself-​priority

1/​53 (0.02)

0/​68 (0.00)

1.14

prioritySafety

0/​53 (0.00)

4/​68 (0.06)

1.80

*p < 0.05; **p < 0.01; ***p