AI-Driven: Social Media Analytics and Cybersecurity 9783031803338, 9783031803345

Table of contents :
Contents
About the Editors
Social Media Analytics
The Role of Social Media Networks in Enhancing School Libraries
1 Introduction
1.1 Problem Statement
1.2 Research Questions and Hypotheses
2 Literature Review
2.1 Social Media in Education
2.2 Libraries in the Digital Age
2.3 Case Studies and Best Practices
2.4 Benefits of Social Media Networks in School Libraries
2.5 Challenges of Integrating Social Media in School Libraries
2.6 Strategies for Effective Use of Social Media in School Libraries
3 Methodology
3.1 Method
3.2 Participants
4 Results
4.1 Demographic Characteristics
4.2 Impact of Social Media Networks on Enhancing Communication, Resource Accessibility, and Community Engagement in School Libraries
4.3 Challenges School Libraries Encounter When Integrating Social Media Networks
4.4 Best Practices and Successful Case Studies of Social Media Use in School Libraries
5 Discussion
6 Conclusion
6.1 Implications
6.2 Recommendations for Future Research
References
Machine Learning Approaches for Sentiment Analysis on Social Media
1 Introduction
2 Related Studies
2.1 Natural Language Processing
2.2 Machine Learning Approaches for Cyberbullying Detection
2.3 The State of the Art
3 Methods
3.1 Data Collection
3.2 Data Pre-processing
3.3 Preprocessing the Arabic Language for Sentiment Analysis to Detect Cyberbullying
3.4 Feature Extraction
3.5 Model Selection
3.6 Performance Measures
4 Results and Discussion
5 Conclusion
References
Harnessing Supervised Machine Learning for Sentiment Analysis in Urdu Text
1 Introduction
2 Related Work
3 Proposed Model
4 Results and Discussion
5 Conclusion and Future Work
References
Early Depression Detection from Social Media: State-of-the-Art Approaches
1 Introduction
2 Datasets
3 Machine Learning Approaches
4 Deep Learning Approaches
5 Transformers
6 Conclusion
References
Classification of Student Stress Levels Using a Hybrid Machine Learning Model
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Data Acquisition
3.2 Data Preprocessing
3.3 Exploratory Data Analysis
3.4 Feature Selection
3.5 Methods
4 Results
4.1 Feature Correlation
4.2 Target Outcome Correlation
4.3 Relation of Student Stressors in Other Regions to the Dataset
4.4 Classification Results
4.5 Limitations of the Methodology
4.6 Recommendations of Student Stress Management Methods
5 Discussion and Conclusion
References
From Disease Detection to Health Campaigns: The Role of Social Media Analytics in Public Health
1 Introduction
2 Health Data Collection and Research
3 Public Health Misinformation
4 Community Health Promotion
5 Conclusion
References
Effect of Social Media Networks on People’ Bahaviour in the Light of the Fourth Technological Revolution—A Perspective from Their Families
1 Introduction
2 Literature Review
3 Data and Methodology
3.1 Data
3.2 Methodology
4 Results
4.1 Cronbach’s Alpha Test
4.2 Exploratory Factor Analysis
4.3 Correlation Matrix
4.4 Results
5 Conclusion
References
An Efficient High-Speed Feature Extraction Analysis Methods of Digital Images in Social Media Platforms
1 Introduction
2 Processing Social Media Images
3 Color Image Feature Extraction Based on LBP
4 Classification and Feature Selection
5 Fuzzy C_Mean Clustering
6 Implementation and Experimental Results
7 Conclusion and Future Work
References
Cybersecurity and Digital Safety
Quantum Computing: Transforming Cybersecurity and Social Media in the Digital Age
1 Introduction
2 Quantum Computing
2.1 Impact of Quantum Computing on Social Media Platforms
2.2 Quantum Artificial Intelligence
2.3 Quantum Machine Learning
2.4 Quantum Deep Learning
3 Quantum Deep Learning for Cybersecurity
3.1 Quantum-Enhanced Threat Detection
3.2 Quantum Cryptography Integration
3.3 Attack Surface Reduction
4 Quantum Deep Learning for Social Media Analysis
4.1 Sentiment Analysis and Trend Detection
4.2 Anomaly Detection
4.3 User Behavior Prediction
5 Implementation Strategy
5.1 Hardware Requirements
5.2 Software Tools
5.3 Cybersecurity Case Study
5.4 Social Media Case Study
6 Conclusion
References
Factors Influencing on the Cybersecurity in an Emerging Economy
1 Introduction
2 Literature Review
3 Data and Methodology
3.1 Data Collection
3.2 Methodology
4 Research Results
4.1 The Test of the Cronbach’s Alpha
4.2 Correlation Matrix
4.3 Regressin Results
5 Conclusion
References
Innovative Security Measures: A Comprehensive Framework for Safeguarding the Internet of Things
1 Introduction
2 Literature Review
2.1 IOT in Cybersecurity
2.2 Machine Learning Techniques
2.3 Internet of Things (IoT)
2.4 Intrusions Detection System (IDS)
3 Materials and Methods
4 Results and Discussion
4.1 Results Graphs and Outcomes
5 Conclusion
References
Adaptive Social Network Quality (ASNQ) Model: A Conceptual Quality Model
1 Introduction
2 Literature Review
3 Adaptive Social Network Quality (ASNQ) Model
4 Discussion
5 Conclusion
References
Advanced Technologies and Their Broader Impacts
Authorship Attribution: Performance Model Evaluation
1 Introduction
2 Background
2.1 Authorship Attribution (AA)
2.2 Machine Learning (ML)
2.3 Feature Extraction in AA
3 Related Works
4 Research Methodology
4.1 Dataset Selection
4.2 Data Pre-processing
4.3 Features Extraction
4.4 Model Training
4.5 Model Evaluation
5 Results and Discussion
6 Conclusion
References
Digital Storytelling: Developing Twenty-First Century Skills in Arabic Language Education
1 Introduction
2 Method
3 Result and Discussion
4 Conclusion
5 Suggestion
References
Effective Techniques in Lexicon Creation: Moroccan Arabic Focus
1 Introduction
2 Lexicon Creation Techniques
2.1 Manual Annotation
2.2 Machine Learning
2.3 Corpus-Based Generation
2.4 Crowdsourcing
2.5 Hybrid Approach
3 Challenges and Best Practices
3.1 Challenges
4 Case Study: Moroccan Arabic Lexicon Creation
4.1 Overview of Moroccan Arabic and Its Linguistic Features
4.2 Data Collection and Preprocessing
4.3 Machine Learning Integration
4.4 Lexicon Inference
4.5 Lexicon Refinement
4.6 Discussion
5 Conclusion
References
The Role of Future Technology in the Preparation of Prospective Teachers
1 Introduction
2 Method
2.1 Research Design
2.2 Research Procedure
2.3 Research Subject
2.4 Research Ethics
2.5 Data Collection Techniques
2.6 Data Processing and Data Analysis Techniques
3 Results
3.1 The Role of Future Technology in the Preparation of Prospective Teachers
3.2 Future Technology Has a Very Important Role for Prospective Teachers
4 Discussion
4.1 The Role of Future Technology in the Preparation of Prospective Teachers
4.2 Future Technology Has a Very Important Role in the World of Education, Especially for Prospective Teachers
5 Conclusion
References
A Website Development of UCSI e-Market Hub: Transforming Unwanted Possessions
1 Introduction
1.1 Problem Statement
1.2 Aim
1.3 Objectives
1.4 Justification
2 Literature Review
2.1 The Evolution of Second-Hand Marketplace
2.2 Classified Advertisement
2.3 Sustainability and Circular Economy
2.4 Discussion of Existing and Similar Websites in Malaysia
3 Research Methodology
3.1 Research Approaches
3.2 Data Collection Method
4 Analysis and Design
5 Implementation
6 Conclusion
References
Investigating LLMs Potential in Software Requirements Evaluation
1 Introduction
2 Background and Literature Review
3 Research Methodology
4 Experimentation and Results
5 Conclusion
References
Advancements and Applications of Multimodal Large Language Models: Integration, Challenges, and Future Directions
1 Introduction
2 Background and Literature Review
3 Current Trends and Applications of Multimodal LLMS
4 Technical Overview of Multimodal LLMS
5 Model Specialization and Industry Applications
6 Challenges and Future Directions
7 Conclusion
References

Citation preview

Studies in Computational Intelligence 1180

Wael M. S. Yafooz Yousef Al-Gumaei   Editors

AI-Driven: Social Media Analytics and Cybersecurity

Studies in Computational Intelligence Volume 1180

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland Editorial Board Marco Dorigo , Université Libre de Bruxelles, Bruxelles, Belgium Andries Engelbrecht, University of Stellenbosch, Stellenbosch, South Africa Vladik Kreinovich, University of Texas at El Paso, El Paso, TX, USA Francesco Carlo Morabito, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy Roman Slowinski, Poznan University of Technology, Poznan, Poland Yingxu Wang, Schulich School of Engineering, Calgary, AB, Canada Yaochu Jin, Westlake University, Hangzhou, China

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, selforganizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI AG (Switzerland), zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

Wael M. S. Yafooz · Yousef Al-Gumaei Editors

AI-Driven: Social Media Analytics and Cybersecurity

Editors Wael M. S. Yafooz Taibah University Madinah, Saudi Arabia

Yousef Al-Gumaei Heriot-Watt University Edinburgh, UK

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-031-80333-8 ISBN 978-3-031-80334-5 (eBook) https://doi.org/10.1007/978-3-031-80334-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland If disposing of this product, please recycle the paper.

Contents

Social Media Analytics The Role of Social Media Networks in Enhancing School Libraries . . . . . Adamu Sa’ad Madaki, Abdulghader Abu Reemah A. Abdullah, and Mahmoud M. M. Musleh Machine Learning Approaches for Sentiment Analysis on Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reham Alhejaili Harnessing Supervised Machine Learning for Sentiment Analysis in Urdu Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abid Ali, Mehmood Ul Hassan, Muhammad Munwar Iqbal, and Habib Akbar Early Depression Detection from Social Media: State-of-the-Art Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahlam Alsaedi and Wael M. S. Yafooz Classification of Student Stress Levels Using a Hybrid Machine Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Victor Doma, Ali Abd Almisreb, Emine Yaman, Salue Amanzholova, and Nurlaila Ismail

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From Disease Detection to Health Campaigns: The Role of Social Media Analytics in Public Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Wael M. S. Yafooz, Yousef Al-Gumaei, Abdullah Alsaeedi, and Satria Mandala Effect of Social Media Networks on People’ Bahaviour in the Light of the Fourth Technological Revolution—A Perspective from Their Families . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Chien-Van Nguyen

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An Efficient High-Speed Feature Extraction Analysis Methods of Digital Images in Social Media Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Jawad H. Alkhateeb, Rashad Rasras, Ziad Alqadi, Mutaz Rasmi Abu Sara, Aiman Turani, and Rashiq Rafiq Marie Cybersecurity and Digital Safety Quantum Computing: Transforming Cybersecurity and Social Media in the Digital Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Syed Atif Ali Shah Factors Influencing on the Cybersecurity in an Emerging Economy . . . . 165 Hong Thi Nguyen Innovative Security Measures: A Comprehensive Framework for Safeguarding the Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Zeyad Ghaleb Al-Mekhlafi and Sarah Abdulrahman Alfhaid Adaptive Social Network Quality (ASNQ) Model: A Conceptual Quality Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Gana Sawalhi and Mohammad Abdallah Advanced Technologies and Their Broader Impacts Authorship Attribution: Performance Model Evaluation . . . . . . . . . . . . . . 203 Bodor Shalbi and Tawfeeq Alsanoosy Digital Storytelling: Developing Twenty-First Century Skills in Arabic Language Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Mahyudin Ritonga, Asrina, S. Purnamasari, Adam Mudinillah, Bambang, and Yayan Nurbayan Effective Techniques in Lexicon Creation: Moroccan Arabic Focus . . . . . 235 Ridouane Tachicart, Karim Bouzoubaa, and Driss Namly The Role of Future Technology in the Preparation of Prospective Teachers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Mahyudin Ritonga, Syaipuddin Ritonga, Adam Mudinillah, Julhadi, and Ilham Eka Putra A Website Development of UCSI e-Market Hub: Transforming Unwanted Possessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Joey Chan Yen Yun, Kasthuri Subaramaniam, Raenu Kolandaisamy, and Ghassan Saleh Aldharhani

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Investigating LLMs Potential in Software Requirements Evaluation . . . . 291 Najlaa Alsaedi, Ahlam Alsaedi, Amjad Almaghathawi, Mai Alshanqiti, and Abdul Ahad Siddiqi Advancements and Applications of Multimodal Large Language Models: Integration, Challenges, and Future Directions . . . . . . . . . . . . . . . 309 S. K. Ahammad Fahad, Daniel Wang Zhengkui, Ng Pai Chet, Nicholas Wong, Aik Beng Ng, and Simon See

About the Editors

Prof. Dr. Wael M. S. Yafooz is Professor in the computer Science Department, Taibah University, Saudi Arabia. He was Associate Professor in the information technology department at Al-Madinah international university (MEDIU)-Malaysia and faculty dean. He received his B.S degree in the area of Computer Science from Egypt in 2002 while M.S degree in computer Science from the University of MARA Technology (UiTM)-Malaysia in 2010 as well as a Ph.D. in Computer Science in 2014 from UiTM. He was awarded many Gold and Silver Medals for his contribution to a local and international expo of innovation and invention in the area of computer science. Besides, he was awarded the Excellent Research Award from UiTM. He served as a member of various committees in many international conferences. Additionally, he chaired IEEE international conferences in Malaysia and China. Besides, he is a volunteer reviewer with different peer-review journals. He supervised number of students at the master and Ph.D. levels. Furthermore, He delivered and conducted many workshops in the research area and practical courses in data management, visualization and curriculum design in area of computer science. He was invited as a speaker in many international conferences held in Bangladesh, Thailand, India, China and Russia. His research interest includes, Data Mining, Machine Learning, Deep Learning, Natural Language Processing, Social Network Analytics and Data Management.

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About the Editors

Dr. Yousef Al-Gumaei is an Assistant Professor at the Global College, Heriot-Watt University, Edinburgh, UK. He previously served as Assistant Professor and Deputy Dean of the Faculty of Engineering at AlMadinah International University (MEDIU), Malaysia. Dr. Al-Gumaei earned his BEng in Electronics and Communication from IBB University, Yemen, in 2003, followed by an M.Eng.Sc. in Wireless Communication in 2010, and a Ph.D. in Wireless Communication in 2014, both from the University of Malaya (UM), Malaysia. From 2019 to 2021, he held a Postdoctoral Research Fellow position in the Faculty of Engineering and Environment at Northumbria University, Newcastle Upon Tyne, UK. Currently, he leads the technology and computing team at Heriot-Watt’s Edinburgh campus and partners with Zhubanov Aktobe Regional University in Kazakhstan. He is an active member of the HW Technology Steering Committee, School Disciplinary Committee (SDC), and the Senate. Dr. Yousef was awarded the prestigious Graduate Research Assistantship Scheme (2012–2015) at UM, where he contributed to multiple funded projects during his postgraduate studies. He has authored several highimpact journal articles in areas such as wireless communication, game theory, and the Internet of Things (IoT). He regularly reviews for academic journals and conferences, and supervises students across different levels of study.

Social Media Analytics

The Role of Social Media Networks in Enhancing School Libraries Adamu Sa’ad Madaki , Abdulghader Abu Reemah A. Abdullah , and Mahmoud M. M. Musleh

Abstract This study examines the role of social media networks, such as Facebook, Twitter, and Instagram, in enhancing school libraries by improving communication, resource accessibility, and community building. The research aims to explore how these platforms can transform school libraries into dynamic, interactive learning environments that promote literacy and academic excellence. A quantitative approach was employed, utilizing structured questionnaires to gather data from 287 respondents, including school librarians, parents, students, and educators from four selected government secondary schools in Nigeria and Libya. Descriptive statistics were used to analyze the collected data, providing insights into the current use and impact of social media networks within school libraries. The findings reveal that while the integration of social media networks significantly enhances communication, promotes literacy, and fosters community engagement in school libraries, several challenges hinder its effectiveness. Key issues include privacy and safety concerns, unequal access to technology and internet connectivity, inadequate staff training, lack of resources, and the absence of social media policies and guidelines. The study identifies effective implementation strategies, such as developing clear social media policies, conducting regular staff training, strategic content creation and management, and ongoing monitoring and evaluation as critical to overcoming these challenges. This research highlights the transformative potential of social media in school libraries, demonstrating how these platforms can make libraries more accessible, A. S. Madaki (B) Department of Library and Information Science, Faculty of Education, Federal University Dutse, Dutse P.M.B 7156,, Jigawa, Nigeria e-mail: [email protected] A. A. R. A. Abdullah (B) Department of Computer, Faculty of Education, Bani Waleed University, 3544 Bani Waleed, Libya e-mail: [email protected] M. M. M. Musleh Strategic Information and Software Systems (SISS), Faculty of Information Science and Technology, University Kebangsaan Malaysia, 43000 Bangi, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_1

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interactive, and aligned with the digital habits of students. It provides evidencebased recommendations that can guide school libraries in adopting social media as a tool to better engage with the school community and support educational outcomes. The study is limited to selected schools in Nigeria and Libya, which may affect the generalizability of the findings. Future research could expand to other regions and explore the long-term impact of social media on student literacy and academic performance. The implications of this research suggest the need to foster digital literacy among students and staff, enhance access to technology, and continuously innovate social media integration strategies in educational settings. Keywords Social media integration · School libraries · Digital literacy

1 Introduction School libraries have long been essential educational resources, offering students access to books, research materials, and quiet spaces for learning. Traditionally seen as repositories of knowledge, their role has evolved significantly in the digital age, expanding beyond physical collections to include digital resources, online databases, and multimedia content [1]. In this context, school libraries are increasingly becoming dynamic learning hubs that support both academic and personal growth. The advent of technology has transformed how students access and engage with information, with social media networks emerging as powerful tools that can further enhance the reach and impact of school libraries [2, 3]. Social media platforms such as Facebook, Twitter, Instagram, and YouTube have significantly influenced various sectors, including education, by facilitating communication, information sharing, and community engagement. In school settings, these platforms offer innovative ways to connect with students, parents, and educators, creating a more interactive and engaging library environment [4]. This research xplores the role of social media networks in enhancing school libraries and examines how these platforms can support academic and literacy goals. By integrating social media, school libraries can promote reading, provide timely updates about resources and events, and foster a sense of community among students [5]. The importance of integrating social media into school libraries cannot be overstated. Today’s students, as digital natives, are accustomed to consuming and sharing information through social platforms. Leveraging these networks allows school libraries to meet students where they are, promoting library services in a manner that aligns with their digital habits [6]. This suggests that social media serves as a bridge, making the library more accessible and relevant to students, encouraging them to explore its resources and engage in reading and research activities. Additionally, these platforms enable librarians to highlight new books, share educational content, and create virtual reading communities that enhance the overall learning experience.

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This study underscores the potential of social media to revolutionize school libraries by enhancing their visibility, fostering digital literacy, and supporting academic excellence. By examining successful implementations and addressing the potential challenges associated with integrating social media networks in school libraries in developing countries, specifically using Nigeria and Libya as case studies, this research aims to provide a comprehensive understanding of how social media networks can be strategically employed to enhance school libraries, ultimately contributing to the academic and personal development of students.

1.1 Problem Statement School libraries have traditionally played a crucial role in fostering literacy and supporting academic growth. However, in the digital age, many libraries struggle to engage students and maintain their relevance [2]. Social media networks such as Facebook, Twitter, and Instagram present a unique opportunity to transform school libraries into dynamic, interactive learning environments [5]. Despite this potential, integrating social media into school libraries, particularly in developing countries like Nigeria and Libya, faces significant challenges, including privacy concerns, online safety risks, and unequal access to technology among students [7–9]. Moreover, a lack of strategic planning, inadequate staff training, and the absence of effective social media policies hinder libraries from fully leveraging these platforms [10–13]. This research aims to bridge these gaps by examining how social media can enhance communication, resource accessibility, and community building within selected school libraries in Nigeria and Libya. It seeks to provide actionable insights into best practices and strategies for overcoming these implementation challenges, ultimately helping to modernize and revitalize school libraries in the AI-driven digital era.

1.2 Research Questions and Hypotheses This empirical study aims to determine whether integrating social media networks into school libraries enhances their effectiveness and to explore the perspectives of school librarians, parents, students, and educators on evidence-based practices for using social media in school libraries. The study seeks to provide a comprehensive understanding of the role of social media in enhancing educational outcomes. The following research questions and corresponding hypotheses guide the study in addressing the research objectives: RQ1: How do social media networks impact communication, resource accessibility, and community building within school libraries in Nigeria and Libya? H1: Social media networks significantly enhance communication, resource accessibility, and community building within school libraries in Nigeria and Libya.

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RQ2: What challenges do school libraries in Nigeria and Libya face when integrating social media networks, and how can these challenges be effectively managed? H2: The challenges of integrating social media networks in school libraries, such as privacy concerns, online safety risks, and unequal access to technology, can be effectively managed through strategic planning, staff training, and the implementation of robust social media policies. RQ3: What are the best practices and successful case studies of social media integration in school libraries, and how do they contribute to promoting literacy and academic excellence? H3: Best practices and successful case studies of social media integration in school libraries significantly contribute to promoting literacy and academic excellence.

2 Literature Review 2.1 Social Media in Education Social media networks have become integral tools in the educational landscape, offering diverse opportunities to enhance learning, communication, and collaboration. Studies have highlighted that platforms such as Facebook, Twitter, and Instagram are increasingly used to connect students, educators, and the broader academic community [4, 12]. In educational contexts, social media provides opportunities for sharing information, fostering student engagement, and supporting collaborative learning beyond the classroom walls [12]. This indicates that social media allows for real-time communication and the dissemination of educational content, making it a valuable resource for engaging today’s digitally savvy students. However, integrating social media in education also presents challenges. Issues such as data privacy, cyberbullying, and the potential for distraction have been identified as significant concerns [1, 14]. Educators and institutions are often hesitant to fully embrace these platforms due to concerns over maintaining a professional boundary between educational content and students’ personal social media use. Despite these challenges, the benefits of incorporating social media into educational settings are increasingly recognized, particularly when implemented with clear guidelines and educational objectives.

2.2 Libraries in the Digital Age The role of libraries has evolved significantly in the digital age, transforming from traditional repositories of books into interactive learning hubs that utilize technology to enhance user experiences. Modern libraries are embracing digital tools, including

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e-books, online databases, and multimedia resources, to meet the needs of the 21st century learner [1]. This shift has not only expanded the resources available to library users but also enhanced the ways in which these resources are accessed and utilized. School libraries, in particular, are integrating technology to promote literacy, support academic research, and provide students with essential digital skills. Social media has emerged as a key component of this transformation, enabling libraries to engage more dynamically with their communities. Through social media, libraries can promote new books, advertise events, and provide updates on library services in a timely and accessible manner. These platforms also allow libraries to create interactive and engaging content, such as virtual book clubs and online reading challenges, fostering a sense of community among users [14]. By embracing social media, school libraries can become more visible and relevant, catering to the needs of students who are accustomed to digital interaction.

2.3 Case Studies and Best Practices The integration of social media into school libraries has been successfully demonstrated in various contexts, including schools in Nigeria and Libya, where innovative approaches have been adopted to enhance library services. In Nigeria, for example, social media has been effectively used to promote library events, reading programs, and new book arrivals, helping to bridge the gap between the library and its users [15]. One notable case is the use of social media networks by a Federal Government College in Jos, Nigeria, where the platform such as Facebook is used to engage students with educational content, share study tips, and promote literacy campaigns [16]. The library’s Facebook page has become a vibrant space for students to interact, ask questions, and stay informed about library activities, demonstrating the potential of social media to extend the library’s reach and impact. Similarly, in Libyan schools, social media platforms like WhatsApp and Facebook have been employed to connect students with library services, particularly in the context of limited access to physical resources due to political instability and economic challenges [13, 17]. These platforms are used to share digital resources, promote reading competitions, and facilitate virtual discussions, effectively keeping students engaged despite the physical limitations. These case studies highlight the adaptability and resilience of school libraries in leveraging social media to support their educational missions, even in challenging environments. Overall, the literature emphasizes that while challenges exist, the strategic use of social media in school libraries can greatly enhance their role as educational hubs. By adopting best practices and learning from successful case studies, school libraries can harness the power of social media to promote academic excellence, foster a culture of reading, and create a more connected and informed student community.

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2.4 Benefits of Social Media Networks in School Libraries Enhanced Communication and Engagement Social media networks have revolutionized how school libraries communicate and engage with their communities [18]. Platforms like Twitter, Facebook, and Instagram provide libraries with the tools to connect directly with students, parents, and staff, making information dissemination timely and interactive. Through these platforms, libraries can update their communities about new book arrivals, upcoming events, changes in library hours, and other important announcements. For example, a school library can use Twitter to share quick updates and links to educational resources, while Facebook can be utilized to create event pages for book fairs, reading weeks, and author visits, allowing users to RSVP and receive reminders. Instagram, with its visual appeal, offers an excellent platform for libraries to showcase book displays, highlight student book reviews, and share behind-the-scenes glimpses of library activities. This not only keeps the community informed but also creates a vibrant and engaging digital presence that appeals to students. By leveraging these platforms, school libraries can maintain constant communication with their audiences, ensuring that students and parents are always aware of the resources and services available to them. Promoting Literacy and Learning One of the most significant benefits of social media in school libraries is its ability to promote literacy and foster a culture of reading among students [19]. Libraries can utilize social media platforms to host virtual book clubs, where students can join discussions on selected books, share their thoughts, and even interact with authors. Reading challenges can also be promoted through social media, encouraging students to read a certain number of books within a specified period. These challenges can be gamified, with participants earning badges or certificates that can be shared on their social media profiles, further motivating them to engage with library resources. Social media also provides a platform for libraries to promote educational content, such as video book trailers, author interviews, and reading lists tailored to different age groups and interests. Online discussions hosted on platforms like Facebook or Instagram Live can offer students the opportunity to engage with content in a way that feels personal and interactive, enhancing their learning experience. By harnessing social media to promote literacy, libraries can create a dynamic and supportive environment that encourages students to explore and enjoy reading. Access to Resources and Information Social media networks serve as gateways to a wealth of digital resources, making information more accessible to students [20]. Through links shared on social media, libraries can direct students to e-books, online databases, educational websites, and research tools that support their academic needs. This ease of access is particularly beneficial for students who may not be able to visit the library physically due to scheduling conflicts, transportation issues, or other barriers.

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For instance, school libraries can use platforms like Twitter to share links to free e-books during school breaks, ensuring students continue to have access to reading materials even when the library is closed. Instagram Stories can be used to highlight library tutorials, guiding students on how to use digital resources effectively. Libraries can also curate and share content relevant to students’ coursework, such as articles, videos, and infographics, creating a more engaging and supportive learning environment. Furthermore, social media can be a valuable tool for teaching students digital literacy skills, such as evaluating online information and understanding how to navigate digital platforms responsibly. By incorporating social media into their resourcesharing strategies, libraries can enhance students’ access to knowledge and equip them with the skills they need to thrive in a digital world. Building a Library Community Social media networks play a crucial role in building a sense of community around school libraries [21]. These platforms provide a space where students, teachers, and parents can come together to celebrate reading, share recommendations, and participate in library initiatives. For example, libraries can host themed quizzes or trivia contests on their social media pages, encouraging students to test their knowledge on books, authors, and literary genres. These interactive activities foster a sense of belonging and make the library experience more engaging. Social media also allows students to share their own content, such as book reviews, artwork inspired by their readings, or even videos discussing their favorite books. This user-generated content can be showcased on the library’s social media pages, giving students a voice and encouraging them to actively participate in the library community. Additionally, libraries can use social media to celebrate student achievements, such as awarding the “Reader of the Month” or highlighting successful reading challenge participants, further enhancing the sense of community and recognition. By utilizing social media networks, school libraries can create an inclusive and interactive environment that extends beyond the physical space of the library. This not only enhances the library’s role within the school but also fosters a culture of collaboration, learning, and shared enthusiasm for reading.

2.5 Challenges of Integrating Social Media in School Libraries Privacy and Safety Concerns One of the primary challenges of integrating social media into school libraries is ensuring the privacy and safety of students. Social media platforms, while providing significant opportunities for engagement and learning, also pose risks related to data protection, cyberbullying, and exposure to inappropriate content [7]. The personal information of students, when shared on social media, can be vulnerable to misuse,

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raising concerns about data privacy. For instance, sharing photos or names of students participating in library events without proper consent can lead to unintended exposure [22]. Moreover, social media networks are often targeted by cyber threats, such as phishing scams and malicious links, which can compromise students’ online safety [22]. Schools must therefore implement strict privacy policies and guidelines to protect student information, including obtaining parental consent for students’ participation in online activities. Educators and librarians need to be vigilant and educate students on responsible social media use, emphasizing the importance of privacy settings, avoiding sharing personal information, and recognizing potentially harmful content [14]. A comprehensive approach to digital citizenship is essential to safeguard students and foster a safe online environment within school libraries. Digital Divide The digital divide represents a significant barrier to the effective integration of social media in school libraries. Unequal access to technology and internet connectivity among students means that not all students can benefit equally from social media initiatives [7]. While some students may have high-speed internet access and personal devices at home, others may rely solely on school resources, which may be limited or unavailable outside of school hours. This disparity can hinder the ability of certain students to participate fully in social media-driven library activities, such as virtual book clubs, online discussions, or digital reading challenges. Addressing the digital divide requires strategic efforts from schools to ensure equitable access to technology. This may involve investing in school-owned devices, expanding Wi-Fi access on school premises, and providing alternative ways for students to engage with library content offline [23]. Libraries can also develop inclusive programs that do not rely solely on digital participation, ensuring that students without access are not left behind. Bridging the digital divide is essential to maximizing the reach and impact of social media initiatives in school libraries and ensuring that all students benefit from these innovative approaches. Staff Training and Resources The successful integration of social media into school libraries also hinges on the availability of trained staff who are well-versed in using these platforms as part of the library’s strategy [24]. Many school librarians may not have prior experience managing social media accounts or developing content that engages students effectively. Without adequate training and resources, the potential of social media to enhance library services may remain underutilized [24]. Librarians need to be equipped with the skills to create engaging posts, moderate online discussions, and use analytics tools to measure the impact of their social media activities. Additionally, managing social media accounts requires time and effort, which may be challenging given the limited staffing and workload of many school libraries. Schools need to provide ongoing professional development opportunities for librarians, focusing on digital marketing, content creation, and social media management. Allocating resources such as dedicated time for social media planning and access

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to design tools can further support librarians in leveraging social media effectively. By investing in staff training and resources, schools can empower their librarians to harness the full potential of social media, ultimately enhancing the library’s role in supporting student learning and engagement. Integrating social media into school libraries offers numerous benefits, but it also presents challenges that must be carefully managed. Addressing privacy and safety concerns, bridging the digital divide, and investing in staff training are critical steps to overcoming these challenges and maximizing the positive impact of social media on school libraries.

2.6 Strategies for Effective Use of Social Media in School Libraries Developing a Social Media Policy Establishing a clear social media policy is crucial for the effective and safe use of social media in school libraries [25]. A well-defined policy provides guidelines on acceptable use, content management, and privacy protection, ensuring that the library’s social media activities align with the school’s educational goals and safeguarding measures. The policy should outline the responsibilities of staff managing social media accounts, including rules on what types of content can be posted, how to handle interactions with students, and measures to protect student data and privacy. Additionally, the policy should address the moderation of comments and the handling of inappropriate or negative feedback, helping to maintain a positive and respectful online environment. By setting clear boundaries and expectations, the policy serves as a valuable tool for protecting students and ensuring that social media is used responsibly to enhance the library’s services. Content Creation and Management Creating engaging and relevant content is essential to capture the interest of students and encourage their active participation in the library’s social media initiatives [26]. School libraries can use a variety of content formats, such as interactive posts, videos, infographics, and live events, to make their social media presence dynamic and appealing. For instance, libraries can host live book readings, Q&A sessions with authors, or virtual tours of new library resources to foster engagement. Social media platforms like Instagram and TikTok are particularly effective for sharing short, visually appealing content that resonates with younger audiences. Libraries can also encourage student involvement by featuring student-created content, such as book reviews, artwork, or recommendations, which not only boosts engagement but also fosters a sense of community. Planning a content calendar can help librarians manage their posts effectively, ensuring a consistent and strategic approach to content creation that aligns with the library’s goals.

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Monitoring and Evaluation Monitoring and evaluating the impact of social media efforts is essential to refine strategies and maximize effectiveness [27]. Libraries can use analytics tools provided by social media platforms, such as Facebook Insights or Twitter Analytics, to track key metrics like reach, engagement, and follower growth. These insights help librarians understand what types of content resonate most with their audience and identify areas for improvement. Additionally, gathering feedback from the school community through surveys, comment sections, or direct feedback sessions can provide valuable insights into the effectiveness of the library’s social media presence. Regular evaluation allows libraries to adapt their strategies, experiment with new content formats, and ensure that their social media efforts continue to meet the needs of students, parents, and staff. By continuously assessing the impact, libraries can enhance their online presence and further support their role in fostering a vibrant and connected learning community.

3 Methodology 3.1 Method This study primarily employed a quantitative approach to gather research data. A structured questionnaire was distributed via email and in hard copy to school librarians, parents, students, and educators in Nigeria and Libya. The questionnaire was designed to collect data within a naturalistic framework, with responses gathered both in person and online. The questionnaires were sent to heads of secondary schools, school librarians, teachers, parents, and students from Rumfa College Kano and Dutse Model International School in Nigeria, and Al-Farooq School Bani Walid and Al-Barq School Bani Walid in Libya—four government secondary schools selected for their experience in library management, education, and diverse perspectives. A total of 350 questionnaires were distributed, and 287 responses were received, yielding an 82% response rate. A Likert scale was used to measure respondents’ opinions on specific questions. For example: • School Librarians and Teachers: How do you perceive the role of social media in promoting literacy and learning among students? How do you use social media to engage students? • Students: What challenges have you encountered? How do you perceive the role of social media in promoting literacy and learning among students? • Heads of Schools: What policies are in place for social media use in the library? How do you support the library’s social media initiatives? • Parents: How do you view the school library’s use of social media to communicate with students and parents?

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The survey data from the questionnaires were exported to SPSS and Excel for analysis. Descriptive statistics were predominantly utilized to analyze the quantitative data.

3.2 Participants Purposive sampling, a popular method involving the selection of individuals from a prepared list that includes school librarians, parents, students, and educators, was used to recruit participants for this study [28]. Student participants were selected from senior secondary school pupils aged 15–18 years. Consent and assent forms were sent home with students via their classroom teachers. After the guardians signed the forms, the teachers distributed the questionnaires to the students who had returned their signed forms.

4 Results 4.1 Demographic Characteristics As demonstrated in Table 1, the demographic data indicate that the sample is relatively balanced in terms of gender, with a slight skew towards male participants (53.31% males and 46.69% females). Most participants hold a degree (54.70%), reflecting a well-educated sample, while 19.51% have a master’s degree, and only 2.44% possess a Ph.D., highlighting a limited presence of highly specialized education. A notable 23.34% are categorized as “Others,” suggesting qualifications outside traditional academic pathways. Professionally, the majority are school librarians and teachers (71.08%), emphasizing the study’s focus on educational settings, while students make up 17.07%, parents 10.10%, and heads of schools a minimal 1.74%, indicating limited representation from top administrative roles. Experience levels among participants vary, with 35.89% having 5–10 years of experience, 33.80% with 11–15 years, 13.24% with more than 15 years, and 17.07% with less than 5 years, aligning closely with the percentage of students in the study.

4.2 Impact of Social Media Networks on Enhancing Communication, Resource Accessibility, and Community Engagement in School Libraries To assess how social media networks impact communication, resource accessibility, and community building within school libraries in Nigeria and Libya, respondents

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Table 1 Demographic information of respondents Demographic information Gender Educational qualification

Professional position

Experience

Frequency

Percentage (%)

Male

153

53.31

Female

134

46.69

Ph.D.

07

2.44

Masters

56

19.51

Degree

157

54.70

Others

67

23.34

Heads of schools

05

1.74

School librarians and teachers

204

71.08

Parents

29

10.10

Students

49

17.07

More than 15 years

38

13.24

Between 11 and 15 years

97

33.80

Between 05 and 10 years

103

35.89

Below 05 years

49

17.07

were asked to rate the importance of these networks on a scale ranging from ‘not at all important’ to ‘extremely important,’ as reflected in Table 2. The findings indicated that all respondents (n = 287, 100%) unanimously agreed that social media platforms like Facebook, Twitter, and Instagram play a significant role in enhancing communication, resource accessibility, and community engagement in school libraries. A majority of the respondents (n = 228, 79.56%) agreed that social media networks enhance communication and engagement. Among these respondents, 263 (91.56%) strongly agreed that social media networks promote literacy and learning among educators and students, while 257 (89.56%) agreed that they provide easy and timely access to resources and information. Additionally, 194 respondents (67.46%) agreed that social media networks play a crucial role in building a sense of community around school libraries. These findings clearly suggest that by incorporating social media into their resource-sharing strategies, libraries can enhance students’ access to knowledge and equip them with the skills they need to thrive in an AI-driven digital world. This table captures the key findings related to the impact of social media networks in school libraries based on the responses.

4.3 Challenges School Libraries Encounter When Integrating Social Media Networks Respondents were asked about the challenges that school libraries in Nigeria and Libya face when integrating social media networks and how these challenges can

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Table 2 Impact of social media networks in school libraries Category

Statement

Frequency (n)

Percentage (%)

Enhanced communication and engagement

Social media networks enhance communication and engagement

228

79.56

Promotion of literacy and learning

Social media networks promote literacy and learning among educators and students

263

91.56

Access to resources and information

Social media provides easy and timely access to resources and information

257

89.56

Building a library community

Social media networks build a library community around school libraries

194

67.46

be effectively managed. As shown in Table 3, the majority of respondents (n = 251, 87.33%) revealed that a lack of privacy and safety for students, due to various content leading to distraction and a lack of user focus, is a major obstacle. This was followed by the digital divide (n = 240, 83.58%), which results in unequal access to technology and internet connectivity among students, meaning that not all students can benefit equally from social media initiatives and the inconsistency among growing social media users. Additionally, 231 respondents (80.56%) acknowledged a lack of adequate staff training and resources, and 219 respondents (76.23%) stated that librarians faced challenges due to the absence of feedback from users, making it difficult for them to analyze gaps and further improve the content delivery platform. Equally, 253 respondents (88.20%) strongly agreed that the lack of a social media policy and guidelines is one of the major challenges every school library should address to achieve the successful integration of social media networks. Conversely, the majority of respondents strongly agreed that to effectively manage these challenges, there is a need for developing a social media policy (n = 230, 80.20%), content creation and management (n = 221, 77.09%), monitoring and evaluation (n = 230, 80.05%), and regular staff training (n = 259, 90.11%). Table 3 above outlines the main challenges that school libraries face when integrating social media networks and the suggested solutions to address these issues.

4.4 Best Practices and Successful Case Studies of Social Media Use in School Libraries Respondents were asked, “What are the best practices and successful case studies of social media integration in school libraries, and how do they contribute to promoting literacy and academic excellence in their respective schools?” They were asked to respond on a Likert scale ranging from ‘not at all innovative’ to ‘extremely innovative,’ as shown in Table 2.

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Table 3 Challenges school libraries encounter when integrating social media networks Category

Statement

Frequency (n)

Percentage (%)

Enhanced communication and engagement

Social media networks enhance communication and engagement

228

79.56

Promotion of literacy and learning

Social media networks promote literacy and learning among educators and students

263

91.56

Access to resources and information

Social media provides easy and timely access to resources and information

257

89.56

Building a library community

Social media networks build a library community around school libraries

194

67.46

The study’s findings showed that most respondents (59%, n = 169) use Facebook as their primary social media platform and describe it as very creative in providing services. Therefore, it can be concluded that using online social media networks like Facebook to contact users is a suitable approach. When it comes to using X (formerly Twitter), 51% of respondents (n = 146) think it’s really creative for sharing library information with members on an online collaboration platform. In the third and fourth sequences, the majority of respondents stated that social media platforms like WhatsApp (n = 210, 73.19%) and Instagram (n = 142, 49.47%) are used creatively to promote libraries. Regarding the utilization of LinkedIn and TikTok as social media platforms, the majority of respondents—151 (52.77%) and 166 (57.87%), respectively—said that these platforms are incredibly inventive for their libraries to display and provide services. Regarding Snapchat, 204 respondents (71.13%) said it was novel for their libraries to share presentations and other resources to support study. Of the respondents, 142 (49.35%) and 144 (50.17%) thought Pinterest and Reddit were the least inventive tools for their library, indicating that these platforms are not very popular. Flickr was cited as creative (n = 144, 50.17%) for users sharing photos and quick videos pertaining to libraries. Finally, regarding YouTube’s contribution to promoting literacy and academic excellence in their respective schools, 196 respondents (68.27%) said it’s a very inventive, evidence-based method of sharing and showcasing library resources in video format that’s also well-known among users. Table 4 summarizes the respondents’ views on the use of various social media platforms in school libraries, indicating their perceived innovativeness and effectiveness in enhancing communication, resource accessibility, and community engagement.

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Table 4 Social media platforms used in school libraries Social media platform

Frequency (n)

Percentage (%)

Description

Facebook

169

59.00

Very creative in providing services

X (formerly Twitter)

146

51.00

Creative for sharing library information on an online collaboration platform

WhatsApp

210

73.19

Used creatively to promote libraries

Instagram

142

49.47

Used creatively to promote libraries

LinkedIn

151

52.77

Incredibly inventive for displaying and providing services

TikTok

166

57.87

Incredibly inventive for displaying and providing services

Snapchat

204

71.13

Novel for sharing presentations and other resources to support study

Pinterest

142

49.35

Least inventive tool, not very popular

Reddit

144

50.17

Least inventive tool, not very popular

Flickr

144

50.17

Creative for sharing photos and quick videos related to libraries

YouTube

196

68.27

Very inventive, evidence-based method for sharing and showcasing library resources in video format

5 Discussion The findings of this study demonstrate the significant role that social media networks play in enhancing communication, resource accessibility, and community engagement in school libraries in Nigeria and Libya. Social media platforms such as Facebook, Twitter, and Instagram have become essential tools for modern libraries, facilitating a dynamic and interactive environment where information is more accessible to students, educators, and the broader school community. A majority of respondents (n = 228, 79.56%) acknowledged that social media networks enhance communication and engagement, highlighting how these platforms enable libraries to reach out to users effectively. This improvement fosters a more inclusive and participatory environment within the school community, allowing for real-time feedback and interaction. Furthermore, 263 respondents (91.56%) strongly agreed that social media networks promote literacy and learning, illustrating the potential of these platforms to supplement traditional library resources and enhance educational outcomes. Social media networks also contribute significantly to the accessibility of resources, as indicated by 257 respondents (89.56%) who agreed that these platforms provide easy and timely access to information. This accessibility is critical in bridging the gap between students and library resources, especially in settings where

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traditional access might be limited due to physical or logistical constraints. Additionally, 194 respondents (67.46%) agreed that social media networks play a crucial role in building a sense of community around school libraries, further strengthening the connection between students, educators, and library staff. Overall, these findings suggest that the strategic use of social media can transform school libraries into vibrant, knowledge-rich environments that support academic success. By integrating social media into their operations, libraries not only improve communication and access but also foster a collaborative community that equips students with the digital skills necessary to thrive in today’s AI-driven world.

6 Conclusion This research has explored the transformative role of social media networks in enhancing school libraries, highlighting how platforms like Facebook, Twitter, and Instagram improve communication, promote literacy, provide access to resources, and foster a connected library community. Social media enables libraries to engage effectively with students, parents, and staff by offering updates on resources, events, and services. It supports literacy and learning through the promotion of reading programs and book discussions, and provides easy and timely access to digital resources, bridging the gap between students and the library. Therefore, the integration of social media allows school libraries to evolve from traditional information hubs into dynamic, interactive spaces that encourage a love for reading and learning.

6.1 Implications The integration of social media in school libraries has broader implications for educational institutions, highlighting the future potential of library services. Social media can revolutionize how libraries interact with students, transforming passive users into active participants in their learning journeys. By embracing these platforms, libraries extend their reach beyond physical boundaries, engaging students in ways that align with their digital habits. For educational institutions, this signifies a shift towards more connected, student-centered learning environments that prioritize accessibility and engagement. The ability to connect with students through familiar digital platforms opens new opportunities for promoting academic excellence and fostering a culture of continuous learning.

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6.2 Recommendations for Future Research While the benefits of social media in enhancing school libraries are evident, further research is needed to understand the long-term impacts of this integration. Future studies could examine how social media influences students’ reading habits, academic performance, and overall engagement with library resources. Research could also explore the effectiveness of different content strategies and the role of social media in supporting diverse learning needs. Additionally, investigating challenges such as digital equity and the digital divide within the context of school libraries would provide valuable insights to ensure that social media benefits all students equally. Addressing these areas will deepen our understanding of maximizing the positive impact of social media on school libraries and support their role in education.

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Machine Learning Approaches for Sentiment Analysis on Social Media Reham Alhejaili

Abstract Sentiment analysis is an essential task in natural language processing, and it involves evaluating attitudes, opinions, and emotions related to different entities. However, most research on sentiment analysis has been focused on English, leaving a gap for more diverse language studies, especially in Arabic. The prevalence of behaviors such as sharing private messages, spreading rumors, and making sexual comments has drawn significant attention due to their severe negative social consequences. Detecting bullying texts or messages on social media has become a fundamental interest for researchers worldwide. This study focuses on creating an efficient online method to identify abusive and bullying messages by combining natural language processing (NLP) with machine learning. The research assesses the performance of different machine learning algorithms using frequency-inverse document frequency (TFI_DF). This chapter delves into significant research on sentiment analysis of Arabic text using deep learning algorithms. It offers a detailed examination of Sentiment analysis to detect cyberbullying, outlining the methodologies employed for data collection, preprocessing, and analysis. An extensive literature review identified research gaps and practical techniques for cyberbullying detection across various languages, highlighting significant potential for advancement in Arabic. Consequently, this research evaluates specific machine learning algorithms for classifying Arabic datasets obtained from Twitter (now known as X). The Machine learning algorithms under investigation include Support Vector Machine (SVM), Support Vector Classifier (SVC), Naïve Bayes (NB), Multinominal Naïve Bayes (MNB), Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GBoost), K-nearest Neighbor (KNN), Adaptive Boosting (AdaBoost), Decision Tree (DT) and eXtreme Gradient Boosting (XGBoost), selected for their proven effectiveness in similar contexts. These algorithms’ performance was measured using well-established metrics such as accuracy, precision, recall, and F1-score. RF emerged as the top performer with an accuracy of 80.9%, showing promise compared

R. Alhejaili (B) Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_2

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R. Alhejaili

to current state-of-the-art methods. This research has the potential to significantly advance cyberbullying detection and stimulate further studies in this field. Keywords Arabic language sentiment analysis · Social media · Deep learning · Machine Learning (ML) · NLP

1 Introduction Opinion mining, also known as sentiment analysis, systematically identifies, extracts, and quantifies the emotional tone of subjective data. Social networking platforms have become the primary venue for young people’s social interactions. However, the widespread issue of cyberbullying on these digital platforms poses a significant and urgent threat to their well-being. According to data from the White House and the American Psychological Association, over 40% of American teenagers have experienced cyberbullying on social media platforms [1]. Similarly, recent studies from the UK highlight the severity of the issue, revealing that cyberbullying is more prevalent than physical bullying, with 12% of students reporting such incidents [2]. The frequency of cyberbullying incidents is increasing annually, presenting a complex issue with diverse motivations, methods, and manifestations. This escalating problem severely impacts the physical and mental health of victims, sometimes leading to suicidal thoughts. Beyond individual effects, cyberbullying has emerged as a significant public health concern, prompting increased research in psychology and computer science. This study aims to understand the nature of cyberbullying and develop effective strategies to detect and address it on social networks [3]. In the context of automated cyberbullying detection, where harmful verbal attacks are prevalent, current efforts mainly focus on analyzing textual features. Various methods have been developed to classify text and identify instances of cyberbullying, defined as the repeated posting of offensive or violent content on social media by individuals or groups with the intent to harm or upset others [4]. However, relying solely on textual analysis presents challenges in determining whether the content targets specific individuals or groups without contextual information. Additionally, inappropriate visual content within traditional text-based material adds another risk on social media platforms. Therefore, it is essential to incorporate diverse types of social media data, such as images, videos, comments, and social networks, into cyberbullying detection efforts [5]. The potential of multi-modal data integration offers hope for more effective cyberbullying detection in the future. Current approaches that focus on multi-modal information often prioritize specific modalities. Comments and visual content, for example, can provide valuable context that text alone cannot capture. Combining these different types of data could lead to more accurate and robust detection systems, which are beneficial and mandatory for effectively identifying and mitigating cyberbullying [6]. One study aimed [7] to enhance the understanding of context and behavior by utilizing contextual information. However, it did not account for the interaction

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between individual comments while analyzing their correlation. In contrast, on other study the authors [8] introduced a method incorporating visual elements to address the limitations of text-only analysis. While these methods outperform text analysis alone, they still need to improve in overcoming the constraints of single-mode information. Additionally, cyberbullying exhibits persistent and recurring hostile behavior, as highlighted by Agoston et al. [9]. Addressing conversations about cyberbullying without causing further harm presents a challenge, necessitating rapid detection of multi-modal bullying content to prevent further discussions. To tackle these evolving forms of sentiment analysis to detect cyberbullying, we propose a method that integrates textual, visual, and additional meta-information to determine if a post is related to bullying. Our Cyberbullying Machin learning Detection used to address these challenges. We suggest identifying offensive comments in blogs associated with cyberbullying. The textual content features are used to improve cyberbullying detection performance. The main contributions of this work include introducing a fresh perspective on problem-solving by leveraging and processing NLP to effectively manage different types of cyberbullying. We have applied (ML) techniques for detecting cyberbullying that independently analyzes textual, visual, and other forms of information. By integrating these components, we achieve efficient information merging, which aids in resolving the complex issue of cyberbullying more effectively. We also collected data from prominent social media platforms, including Twitter, to validate our methodology. Furthermore, we thoroughly analyzed how the data impacts cyberbullying detection, ensuring that our approach is practical. This chapter provides a focused study evaluates machine learning classifiers used in detecting cyberbullying, examining their performance based on accuracy, precision, recall, and F1 score. The primary contributions of this work include an extensive review of quality papers to identify widely used machine learning methods for cyberbullying detection on social media platforms and an evaluation of these classifiers’ usability and accuracy on a large, generic dataset. We developed an automated detection model incorporating feature extraction to enhance classifier efficiency. We compared the performance of seven commonly used ML classifiers for cyberbullying detection, utilizing Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. This comparison helped us to understand the strengths and weaknesses of (ML) text classification models. Our research aims to address the following questions: What machine-learning techniques are extensively used for detecting cyberbullying on social media platforms? How can an automatic cyberbullying detection model be developed with high accuracy and minimal processing time? How can feature extraction improve the detection process?

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2 Related Studies Over recent years, significant studies have been made in the detection of cyberbullying, offering hope for controlling or reducing its prevalence on social media platforms. Cyberbullying is particularly troubling as victims often struggle to cope with the emotional burden of violent, intimidating, degrading, and hostile messages. Addressing the harmful effects of cyberbullying requires comprehensive studies on its detection, prevention, and mitigation [10]. The Arabic language presents unique challenges for cyberbullying detection due to its complex morphology, rich vocabulary, and numerous dialects. Written from right to left, Arabic includes diacritics that can significantly alter word meanings. Additionally, its morphology allows for creating new words through various inflections, complicating the detection of abusive language. The diversity of Arabic dialects, ranging from Modern Standard Arabic (MSA) to regional colloquial varieties, further complicates the task of automatic text processing and cyberbullying detection [1, 2]. Arabic sentiment analysis encounters several significant challenges. Firstly, the morphological complexity of the language is a major hurdle. Arabic words undergo extensive transformations due to the root-based structure, which involves deriving words from roots through various affixes and inflections. This technique necessitates advanced preprocessing techniques to effectively normalize and stem words, demanding a deep understanding of the language’s structure and context [3]. Secondly, dialectal variation poses a problem, as Arabic comprises numerous dialects with distinct expressions and slang. For instance, the word for ‘good’ differs between ‘zain’ in Levantine dialect and ‘jayyid’ in Modern Standard Arabic. This variability complicates the use of machine learning models trained on one dialect for others, making it essential for sentiment analysis models to specialize in specific dialects or handle multiple dialects simultaneously [4]. Lastly, the need for high-quality annotated data further challenges sentiment analysis efforts. The training and validation of machine learning models are hindered by the limited availability of large, labeled datasets, which are essential for effective generalization. It is crucial to address this shortage to advance Arabic sentiment analysis [5].

2.1 Natural Language Processing Natural Language Processing (NLP) has a long history and draws from multiple fields to help computers work with human language by analysing different aspects of text. Its roots go back to the late 1940s, when efforts in Machine Translation began, marking the start of attempts to automate language translation. Over the years, NLP has expanded to cover many applications for analysing and understanding text. NLP is a compelling area of computer science that plays an increasingly vital role in everyday life. It powers virtual assistants like Siri and Alexa and language translation services. The main goal of NLP is to bridge the gap between computers

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and human language, aiming to create systems that can understand and process language on their own. Its many applications highlight its growing importance in today’s world. Arabic, the fourth most widely used language online, showcases how essential NLP is in enabling communication across different languages. Nearly 400 million people in 22 countries speak Arabic, which includes classical Arabic, which is mostly used in literature, official documents, and religious texts like the Quran. Modern Standard Arabic, which developed from Classical Arabic, is used in formal communication, while a range of regional dialects is spoken in everyday conversations. These dialects vary widely and lack a standardized format [11]. Arabic’s complex structure and variety of dialects present specific challenges. Its right-to-left script and lack of capitalization cause the shape of letters to change based on their position in a word. These challenges require specialized methods to address the language’s detailed structure and features [12]. These techniques are not just theoretical, they have practical uses in many areas. For instance, in information retrieval, they extract important information from large datasets. In text summarization, they condense long documents and provide accurate answers to user questions. These methods are crucial for making virtual assistants, search engines, and translation tools work smoothly, improving their efficiency and user experience. They also handle tasks like converting printed or handwritten text into digital form (optical character recognition), identifying word boundaries, clarifying word meanings in context (word sense disambiguation), and turning spoken language into text [13]. Key areas of focus include analysing speech sounds (Acoustic–Phonetic analysis), studying word structures and sentence formation (Morphological-Syntactic analysis), and interpreting meaning within broader contexts (Semantic-Pragmatic analysis). These methods help bridge the gap between human language and digital systems, driving technological and research innovation [14].

2.2 Machine Learning Approaches for Cyberbullying Detection Recent advances in machine learning have provided valuable tools for improving how we analyse sentiment, especially when detecting cyberbullying. These techniques are now being applied to Arabic, a more complex language with challenges. Below are some significant methods used to detect cyberbullying in Arabic [15]: 1. Naive Bayes Classifier: This is a popular method for classifying text, including spotting abusive language. It is simple but works well with large amounts of data. If each word or phrase stands alone makes it easier to sort through Arabic text and label it as either abusive or not, helping improve online content moderation [16].

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2. Support Vector Machines (SVM): SVMs are great for sorting different kinds of text, including determining when people are harmful or abusive. They work by finding the best way to separate different types of data. When tailored for Arabic, SVMs can handle more complex and high-volume data, making spotting harmful language on social media easier [17]. 3. Deep Learning Models: Deep learning has brought more advanced tools like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are good at identifying abusive words, while RNNs help understand the order and context of words, like when someone is being sarcastic or indirectly insulting. These methods detect patterns in Arabic text and spot abusive content [18]. 4. Pre-trained Language Models: Pre-trained models like BERT and its Arabic version (Arabic-BERT) have extensively detected harmful content. These models are trained on large sets of data. They can pick up on nuances in Arabic, like slang, dialects, and the context of conversations, making them highly effective at recognizing abusive language online [19]. As machine learning continues to evolve, these methods are becoming more critical in tackling cyberbullying and other forms of online abuse. By adjusting these approaches to work with the Arabic language, experts are helping make online spaces safer for everyone.

2.3 The State of the Art Researchers have been investigating ensemble machine learning techniques using public datasets, finding that combining multiple models often yields better results than single models. However, there is a notable lack of research on applying these techniques to detect cyberbullying, particularly in Arabic. This gap presents a promising area for further exploration and potential breakthroughs [20]. One study highlighted the success of boosting methods in multi-class text classification. By training a meta-learner on datasets from Reuters, AP Newswire, and Usenet, this approach consistently achieved higher precision than individual models. Another study applied Random Forest, an ensemble method, to detect cyberbullying on Twitter in English. The Random Forest model achieved an accuracy rate exceeding 91% by incorporating network features such as influence and community membership [21]. Further research has explored using two detectors: one focusing on message content and the other on social structures. This combined approach significantly improved performance compared to using a single classifier. Stacking methods, which merge multiple models, have also proven more effective than individual models alone. These techniques have been successfully used in various aspects of Arabic language processing, demonstrating their potential in cyberbullying detection [12, 22].

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Recent advancements in Arabic sentiment analysis show that blending traditional machine-learning techniques with modern deep-learning approaches enhances detection accuracy. Combining Support Vector Machines (SVM) and Naive Bayes with deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) provides a more detailed understanding of text and improves the identification of abusive content [23]. Improvements in data preprocessing methods, such as tokenization, stemming, and lemmatization, have also played a key role in enhancing model performance. These methods help standardize and clean text data, which is crucial for effective machine learning models [24]. Haidar and his team have made significant progress in detecting cyberbullying by exploring various techniques focusing on the Arabic language. They developed a machine learning system to detect cyberbullying in Arabic and English, including transliterating Arabic text into English [25]. Similarly, Iandani and his team created a web application to identify and report cyberbullying on social media platforms. They analysed Facebook comments using machine learning algorithms and classified them as positive, negative, or neutral. Nalini and her team developed a sentiment classifier for detecting bullying on Twitter. They analysed around five thousand tweets, categorizing them as either bullying or non-bullying. Their Naive Bayes classifier reached a precision of 70.5%, a recall of 70.6%, and an F-measure of 70.4% [26]. Globally, efforts to prevent cyberbullying and improve online safety, especially for children, are expanding. Despite many programs aiming to address and prevent cyberbullying, gaps remain. Victims often prefer seeking anonymous help online rather than speaking to adults. Web-based solutions provide accessible support as needed. Examples include the Kiva program in Finland, France’s Anti-Harassment campaign, and Belgium’s anti-cyberbullying initiatives [10]. Practical strategies for preventing cyberbullying should include: 1. Raising awareness about cyberbullying risks with targeted interventions [27]. 2. Providing education on health and emotional self-management. Their Naive Bayes model achieved about 79% accuracy, while their Support Vector Machine model assessed sentiment intensity with an accuracy of 58% [28]. 3. Promoting reactive measures (e.g., blocking messages) and preventive measures (e.g., increasing security awareness) [29]. 4. Offering resources to help victims manage stress and negative emotions [30]. 5. Addressing traditional bullying, as it often overlaps with cyberbullying [31]. 6. Teaching empathy, encouraging healthy internet behaviour, and promoting online labelling [32]. Despite these efforts, preventing cyberbullying remains a challenge. Awareness programs and peer mentoring are essential, especially during adolescence when peer influence is strong. Programs should focus on precise definitions of cyberbullying, firm policies, staff and parent training, and effective internet filtering technologies to support students [30].

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Studies indicate that social support can significantly help reduce the effects of cyberbullying. Unfortunately, many victims choose not to report these incidents and remain silent. This underscores the importance of developing effective methods for detecting and filtering harmful content on social media. Techniques such as Natural Language Processing (NLP) and Machine Learning (ML) are essential for identifying and addressing cyberbullying and supporting victims [33]. Recent advances in Arabic sentiment analysis bring new opportunities for improving cyberbullying detection systems and furthering research in this critical field [34]. Since cyberbullying is fundamentally a classification problem (determining whether an instance is offensive or non-offensive), this study employs several supervised learning algorithms to enhance classification accuracy and performance in detecting cyberbullying on social media, particularly on Twitter. The classifiers used in this study are detailed in the following section.

3 Methods In our methodology, we have developed a machine learning models to detect Arabic cyberbullying tweets, as shown in Fig. 1. The methodology involves the following steps: (1) data collection, (2) pre-processing, (3) extraction of different scenarios, (4) feature extraction, (5) classifications, and (6) evaluation metrics.

3.1 Data Collection It is essential to build Natural Language Processing (NLP) models to detect cyberbullying and collect relevant data, primarily from platforms like Twitter, where cyberbullying occurs multiple times. The model’s robustness depends on the quality and variety of the data collected. Data is typically sourced from social media platforms like Twitter, Facebook, Instagram, and Reddit, although Twitter was the main focus here. The collected data consists of text content such as comments, posts, and messages, which may contain offensive or abusive language. In addition to the text, metadata such as user profiles, timestamps, and other metrics (e.g., retweets, likes) gathered to provide extra context. For machine learning purposes, the data labelling by using pre-labelled datasets. These labels often include categories like hate speech, personal attacks, or derogatory remarks. Before the data is input into the model, it needs to go through a process of cleaning and normalization, as illustrated in Fig. 2.

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Fig. 1 Cyberbullying Arabic language data processing

Figure 2 shows the data collected from twitter. To train our model, we collected a dataset on Dec 19, 2023, from Twitter APIs, comprising 25,000 Arabic comments sourced from tweets. Dataset was labelled into bullying and non-bullying posts based on a list of standard and frequent bullying keywords in Arabic society, which were manually compiled from frequent words in the posts. This process helped us create an extensive list of bullying terms to classify the posts accurately.

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Fig. 2 Cyberbullying data collection

3.2 Data Pre-processing Figure 2 outlines the steps involved in data processing, which will be detailed in the following subsections. We used the Natural Language Toolkit (NLTK) for data pre-processing and tokenization, which involves splitting large text samples into individual words [35]. Before feeding the data into the model, it needs to be cleaned and normalized through the following steps which will be in the following Sect. 3.3.

3.3 Preprocessing the Arabic Language for Sentiment Analysis to Detect Cyberbullying Preprocessing Arabic text for cyberbullying detection is essential to ensure the data is clean, consistent, and ready for analysis. The unique characteristics of the Arabic language, such as its rich morphology, right-to-left writing direction, and complex script, require a customized approach.

3.3.1

Tokenization

The first step in preprocessing Arabic text is tokenization, which involves breaking the text into individual words or tokens. Tokenization in Arabic can be challenging due to the language’s agglutinative nature, where words often include prefixes, suffixes, and infixes. Effective tokenization requires advanced techniques to segment words while preserving their meaning accurately [36].

Machine Learning Approaches for Sentiment Analysis on Social Media

3.3.2

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Normalization

Normalization standardizes the text by converting different forms of a word into a single form. For Arabic, this includes normalizing various forms of the same letter, such as converting all forms of ‘alef’ to a standard form . Diacritics (harakat) are often removed to simplify the text and facilitate processing [37].

3.3.3

Stop Words Removal

Removing stop words is another crucial step. Stop words are common words that usually do not contribute much to the meaning of a sentence and can be filtered out to reduce noise in the data. Examples of Arabic stop words include “ ” (from), “ ” (in), and “ ” (on). By eliminating these stop words, the focus shifts to more meaningful words that contribute to detecting cyberbullying content [38].

3.3.4

Stemming and Lemmatization

Stemming and lemmatization reduce words to their root form. Stemming involves stripping affixes from words to obtain the root form, while lemmatization considers the context and returns a word’s base or dictionary form. For Arabic, advanced algorithms like the Khoja stemmer and sophisticated lemmatization techniques are used to effectively handle the language’s complex morphology [39].

3.3.5

Handling Dialects

Arabic encompasses many dialects, varying significantly from Modern Standard Arabic (MSA). Preprocessing must account for these variations by incorporating dialect normalization or dialect-specific processing techniques. This ensures that text from different Arabic-speaking regions is accurately analyzed and understood [40, 41].

3.4 Feature Extraction Feature extraction transforms raw data into a format suitable for use in machine learning models. A common and effective technique for this is the Term FrequencyInverse Document Frequency (TF-IDF) vectorizer, which helps identify and rank essential features within a dataset. TF-IDF is a technique used in text analysis that assigns weights to words based on their significance within a specific document and their overall importance across the entire collection of documents (corpus). It consists of two key components:

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1. Term Frequency (TF): This measures the number of times a term t appears in a document d relative to the total number of words in that document [41, 42]: n(t, d ) TF(t, d ) =  k n(k, d )

(1)

where n (t, d) is the count of term t in document d. 2. Inverse Document Frequency (IDF): This determines the importance of a term across the entire set of documents. It is calculated as the logarithmic ratio of the total number of documents N to the number of documents containing the term dfi [41, 42]. IDF(wt) =Log



N dfi



(2)

3. TF-IDF Weight: The TF-IDF value is obtained by multiplying the term frequency by the inverse document frequency [41, 42]: TF −IDF(wt) =TF(t, d )xLog



N dfi



(3)

This study used different TF-IDF analyzers to capture a range of text features. These approaches play a vital role in processing and analyzing text data, especially when applying machine learning models to identify instances of cyberbullying in Arabic content from social media platforms [43].

3.5 Model Selection Several classifiers were utilized in the conducted experiments, including: Random Forest (RF), Support Vector Classifier (SVC), Logistic Regression (LR), eXtreme Gradient Boosting (XGB), Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Stochastic Gradient Descent (SGD), Adaboost (ADA), Gradient Boosting, K-Nearest Neighbor (KNN) and Naïve Bayes (NB). 1. Random Forest (RF): Random Forest is a method that builds several decision trees during training and combines their results to improve accuracy and prevent overfitting. This technique is beneficial in text classification because it can handle many features and identify complex patterns in the data [10]. 2. Support Vector Classifier (SVC): SVC is part of the Support Vector Machines (SVM) family and works by finding the best way to separate data points into different classes. Due to its ability to handle large amounts of data, it is especially useful in natural language processing (NLP) for both binary and multi-class classification tasks [44].

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3. Logistic Regression (LR): This straightforward model is often used for binary classification problems. It estimates the probability that a given input falls into a specific category. In NLP, it is commonly used for tasks like sentiment analysis and spam detection [45]. 4. eXtreme Gradient Boosting (XGB): XGBoost is an advanced form of Gradient boosting designed for speed and performance. It builds a series of decision trees, with each new tree focusing on correcting the mistakes of the previous ones. This method is highly effective in text classification, mainly when working with large datasets [10]. 5. Multinomial Naive Bayes (MNB): This version of the Naive Bayes algorithm is tailor-made for multi-class classification problems. Its suitability for text classification tasks, such as spam filtering and sentiment analysis, is due to its ability to calculate the probability of each class based on the assumption that features (words) are independent of each other [46]. 6. Support Vector Machine (SVM): SVM is a widely used classification model that finds the best boundary, or “hyperplane,” to separate data points into different categories. In natural language processing (NLP), SVMs are useful for text classification tasks like spam detection, sentiment analysis, and document categorization. They work well with high-dimensional data, such as word vectors, and can be adapted for binary and multi-class classification. SVMs can also utilize various kernel functions to handle complex data relationships [47]. 7. Decision Tree: A decision tree model splits data into smaller parts to create a series of decision paths based on different features. In NLP, decision trees can classify text by creating branches representing decision rules, often based on factors like word frequency or specific phrases. Although they are easy to understand and interpret, decision trees can sometimes become too specific to the training data, a problem known as overfitting [48]. 8. Stochastic Gradient Descent (SGD): SGD is used for training models, particularly linear ones like logistic regression and SVMs. It updates model parameters incrementally with each training example, making it efficient for working with large datasets. In NLP, this approach is valuable for handling extensive text data, as it allows the model to converge faster than traditional gradient descent [25]. 9. Adaboost (ADA): Adaptive Boosting, or Adaboost, is an ensemble learning method that combines several weak classifiers to create a stronger one. In NLP, Adaboost is used for text categorization and sentiment analysis tasks. The method trains classifiers sequentially, with each model focusing on correcting the errors made by the previous ones. It assigns more weight to misclassified instances, helping the final model improve its overall accuracy [49]. 10. Gradient Boosting: Gradient Boosting is another ensemble method that builds models in a sequence, where each new model aims to correct the errors of the previous one. In NLP, it is effective for tasks such as document classification, named entity recognition, and sentiment analysis. Gradient boosting creates a more accurate final model by minimizing errors from earlier models. It works with different text features, but proper tuning is necessary to prevent overfitting and achieve the best results [50].

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11. K-Nearest Neighbor (KNN): KNN is a simple classification method used in natural language processing (NLP). It works by finding the closest examples in the feature space to a new input, like a text or document, and assigning the most common label among these nearby examples. KNN applies to various text classification tasks such as sentiment analysis, document categorization, and spam detection. A key benefit of KNN is that it does not need a separate training phase; instead, it classifies data using the stored examples directly. However, KNN can be demanding in computation when dealing with large datasets, as it requires distance calculations between the new input and every stored instance [46]. 12. Naïve Bayes (NB): Naïve Bayes, a probabilistic model that relies on Bayes’ theorem, is unique in that it assumes features are independent of each other. Despite this simplifying assumption, it performs effectively in several NLP tasks, including text classification, spam filtering, and sentiment analysis. The model calculates the likelihood that a piece of text belongs to a particular category based on the frequencies of the words it contains. Variants like Multinomial Naïve Bayes are especially useful for text data because they consider word frequencies in documents. Naïve Bayes models are appreciated for their speed and efficiency, making them well-suited for applications with a large number of features [51].

3.6 Performance Measures After training, the model’s performance is assessed using task-appropriate metrics, including accuracy, precision, recall, F1 score. Evaluation metrics originally developed for Information Retrieval (IR) have been adapted and extended to various domains, including machine learning (ML). These metrics are essential for evaluating the performance of both IR and ML systems. Key metrics include Recall, Precision, Area under the ROC Curve, and F-Measure. 1. Recall quantifies the proportion of relevant documents (or values) that are correctly retrieved from all relevant documents, both retrieved and not retrieved. It is also known as the sensitivity of a system. The formula for Recall is: Recall =

(TP) [59] (TP + FN )

(4)

where: – (TP) =True Positives. – (FN) =False Negatives. [1, 2]. 2. Precision measures the proportion of retrieved documents (or values) that are relevant or correct. This metric is also referred to as the accuracy of a system.

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The formula for Precision =

(TP) [60] (TP + FP )

(5)

where: – (TP) =True Positives. – (FN) =False Negatives. 3. F-Measure, introduced by van Rijsbergen in 1979 [52], integrates Precision and Recall into a single metric. It is a weighted harmonic mean designed to balance the trade-off between Precision and Recall. The formula for F-Measure is a parameter that adjusts the balance between Precision and Recall, ranging from 0 to ∞. when the score is 0, Recall is ignored; and when is ∞, Precision is ignored; and when the score is 1, both metrics are equally weighted. F1-Score is a specific case of the F-Measure where the score is equal 1, providing equal weight to Precision and Recall. The formula for F1 is as follows [53, 54]: F1 =

2 ∗ (Precision ∗Recall ) [62] (Precision + Recall )

(6)

F1 =

(2 ∗TP ) [62] ((2 ∗TP ) + FP + FN )

(7)

Or:

4 Results and Discussion In this section, we will discuss our findings and results. Table 1 presents the performance measurements obtained from our models. Table 1 illustrates that the Random Forest (RF) classifier achieved the highest accuracy and F1-score, reaching 80.9 and 87.94%, respectively, when using TFIDF as the feature extraction method, making it the top performer in the table. The highest recall score, however, was obtained by the Multinomial Naive Bayes (MNB) classifier, which achieved 95.45% with TF-IDF. This approach significantly improves the classifier’s overall accuracy. Next, we will present the heatmap diagrams (Figs. 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13).

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Table 1 The Machine learning Results for our Dataset Model

Precision

Recall

F1-score

Accuracy

Random Forest (RF)

82.82

93.73

87.94

80.9

Support Vector Classifier (SVC)

81.51

94.67

87.59

80.09

eXtreme Gradient Boosting (XGB)

83.5

91.22

87.19

80.09

Logistic regression (LR)

80.74

95.29

87.41

79.62

Multinomial Naive Bayes (MNB)

80.44

95.45

87.31

79.39

Support vector machine (SVM)

81.62

92.63

86.78

79.04

Decision tree

80.31

94.67

86.9

78.81

Stochastic Gradient Descent (SGD)

82.38

90.9

86.43

78.81

Adaboost (ADA)

83.7

88.55

86.06

78.69

Gradient boosting (GB)

83.78

87.46

85.58

78.11

K-nearest neighbour (KNN)

84.44

69.74

76.39

67.98

Naïve Bayes (NB)

84

36.2

50.6

47.49

Fig. 3 Cyberbullying detection using ADA

5 Conclusion Sentiment analysis to detect Cyberbullying is a significant issue in the virtual world, particularly affecting the most vulnerable communities. While sentiment analysis is a powerful tool, it is essential to note that it has its limitations. For instance, it may struggle with sarcasm or irony and may not always accurately interpret complex emotions. This chapter proposes a method to detect cyberbullying in Arabic comments on social media platforms, which has the potential to significantly contribute to the field of social media analysis. The approach utilizes Machine Learning, specifically the Naive Bayes classification algorithm based on Bayes’ theorem, to determine the likelihood of comments being categorized as bullying or

Machine Learning Approaches for Sentiment Analysis on Social Media Fig. 4 Cyberbullying detection using DT

Fig. 5 Cyberbullying detection using GB

Fig. 6 Cyberbullying detection using KNN

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38 Fig. 7 Cyberbullying detection using LR

Fig. 8 Cyberbullying detection using NB

Fig. 9 Cyberbullying detection using RF

R. Alhejaili

Machine Learning Approaches for Sentiment Analysis on Social Media Fig. 10 Cyberbullying detection using SGD

Fig. 11 Cyberbullying detection using SVC

Fig. 12 Cyberbullying detection using SVM

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Fig. 13 Cyberbullying detection using XGB

not RF Forest (RF) classifier achieved the highest accuracy and F1-score, reaching 80.9 and 87.94%, using TF-IDF as a feature extraction approach. Future work can involve classifying comments based on the entire dataset, not just bullying keywords, and employing various data mining algorithms such as SVM, K-Means, regression, and others. In the future, our goal is to collect comprehensive information from different social media platforms like Facebook, TikTok, and YouTube. We will use deep learning algorithms to analyze the data, which will help create better decisionmaking systems. We also intend to use high-performance quantum machine learning models to improve machine learning techniques, such as artificial neural networks and ensemble models.

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Harnessing Supervised Machine Learning for Sentiment Analysis in Urdu Text Abid Ali, Mehmood Ul Hassan, Muhammad Munwar Iqbal, and Habib Akbar

Abstract Sentiment analysis is the process of extracting sentiments from data and is used to monitor the popularity of products, brands, services, and individuals. While sentiment analysis has been extensively applied to languages like English and Chinese over the past decade, languages such as Urdu and Hindi have largely been overlooked by the research community. This article introduces a machine learning approach for sentiment analysis in Urdu. The data was collected from various blogging websites and annotated by human experts. Four widely recognized supervised machine learning classifiers—Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes, and Multinomial Logistic Regression (MLR)—were employed to classify sentiments from the preprocessed data. The results were meticulously analyzed and compared, revealing that KNN outperformed the other classifiers, achieving an accuracy of 92% on the test dataset. Keywords Machine learning · Sentiment analysis · Urdu language · NLP

A. Ali (B) · M. M. Iqbal · H. Akbar Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan e-mail: [email protected] H. Akbar e-mail: [email protected] A. Ali Department of Computer Science, GANK(S) DC KTS, Haripur, Pakistan M. Ul Hassan Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran 66446, Saudi Arabia H. Akbar Department of IT, The University of Haripur, Haripur, Pakistan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_3

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1 Introduction The widespread availability of affordable handheld devices and internet access has encouraged users to express their opinions on social media and blogs [1]. These platforms host a variety of content, including product reviews, messages, news, and more. For example, a survey shows that 65 billion messages are exchanged daily on WhatsApp alone, while approximately 9,000 tweets are posted on Twitter every second. Additionally, about 4.4 million blog posts are published on the internet each day [2, 3]. The data shared on social media and blogs is in multiple languages, including English, Urdu, Chinese, and others. Text classification, a crucial technique in various applications such as text filtering, document organization, news categorization, and web searches, is often language-specific, with most systems being developed for English [4]. However, limited work has been done on sentiment analysis in the Urdu language, which presents challenges due to its complex morphology [5]. Sentiment analysis (SA) can be performed at three levels: document level, sentence level, and aspect level. Document-level SA categorizes an entire document as positive, negative, or neutral, while sentence-level SA categorizes individual sentences. Aspect-level SA, on the other hand, identifies fine-grained opinion polarity towards specific aspects within a given class. Sentiment analysis can be approached in two ways: using machine learning or lexicon-based techniques [6–8]. In the machine learning approach, a dataset is created and used to train a model that predicts the sentiment of new data. In the lexicon-based approach, sentiment lexicons are developed, where each word is assigned a degree of positivity or negativity, which then determines the overall sentiment of a document or sentence based on the sum of these values. Over the past decade, sentiment analysis has garnered significant attention from researchers, leading to the development of numerous tools and techniques for analyzing various languages. Despite this progress, SA models still face challenges such as sarcasm detection, handling negations, compound phrases, and word repetition [9]. Like other languages, Urdu is widely used for data sharing on the internet. However, it is evident from the literature that techniques used for sentiment analysis in other languages may not be directly applicable to Urdu. The popularity of Urdu sentiment analysis (UrSA) has been growing in recent years, driven by the increasing amount of Urdu content online [10, 11]. This study aims to analyze and investigate machine learning techniques for UrSA. Although substantial work has been done on semantic analysis and sentence classification in Urdu, few studies have focused on the performance analysis of machine learning models for Urdu sentiment analysis. This paper concentrates on text classification of Urdu language data. After preprocessing the data and extracting features, various supervised machine learning classifiers were applied to Urdu blogs [12]. The dataset was annotated by native Urdu speakers and labeled according to the sentiment expressed in each sentence. The key contributions of this article are as follows: • Development of a novel Urdu dataset for UrSA. • Annotation of the dataset by native Urdu speakers.

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• Implementation of an efficient supervised model for UrSA with improved accuracy over previous models. The remainder of this article is organized into five sections: Sect. 2 discusses related work, Sect. 3 explains the proposed methods, Sect. 4 presents the study’s findings, and Sect. 5 concludes the article.

2 Related Work Exploring the sentiments expressed in text is very meaningful because the text is one of the easiest and most effective ways to interpret and express emotions. This paper summarizes two types of recent research studies for textual sentiment analysis, i.e., traditional and machine learning techniques. In recent years, various conventional approaches have been employed to analyze textual data [13]. Bostanci et al. [14] used an additional Naive Bayes classifier to perform sentiment analysis on amazon movie reviews with an optimum accuracy of more than 70%. Naz et al. [15] proposed a novel method for Twitter sentiment analysis using the SVM classifier. In this study proposed model got an accuracy of 80%. An ensemble method for sentiment analysis was proposed by Rathi et al. [16]. They concluded that existing machine learning techniques could not deliver better sentiment classification results, so they built an ensemble method that combines SVM with decision tree and finally improved more than 2% in overall classification performance. An Urdu sentiment analysis system by using RUSA data set was proposed by Mehmood et al. [17]. The data set contained 11,000 reviews of products. They presented three dis- tinct techniques to achieve text normalization. On a nonstandardized RUSA dataset, the first technique established the BL accuracies. The second and third research utilized six well-known phonetic algorithms and TERUN to optimize the RUSA data set. The resulting data was used for the training of machine learning models. According to the empirical review, the results obtained by TERUN were statistically significant and comparable to those obtained by wellknown phonetic algorithms. The TERUN word normalization technique was then generalized from a corpus-specific to a corpus-independent technique. The study concludes that text normalization enhances machine learning algorithms’ accuracy rate. Another result was that a phonetic algorithm designed for one language would not generalize well to other languages unless it becomes properly updated to fulfill the phonological needs of its target languages.

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Nasim et al. [18] present an article on Urdu SA by combining various linguistic and lexical features. Their work focuses on the development of Urdu SA system for Urdu tweets. A Markov chain model was used to design the approach in this paper. The help of Twitter API gathered the data set. The proposed model was trained on that data, and the model was able to predict people’s attitudes based on their tweets. They also discussed the challenges and limitations of Urdu SA systems. Their proposed model accurately predicted positive emotions because of fewer positive tweets in the data set. A word-level translation framework was proposed by Asghar et al. [19] to enhance the Urdu SA lexicon. The framework was developed by combining different linguistic and lexicon resources, such as the English word list, SentiWord Net, the bilingual English-to- Urdu dictionary, Urdu grammar improvements, and a novel scoring mechanism. Their model consisted of three major modules, i.e., the collection of Words in English for an opinion, the translation of English words into Urdu, and sentiment scoring using SentiWordNet and manual scoring. The results of this study were compared with other studies based on precision, recall, and F1 score. After the comparison, it was found that the proposed method got a satisfactory result. Mukhtar et al. [20] present supervised machine learning technique for Urdu SA. The data of various blogs and categories, i.e., sports, politics, products, etc., were used for classification purposes. This study trained three supervised ML models, namely KNN, LibSVM, and J48, to classify Urdu data. After the successful training and testing phases, all models were compared in terms of accuracy, precision, and recall. Lib SVM was very similar to KNN in terms of efficiency, and its accuracy is on par with KNN on average, but it is the slowest of the three classifiers used in the analysis. Mukhtar et al. [21] discussed that handling of Intensifiers is very crucial to maintain higher accuracy in Urdu SA. Urdu intensifiers were gathered and saved in a different place. The output of sentiment classification of the Urdu text with intensifiers was controlled by several rules. Every sentence was analyzed one by one and categorized as positive, negative, or neutral. Based on the experiments conducted, accuracy improved by 5%, indicating a statistically significant improvement in sentence classification accuracy. As a result, it was determined that intensifiers must be considered while conducting sentiment analysis. Sentiment analyzers will greatly increase their efficiency if intensifies are handled correctly. Furthermore, single intensifiers, whether amplifiers or down toners, were found to appear very commonly in Urdu text than continuous intensifiers. This research was effective in the case of consecutive intensifies, where another amplifier precedes the amplifier. The amplifier, accompanied by the down toner, is not addressed in this study. Table 1 shows the current state of the art work on Urdu Sentiment Analysis.

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Table 1 Previous work on Urdu sentiment analysis Authors

Year

Machine learning

Lexicon

Language

Accuracy (%)

Peng et al. [13]

2017

✓

✓

Chinese

72

Seref et al. [14]

2018

✓

x

English

70

Naz et al. [15]

2018

✓

x

English

80

Rathi et al. [16]

2018

✓

x

English

84

Mehmood et al. [17]

2019

x

✓

Roman Urdu

81

Nasim et al. [18]

2020

✓

✓

Urdu

86

Asghar et al. [19]

2019

x

✓

Urdu

86

mukhtar et al. [20]

2020

✓

Urdu

89

mukhtar et al. [23]

2018

x

✓

Urdu

83.42

Rehman et al. [4]

2016

x

✓

Urdu

66

Khan et al. [22]

2018

✓

✓

Urdu

75

Yafooz [24]

2024

✓

x

Arabic

98

Alhejaili [25]

2021

✓

x

Arabic

87

3 Proposed Model Training a computer to make accurate results when any data is given to it is called machine learning. It is a type of artificial intelligence in which we earn a computer system so intelligent that it can perform various tasks without human intervention. Today machine learning is everywhere, from classifying an image to self-driving cars. Different machine learning techniques can be used for language processing. Supervised learning techniques are machine learning techniques that need labeled data for building models. In this type of learning, the correct data labels are given, and models are trained with proper tags after training the model to predict or classify the new data based on labels given on training time. A supervised-learning model will learn to recognize the clusters of pixels and shapes correlated with each number, given appropriate examples, and ultimately recognize handwritten digits that can accurately differentiate between numbers 6 and 9 or 26 and 29. Figure 1 illustrates the process of sentiment analysis. In the recent decades’ machine learning is widely used for text analysis. The proposed study uses supervised machine learning for sentiment analysis of Urdu language. Figure 4 illustrates the proposed model of the study. The proposed approach used for UrSA consists of three layers. Using a beautiful soup web scraping tool, the first layer of data was gathered from various Urdu blogs. After collecting data, it was not in a standard form. The second layer is data preprocessing; it is performed in which unnecessary data like stop words, URLs, and white spaces, etc., were removed from the data. Various steps were performed in preprocessing phase. These layers include Filtering URL links, tokenizing words, removing stop words and blank spaces, Removing redundant letters such as Removing non-Urdu letters and numbers, and normalization of Urdu text. The algorithm for preprocessing is

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Fig. 1 Process of supervised machine learning

listed below. Proposed Process for UrSA. Figure 2 shows the proposed process of UrSA. Algorithm 1 Preprocessing of Data

After that, a contiguous sequence of “n” terms from a given sequence of text was constructed as a unigram (n = 1), bigram (n = 2), and trigram (n = 3). Urdu stop words were used for removing those stop words in this study. A list of 650 stop words was created for this purpose. Some of the stop words are shown in Fig. 3. The data set is divided into three categories. From the data, 39% of data was labeled as positive, 37% as unfavorable, and 24% as neutral. Figure 4 present the gathered data set and percentage of each category. The whole data set was divided

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Fig. 2 Proposed process for UrSA

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Fig. 3 Stop words

Fig. 4 Dataset division

into training and testing sets. Seventy percent of data was used for training, while 30% of data was used for testing purposes. Training data was used for the training of classifiers, while testing and testing data sets were used to evaluate trained models. In the next step, various features were extracted from the cleaned data to be used for the training of the machine learning model. The data cleaning is done by using the Urdu Hack library, and xlm Roberta model was used for tokenization and feature extraction of the data set in this study. After feature extraction model training was done on the training data set. In this step, the machine learning model learns the data patterns, and after successful training, a classifier is built.

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Fig. 5 Proposed process for Urdu sentiment analysis

For the classification of each review as positive, negative, or neutral, four supervised ML models, i.e., Support Vector Machine (SVM), Naive Bayes, KNN, and Multinomial logistic regression, are used. For the testing of classifiers, the crosstesting technique was used. The k-10 folds cross-validation method is used for the evaluation. We searched thoroughly for all parameter combinations with k = 3 for KNN, alpha = 0.01 for Naive Bayes, SVM with a linear model, and L1 penalty for Multinomial Logistic regression. After the successful training of classifiers, testing the data set was used to test the reliability of classifiers. In the end, outputs of the classifier are presented in graphical form so that analysis can be performed easily. Figure 5 shows the proposed process of UrSA. The Algorithm for the proposed models is listed below:

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Algorithm 2 Model Training

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4 Results and Discussion The features were chosen using the Xlm Roberta framework. With unigrams and bigrams, SVM, NB, Multinomial logistic regression, and KNN models were trained on Urdu data set. Figure 6 represents the accuracies of proposed models for UrSA. The results are analyzed and compared with previous studies on Ursa using machine learning. The results clearly show that the proposed model KNN outperforms other models and studies discussed in the literature review section. One reason for better accuracy of the model is the manual annotation of data, and the second is the usage of both manual and automatic preprocessing techniques for Urdu language. The results can further be improved by using deep learning techniques for UrSA using Fig. 7. A confusion matrix is a matrix that shows how well a classification model (“classifier”) performs on a set of test data for which the correct labels are known. The confusion matrix is a simple and easy way to understand the performance of machine learning models. This matrix contains four elements true positive (TP), true negative (TN), false positive (FP), and false-negative (FN). True positive means that the model predicts the value as an actual value was also true. True negative is the negative prediction on negative values. In false negative, the model predicts false while actual

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Fig. 6 Accuracies of proposed models

Fig. 7 Accuracies of proposed models

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value was true. It is also called a type II error. The last component is falsely positive, in which the model predicts true, but the actual value is false. Figure 8 illustrates the SVM classifier’s confusion matrix for the proposed Urdu data set. Figure 11 the percentage of the diagonal elements of matrix represent the accurate predictions for the classes. Off-diagonal elements show classification errors, with 0.33% of label objective misclassified as negative and 0.15% of label positive misclassified as negative. Figure 9 present the confusion matrix for the Naïve Bayes classifier. Nave Basie outperformed SVM in classifying the majority mark, as seen in the first element of Fig. 10. Since Naïve Basie is dependent on probabilities, the majority class’s prior probabilities exceeded the minority class’s. Therefore, for the most part, all data points were categorized as members of the majority class. Figures 10 and 11 demonstrate the major deficiencies of Multinomial Logistic Regression and KNN incorrectly Fig. 8 Confusion matrix for Naive Bayes model

Fig. 9 Confusion matrix for SVM model

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Fig. 10 Confusion matrix for multinomial logistic regression

classifying minority instances. SVM also outperforms Naive Bayes, Multinomial Logistic Regression, and KNN to classify minority cases correctly. The KNN’s low performance shown in Fig. 11 is due to its proclivity to overgeneralize the majority instance, specifically when there is a large class imbalance. Neighbors often surround minority data points from the majority class, and the chances of being classified as a majority are high. Fig. 11 Confusion matrix for KNN model

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5 Conclusion and Future Work This article aims to focus on Urdu sentiment analysis using supervised machine learning techniques. The data set from various blogs and e-Commerce websites, consisting of 4712 reviews. These reviews were manually labeled and annotated by native speakers for better accuracy. The data was preprocessed by the Urdu hack python library and divided into training and testing data sets. The supervised ML algorithms, i.e., Naïve Bayes, SVM, KNN, and Multinomial Logistics Regression, were trained and validated with different parameters. The best accuracy was obtained by KNN (k = 3) with 92.0% accuracy. In future work, we will work on sarcasm detection from Urdu data to enhance models’ accuracy. The deep learning techniques should also be considered for Urdu SA.

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14. B. Seref and E. Bostanci, “Sentiment analysis using naive bayes and complement naive bayes classifier algorithms on hadoop framework,” in 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2018, pp. 1–7. 15. S. Naz, A. Sharan, and N. Malik, “Sentiment classification on twitter data using support vector machine,” in 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 2018, pp. 676–679. 16. M. Rathi, A. Malik, D. Varshney, R. Sharma, and S. Mendiratta, “Sen- timent analysis of tweets using machine learning approach,” in 2018 Eleventh international conference on contemporary computing (IC3). IEEE, 2018, pp. 1–3. 17. K. Mehmood, D. Essam, K. Shafi, and M. K. Malik, “Sentiment analysis for a resource poor language—roman urdu,” ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), vol. 19, no. 1, pp. 1–15, 2019. 18. Z. Nasim and S. Ghani, “Sentiment analysis on urdu tweets using markov chains,” SN Computer Science, vol. 1, no. 5, pp. 1–13, 2020. 19. M. Z. Asghar, A. Sattar, A. Khan, A. Ali, F. Masud Kundi, and S. Ahmad, “Creating sentiment lexicon for sentiment analysis in urdu: The case of a resource-poor language,” Expert Systems, vol. 36, no. 3, p. e12397, 2019. 20. N. Mukhtar and M. A. Khan, “Effective lexicon-based approach for urdu sentiment analysis,” Artificial Intelligence Review, vol. 53, no. 4, pp. 2521–2548, 2020. 21. N. Mukhtar, M. A. Khan, N. Chiragh, and S. Nazir, “Identification and handling of intensifiers for enhancing accuracy of urdu sentiment analy- sis,” Expert Systems, vol. 35, no. 6, p. e12317, 2018. 22. K. Khan, W. Khan, A. Rehman, A. Khan, and A. Khan, “Urdu sentiment analysis,” (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 9, no. 9, 2018. 23. N. Mukhtar, M. A. Khan, and N. Chiragh, “Lexicon-based approach outperforms supervised machine learning approach for urdu sentiment analysis in multiple domains,” Telematics and Informatics, vol. 35, no. 8, pp. 2173–2183, 201 24. Yafooz, W. (2024). Enhancing Business Intelligence with Hybrid Transformers and Automated Annotation for Arabic Sentiment Analysis. International Journal of Advanced Computer Science & Applications, 15(8). 25. Alhejaili, R., Alhazmi, E. S., Alsaeedi, A., & Yafooz, W. M. (2021, September). Sentiment analysis of the COVID-19 vaccine for Arabic tweets using machine learning. In 2021 9th International conference on reliability, infocom technologies and optimization (Trends and Future Directions)(ICRITO) (pp. 1–5). IEEE.

Early Depression Detection from Social Media: State-of-the-Art Approaches Ahlam Alsaedi and Wael M. S. Yafooz

Abstract Depression is a serious mental health disorder that impacts a significant number of people around the world. A major impact of depression is the persistent sense of sadness, loss of interest, and a decreased motivation. A crucial aspect of effective treatment is detecting and addressing depression at an early stage. As individuals often express their thoughts and emotions online, social media platforms have become valuable resources for mental health research. Textual data from social media can be analyzed to detect early signs of depression, providing an opportunity for timely intervention and support. In text analysis, machine learning algorithms are commonly used to detect depression based on predefined linguistic and emotional features. For traditional techniques, extensive feature engineering is required, and nuanced, context-specific language patterns associated with mental health conditions may be missed. By automatically learning these features, deep learning approaches identify complex patterns. Transformer models are capable of capturing subtle, context-rich markers of depression better than both machine learning and deep learning methods. This study provides an overview of the state-ofthe-art machine learning, deep learning, and transformer-based models for detecting depression in text. Researchers and scholars conducting research on text mining and social media analysis will greatly benefit from this review. Keywords Depression detection · Sentiment analysis · Textual analysis · Machine learning · Deep learning · Transformers

A. Alsaedi (B) · W. M. S. Yafooz Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi Arabia e-mail: [email protected] W. M. S. Yafooz e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_4

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1 Introduction Depression is a mental health issue that negatively affects significant numbers of individuals around the world [1]. Symptoms include persistent sadness, loss of interest, and a lack of motivation. Depression can negatively impact a person’s relationships, work, and overall health. For effective intervention and support, it is crucial to understand and detect depression at an early stage. People who are at risk of or experiencing depression can be supported with resources and interventions to improve their mental health conditions. Over the last years, social media networks have grown to be popular resources for mental health research. Social media networks are widely used by individuals for expressing their opinions, feelings, and experiences. The wealth of textual data offers a unique opportunity for exploring depression’s early symptoms. A social media analysis can detect depression signs and identify individuals in need of early intervention by looking at language and patterns used in social media posts [2, 3]. Through the analysis of language patterns, researchers identify specific expressions of sadness, hopelessness, and emotional stress. A depressive phrase like “I’m so sad” or “There is no hope” may indicate underlying mental health issues [4] (see Fig. 1). Utilizing social media as a data source for depression detection can provide insight into individuals’ mental well-being and facilitate timely support. For the detection of depression from textual data, there have been numerous approaches, including machine learning [5, 6]. Machine learning algorithms are competent of processing vast amounts of textual data and identifying patterns indicative of depression. Nevertheless, traditional machine learning techniques are limited in their ability to accurately detect and classify depression. There are several limitations of this approach, including extensive feature engineering, reliance on handcrafted features, and difficulties handling language and context complexity. Therefore, more advanced and robust methods are needed to improve depression detection accuracy and effectiveness.

Fig. 1 An example of a linguistic feature in a text indicative of depression

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In recent years, deep learning has emerged as a powerful and promising approach for a wide range of Natural Language Processing (NLP) tasks [7, 8]. The power of deep learning models such as Recurrent Neural Network (RNN) and Transformer models evident when dealing with textual data [9, 10]. Since they are capable of learning hierarchical representations and understanding context, they are wellsuited to detect complex mental health conditions such as depression. This study provides an overview of machine and deep learning techniques as well as transformers used in depression detection, explaining how they identify linguistic patterns and depression signs from text data, allowing for more precise and effective mental health diagnosis and interventions [11, 12]. The paper is organized as follows; the second section reviews various datasets that were used to detect depression in texts. The third section examines machine learning techniques for detecting depression. The fourth section explores how deep learning methods can handle complex textual data. The fifth section examines the superior performance of transformer-based models in detecting depression. The conclusion summarizes key findings and observations, along with suggestions for future research.

2 Datasets As the research field of Arabic depression detection develops, several datasets are appearing. Modern Standard Arabic Mood Changing and Depression dataset contains a collection of Arabic tweets intended to capture mood changes and symptoms of depression [13]. Twitter Arabic Sentiment Analysis aims to detect depression from Arabic tweets using machine learning approaches, providing valuable data for understanding Arabic mental health sentiment [14]. Using transformer models, AraDepSu detects both depression and suicidal ideation in Arabic tweets [15]. A similar dataset, CairoDep, uses deep learning techniques for detecting depression in Arabic social media posts using BERT-based transformers [16]. In spite of these advances, there still aren’t sufficient large, well-balanced, and diverse Arabic datasets to fully exploit robust and generalizable depression detection models (Table 1).

3 Machine Learning Approaches Machine learning, which is part of Artificial Intelligence (AI), allows systems to become more intelligent based on experience instead of being programmed directly. Data are analyzed using algorithms that detect patterns and forecast results. To detect depression based on predefined features such as word frequency or emotional markers, traditional machine learning techniques such as Support Vector Machine (SVM), Naive Bayes (NB), and Random Forests (RF) are widely used [17, 18].

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Table 1 A statistical analysis of the datasets used in depression detection research References

Dataset name

Maghraby and Ali [13]

Language

Number of rows

Source

Classes

Modern standard Arabic arabic mood changing and depression

1,229

Twitter

Depressed and non-depressed

Musleh et al. [14]

Twitter arabic sentiment analysis

Arabic

4542

Twitter

Depressed, Non-depressed, and neutral

Hassib et al. [15]

AraDepSu

Arabic

20,213

Twitter

Depressed, Depressed with suicidal ideation, or non-depressed

El-Ramly et al. [16]

CairoDep v1.0

Arabic

7,000

Different sources

Depressed and normal

Nusrat et al. study uses tweets from Twitter to predict five types of depression: Bipolar, Major Depressive Disorder, Psychotic, Atypical, and Postpartum. Their machine learning techniques included SVMs, RFs, and NBs. A total of 14,317 tweets were analyzed, which were categorized into six classes, including depression and non-depression types. As a result, the RF algorithm was found to be more accurate than the others, with an accuracy rate of 94.7%. The SVM algorithm, on the other hand, had an accuracy rate of 94.4%. Despite showing promise, these traditional machine learning models failed to capture the full context of the tweets since they relied on feature extraction techniques such as TF-IDF [19]. Smys and Raj aim to develop a machine-learning algorithm for early depression prediction through a combination of SVM and NB algorithms. Various machine learning approaches are evaluated for early prediction using a standard dataset obtained from online social media. Results demonstrate that the hybrid approach is higher in accuracy and sensitive for early detection than existing methods [20]. A study authored by Govindasamy and Palanichamy uses machine learning algorithms to detect depression from Twitter data using the NB and NBTree classifiers. The sentiment of tweets is analyzed and classified as depressive or nondepressive. Similarly, both models achieved 97.31% accuracy for 3000 tweets and 92.34% for 1000 tweets. Despite the effectiveness of the models, additional user-specific features beyond text could improve them [21]. Sudha et al. use machine learning algorithms to detect depression in users by analyzing textual data. Using algorithms such as K-Nearest Neighbors (KNN), NB, Decision Trees (DT), and RF, the study classified individuals as depressed, mildly depressed, or not depressed. DT exhibited the best accuracy, achieving 98.24%, while RF achieved only 50%. For improved diagnostic accuracy, more diverse data types, such as speech or voice recognition, are needed to complement text data [22]. Sabaneh et al. used machine learning algorithms to predict early signs of depression from Arabic social media posts. To enhance feature extraction, they used the

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ChatGPT transformer to translate Arabic tweets into English, followed by QuickUMLS to extract depression signals. After that, they applied multiple machine learning classifiers, including RF, NB, and Logistic Regression (LR). RF achieved the highest accuracy with Bag of Words feature extraction at 80.24%. Although the model may be generalizable, its small dataset of only 1058 tweets limits its generalizability. In addition, relying on translation may introduce errors, impairing the quality of feature extraction and model performance [23]. Khan and Alqahtani used hybrid machine learning models to detect depression in Twitter users’ posts. There are four hybrid models proposed by the authors, each combining feature extraction and classification techniques. The models include Bidirectional Encoder Representations from Transformers (BERT) with an Artificial Neural Network (ANN), TF-IDF with LR, TF-IDF with SVM, and Spacy with SVM. Using a public sentiment dataset of Twitter posts, the models are trained and validated. Based on accuracy and F1-scores, the TF-IDF with LR model performed best among the models. The study demonstrates how unsupervised feature extraction and supervised classification methods are effective for depression detection. Nevertheless, the study only used Twitter data, which may limit its generalizability to other social media platforms or real-world applications [24]. In their study, Alghamdi et al. analyze Arabic text from an online psychological forum in order to predict depression symptoms using both lexicons and machine learning techniques. Researchers develop ArabDep lexicon and use machine learning algorithms, such as Stochastic Gradient Descent (SGD) and SVM, as well as n-grams and TF-IDF to extract features. In the lexicon-based approach, accuracy reached 80%, while in the machine learning approach it reached 73%, with the best results obtained when SGD was combined with TF-IDF. In spite of that, a key constraint of the study is the small dataset, which affects the performance and generalizability of the model [25]. Each study is described in Table 2, which includes the algorithms, modality, the dataset and its classes, and language used, as well as accuracy.

4 Deep Learning Approaches In Deep Learning, artificial neural networks are utilized to automatically learn and identify features from raw data. Using multiple layers of neurons, or “deep” networks, it can represent data hierarchically, making it useful for NLP and image recognition. RNN and Transformer-based architectures such as BERT have been effective in identifying complex patterns in textual data for depression detection, leading to better knowledge of mental health [25]. Using NLP techniques, Tejaswini et al. present a hybrid deep learning model for depression detection from social media text analysis. “Fasttext Convolution Neural Network with Long Short-Term Memory (FCL)” is a hybrid deep learning model that combines fasttext embedding, convolutional neural networks, and long shortterm memory architecture in order to extract text-based global and local features.

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Table 2 Details about the summarized papers (Machine Leaning Approaches) References

Classifiers

Dataset

Classes

Language

Accuracy

Nusrat et al. [19]

SVM, NB, RF

Twitter

Five depression types + Non-depression

English

94.7% (RF)

Smys and Raj [20]

SVM, NB, RF, DT

Twitter

N/A

English

92% (for SVM and NB hybrid model)

Govindasamy and Palanichamy [21]

NB, NBTree

Twitter

N/A

English

97.31%

Sudha et al. [22]

KNN, NB, RF, DT

Benchmark

N/A

English

98.24% (for DT)

Sabaneh et al. [23]

RF, NB, LR, SVM, Twitter SGD

Depression and Non-depression

Arabic

80.24% (RF)

Khan and Hybrid machine Alqahtani [24] learning models

Twitter

Depression and Non-depression

English

99.4%

Alghamdi et al. [25]

ArabDep corpus of 20,000 posts from ‘Nafsany’ forum

Labeled with Depression and Non-depression (2 classes)

Arabic

80.45% (Lexicon)

Machine-Learning approach algorithms versus Lexicon-based approach

It outperforms existing techniques and achieves high accuracy in detecting depression. Through a hybrid deep learning model and natural language processing, this paper presents an important and relevant topic of detecting depression through social media text analysis. Implementing fasttext embeddings and developing the FCL model demonstrate innovations in improving text representation and feature extraction. Despite this, the evaluation lacks comprehensive comparisons with existing approaches [27]. Jiayu Ye et al. propose another study that uses audio and text modalities to detect depression. It involves 160 Chinese participants and uses a psychological experiment known as the Segmental Emotional Speech Experiment (SESE) to rapidly alter subjects’ emotions. Researchers use deep learning techniques to detect depression by extracting low-level audio features. A multi-modal fusion model with SESE achieves high accuracy and F1 scores for recognizing depression, and the results show that SESE improves recognition accuracy. Study strengths include a comprehensive analysis of different modalities, the introduction of the SESE to induce rapid emotional changes and the use of deep learning for multi-modal fusion. Results demonstrate high accuracy and F1 score, demonstrating the effectiveness of the proposed method. However, the findings may not be generalizable to other populations or languages due to the focus on Chinese subjects. The proposed method would be more robust

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if it were replicated with diverse samples. The study lacks detailed descriptions of datasets and a comparison with existing depression detection techniques, which would help assess the novelty and superiority of the proposed model [28]. Depression can result in feelings of hopelessness and deep despair that can become significant triggers for suicidal thoughts and ultimately result in suicide. To detect depression, sentiment, and multi-label emotion in suicide notes, Ghosh et al. developed a multitask framework. They propose an approach that incorporates external knowledge with an extension of existing emotion-annotated suicide notes corpus. Results from this framework show that secondary tasks play an important role in improving emotion recognition and are superior to single-task models. By providing insight into the mental state of individuals and identifying at-risk subjects, the study contributes to suicide prevention efforts. A valuable contribution to the field is the extension of the emotion-annotated corpus and the proposed multitask framework. As the study only examines English suicide notes, it may disregard cultural and linguistic differences. The ethical and privacy implications of using suicide notes for analysis are not thoroughly discussed. Further, the proposed framework is not sufficiently explored in terms of practical implications and real-world applications. There is a need for further research in order to validate the framework on a broader scale and address these limitations [29]. Using deep learning techniques applied to textual data, Amanat et al. develop a model for early detection of depression. The authors propose combining Long ShortTerm Memory (LSTM) and RNN algorithms for predicting depression from text that achieves 99.0% accuracy and reduces false positive rates compared to frequencybased deep learning models. The study investigates the feasibility of using RNN and LSTM for the early detection of depression in social media users. This paper, however, does not provide any details on the dataset that was used for training and testing the proposed model, which may affect its generalizability [26]. Social media data is used in a study by Lin et al. to detect depression. The authors propose SenseMood, a deep visual-textual multimodal learning approach for detecting and analyzing depression. Using Convolutional Neural Network (CNN)based classifiers and BERT models, the system extracts deep features from both text and image tweets from users on Twitter. Using these features, the emotional expression of users is then reflected. As compared to current methods for detecting depression, their model performs far better on social networks. There are, however, limitations and challenges to relying solely on online data for mental health diagnosis. For accurate assessment and treatment, social media posts do not always represent an individual’s mental state accurately [30]. Using speech characteristics and linguistic contents from participant interviews, Shen et al. proposed a novel approach for automatic depression detection. A corpus of audio and text data on depression has been created by the authors, called Emotional Audio-Textual Depression Corpus (EATD-Corpus). Gate Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) models are combined with an attention layer in this technique. An integrated multi- modal fusion network is then used to detect depression based on these summarized features. Using two depression datasets, the proposed method achieves state-of-the-art performance [31].

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Based on analyzing users’ textual posts on social media, Shah et al. proposed a hybrid model for early depression detection. The research used BiLSTM with different word embedding techniques to train deep learning algorithms using the dataset from [32]. With the Word2VecEmbed + Meta set of features, the proposed model shows promising performance. However, increasing the model’s time performance will improve its reliability and usability [33]. In Qureshi et al. study, emotion intensity is simultaneously learned in order to enhance the estimation of depression levels. Text data is used to regress/classify both depression level and emotion intensity using attention-based multi-task architectures. Compared to emotion-unaware single-task and multi-task approaches, the authors demonstrated substantial improvements in performance using two benchmark datasets [34]. Deep learning techniques were used by Uddin et al. to detect depression symptoms in written texts. They identified self-perceived depression symptoms in Norwegian laguge using an LSTM- based RNN. In order to discriminate between texts describing depression symptoms and non-depression posts, they extract robust features from the texts based on predefined depression symptoms. Results showed that the proposed method outperformed traditional methods. The development of intelligent mental health care systems is a result of research in this area [35]. Khafaga et al. present a new technique to detect depression using Twitter data called Multi-Aspect Depression Detection with Hierarchical Attention Network (MDHAN). Data is preprocessed and deep learning techniques are applied. A combination of the Adaptive Particle and Grey Wolf optimization method is applied for features selection. MDHAN outperforms other deep learning models with an accuracy rate of 99.86%. As a result of the study’s reliance solely on Twitter data, it may not be applicable to diverse user groups or real-world scenarios [36]. A hybrid model using deep learning algorithms for detecting depression is proposed by Vandana et al. LSTM algorithms are used to train a textual CNN model, an audio CNN model, and a hybrid model that combines audio and textual features. According to the results, the audio CNN model achieves a higher accuracy of 98% than the textual CNN model with 92%. In terms of accuracy, the Bi-LSTM model is 88% accurate, while its validation accuracy is 78% accurate [37]. Each study is described in Table 3, which includes the algorithms, modality, the dataset and its classes, and language used, as well as accuracy.

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Table 3 Details about the summarized papers (Deep Leaning Approaches) References Models

Modality Dataset

Classes

Language

Accuracy (%)

Tejaswini et al. [27]

FCL model Text (CNN + LSTM + fasttext)

Twitter and Reddit

Depression and English non-depression

88

Ye et al. [28]

DeepSpect rum features and Word2Vec

Audio and text

Benchmark Dataset

N/A

91.2

Ghosh et al. [29]

Neural Network

Text

Extended CEASE

Depression, English Sentiment, and emotion labels

75.34

Amanat et al. [26]

LSTM + RNN

Text

Kaggle (tweets)

Depression and English non-depression

99.0

Lin et al. [30]

SenseMoo d model (CNN and BERT)

Visual and text

Dataset from [37]

N/A

English

88.393

Shen et al. [31]

GRU + BiLSTM

Audio and text

DAIC-WoZ N/A and EATD-Corpus

Chinese

N/A

Losada et al. [33]

BiLSTM

Text

Dataset from [31]

Depression and English non-depression

N/A

Shah et al. [34]

LSTM

Text

DAIC-WOZ N/A and CMU-MOSEI

English

Qureshi et al. [35]

RNN based Text on LSTM

ung.no

N/A

Norwegian 98

Khafaga et al. [36]

MDHAN model

Text

Kaggle (tweets)

N/A

English

99.86

Vandana et al. [37]

CNN + LSTM, Bi-LSTM

Text and audio

DAIC-WoZ

N/A

English

88 (for Bi-LSTM)

Chinese

63.64

5 Transformers Transformers have revolutionized deep learning, particularly for natural language processing. Transformers, such as BERT and GPT, analyze context more effectively than previous models by relying on a self-attention mechanism [39]. As they effectively capture context in text, transformers are powerful in detecting subtle patterns, suitable for tasks such as detecting depression. When compared to traditional methods, transformer models are more accurate and do not require manual feature extraction [40].

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In Nusrat et al.’s study, tweets from Twitter are utilized to predict depression types, and explainable AI is used to explain the model’s decisions. As part of the study, tweets were scraped based on lexicons, preprocessed, labeled, tokenized, and features were extracted and trained using BERT. Among the various techniques used for training the model for depression detection through tweets, the BERT model showed the best results in terms of accuracy 96% as compared to others [19]. Elmajali and Ahmad use pretrained transformer models, specifically AraBERT and MARBERT, to detect depression symptoms in Arabic tweets using the nine symptoms defined in DSM-5. With the help of data augmentation techniques, using ChatGPT, the authors were able to balance the dataset and increase the performance of the model. AraBERT achieved the best results with an accuracy of 99.3%, whereas MARBERT achieved an accuracy of 98.3%. However, the study was limited by a relatively small dataset, which may impact the model’s ability to generalize effectively and accurately [41]. With the help of transformer models, Hassib et al. predict depression and suicidal ideation in Arabic tweets. As part of their research, the researchers created the AraDepSu dataset, which is comprised of tweets categorized as “depressed”, “suicidal”, and “neutral” [15]. Researchers applied over 30 transformer-based models, and they found that MARBERT, a model fine-tuned to both Modern Standard Arabic and Dialectal Arabic, achieved an accuracy of 91.2%. The dataset is primarily based on specific Arabic dialects, which may not capture the full linguistic diversity of the Arabic language, which limits the generalizability of this study. A. Baghdadi et al. propose an optimized deep learning approach to detect suicidal ideation in Arabic tweets. Using BERT-based and Universal Sentence Encoder (USE) models, the authors developed a complete framework for classifying tweets as “normal” or “suicidal”. In order to improve model performance, they investigated different preprocessing techniques, such as stemming and lemmatization. On a dataset of Arabic tweets, the BERT-BV02T model achieved a 95.26% weighted sum metric (WSM). Despite this success, the study has a notable limitation that its focus is only on Twitter, so this model may not be generalizable to other social media platforms. Moreover, the dataset size was relatively small, and more diverse data from various Arabic dialects would further strengthen the model’s performance [42].

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Adebanji et al. compare traditional machine learning techniques with pre-trained transformer models in identifying depression in social media text. In order to train machine learning models, the authors use a variety of feature representations, including Bag of Words, Word2Vec, and GloVe. They also fine-tune pre-trained transformer models, including BERT, ELECTRA, and RoBERTa. Their experiments found that the transformer models exceeded traditional methods, with RoBERTa and ELECTRA achieving near-perfect accuracy of 99%. However, these models were trained on a relatively small set of social media posts from Reddit, which may limit their generalizability [43]. According to El-Ramly et al., the study aims to detect depression in Arabic social media posts by using advanced NLP models, specifically BERT. Two pretrained BERT models for Arabic—ARABERT and MARBERT—are fine-tuned to identify depressed posts in CairoDep, a dataset containing 7,000 Arabic posts [16]. ARABERT scored 96.93% on accuracy and 96.92% on F1, while MARBERT scored 96.07% on accuracy and 96.07% on F1. Results from this study significantly outperform machine-learning and traditional lexicon-based approaches. There are, however, some data sources that were translated into English, which may introduce translation errors or misinterpretation due to cultural differences in expression of depression, potentially affecting the model’s accuracy. Each study is described in Table 4, which includes the algorithms, modality, the dataset and its classes, and language used, as well as accuracy.

6 Conclusion In conclusion, this study shows the strengths and limitations of machine learning, deep learning, and transformer-based models in detecting depression from text. By automating the learning of complex patterns, deep learning models improve performance compared to traditional machine learning approaches. As a result of their ability to capture context and subtle linguistic nuances, transformer models demonstrate the highest accuracy. Transformer models should be optimized for detecting early signs of depression in text, taking into account dialectal variations and linguistic complexity. With these advancements, early intervention techniques and monitoring of mental health could become more effective. This review lays a solid foundation for future studies, presenting an overview of the state-of-the-art depression detection techniques.

Text

USE versus BERT

Machine-Learning algorithms versus Transformers

ARABERT and MARBERT

Baghdadi et al. [42]

Adebanji et al. [43]

El-Ramly et al. [16]

Text

Text

Text

Hassib et al. 30 + [15] Transformer-based models

Labeled with the 9 Depression Symptoms + ‘Normal’ (10 classes)

Five depression types + Non-depression (6 classes)

Classes

7,000 posts from different resources

7,731 Reddit posts from Kaggle

2,030 tweets

Labeled with Depression and Non-depression (2 classes)

Labeled with Depression and Non-depression (2 classes)

Labeled with Suicide and Normal (2 classes)

AraDepSu dataset of 20,213 tweets Labeled with Depressed, Suicidal, and Neutral (3 classes)

1,290 tweets “Modern Standard Arabic mood changing and depression dataset”

Text

Dataset

Elmajali and AraBERT versus Ahmad [41] MARBERT

Modality Twitter

Algorithm

Nusrat et al. CNN, LSTM, BERT Text [19]

References

Table 4 Details about the summarized papers (Transformers)

Arabic

English

Arabic

Arabic

Arabic

English

Language

96.93% (ARABERT) 96.07% (MARBERT)

99% (RoBERTa and ELECTRA)

96.06% (BERT-BV02T)

91.2% (MARBERT)

99.3% (AraBERT)

96% (BERT)

Accuracy

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Classification of Student Stress Levels Using a Hybrid Machine Learning Model Victor Doma, Ali Abd Almisreb, Emine Yaman, Salue Amanzholova, and Nurlaila Ismail

Abstract Stress is a state of worry and a natural response to a challenge, for students, it is often characterized by academic fatigue. It is a normal phenomenon everyone experiences, but it is excessive stress levels that lead to mental health problems, physical pain, bodily harm and even suicide. This study implements a highly efficient and accurate hybrid machine learning model to classify student stress levels, by taking into account the psychological, physiological, environmental, academic and social stress factors. Student stress level classification will enable early intervention, tailored support, evaluation of stress management programs, resource allocation and improvement of academic success. This also provides us with a deeper understanding of student stressors and how they are related. To build our hybrid model, we first implement five base models namely, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and Extreme Gradient Boosting (XGBoost). We then combine these models and create the ensemble learning model that achieves an accuracy of 92.41%. The dataset includes 20 variables that contribute to students’ stress levels. We obtain our data from a Kaggle dataset which used a questionnaire for data collection. We improve the dataset balance using Synthetic Minority Oversampling Technique (SMOTE). Random Forest feature selection algorithm was utilized in extracting the variables with the highest feature importance in the feature space. To enhance the performance of the models, we also implement hyperparameter tuning and each model was evaluated using several performance metrics of accuracy, precision, recall and f1 score. Keywords Student stress levels · Classification · Hybrid · Machine learning

V. Doma · A. Abd Almisreb (B) · E. Yaman International University of Sarajevo, Hrasnicka Cesta 15, 71210 Ilidza, Bosnia and Herzegovina e-mail: [email protected] S. Amanzholova International Information Technology University, Almaty 050000, Kazakhstan N. Ismail Universiti Teknologi MARA Shah Alam, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_5

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1 Introduction Student stress is a complex combination of psycho-physiological, socioenvironmental and academic pressures that should be prevented as best we can. Taking into account the many changes in the academic climate, student stress levels research has progressed immensely. While early study concentrated on academic pressures such as exams and assignments, more recent studies have broadened their scope to include other elements influencing student well-being. The authors in [1] examined the association between academic stressors and psychological factors. In recent years, more studies acknowledge the impact of mental health and lack of social support as student stressors. Publication [2] addresses how students manage stress through psychological and social structures. The authors in [3] investigate the effects of social media on student engagement and student stress. Disruptions in schooling, online learning and isolation caused by the COVID-19 pandemic, to a greater extent, contributed to heightened student stress levels, globally. Some of the research gaps in this field are, studies often tend to focus on one specific demographic e.g.; ethnicity [4]. They neglect to investigate how student stressors intersect to give more in-depth knowledge of the positive and negative correlations for a wider demographic populace, especially for marginalized student populations such as international [5] and disabled students [6, 7]. The majority of research on student stress is undertaken in Western settings, resulting in a limited understanding of stress experiences in non-Western countries [8–10]. Digital distractions, cyberbullying [11] and social media are prevalent stressors in students nowadays [12]. However, there has been little research into the effect of technology on student stress levels and coping techniques. While multiple studies have identified risk factors for student stress, there is a research void regarding effective therapeutic strategies. Student stress management methods [13] including mental health services at educational institutions are seldom evaluated in many studies. This calls for more research in aforementioned areas to foster inclusive research and enhance our understanding of student stress. The purpose of this research is to create a highly efficient hybrid machine learning model, from several base models, for student stress level classification, to enable early intervention, tailored support, evaluation of stress management programs, resource allocation and improving academic outcomes. Classification of student stress levels creates a good learning environment conducive for growth and development, enabling schools to better support students’ mental well-being as well as bolster academic achievements. Educators can identify highly stressed students early on and quickly intervene, offering mitigation strategies and support [14, 15]. Different stress levels require a personalized approach in treatment, wherein students receive tailored assistance based on their individual stress levels [16]. Knowing the distribution of stress levels, educational institutions can accurately allocate support staff and/or counseling services according to the demand [17]. The effectiveness of stress management programs [18] in schools can be better evaluated and modified from the data obtained in tracking stress levels over time. Overall, efficient student management programs

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will raise awareness about student stress [19] and help reduce stigma associated with mental health issues through open dialogue among students and support personnel. Contributions 1. In-depth analysis of student stressors. In this study we conduct thorough evaluation of the dataset and other research studies related to this topic. Through intensive exploratory data analysis, we identified the most relevant stress features, their correlation between themselves and the target outcome. 2. Highlighted the student stressors in underrepresented regions Having observed that student stress and student stressors are often defined mostly based on the Western standard, we also addressed the diverse student stress factors in underrepresented regions like Africa, Asia and South America. 3. Implemented an efficient hybrid ML model We successfully created a highly effective hybrid machine learning model from 5 base machine learning models with hyperparameter optimization. Our model achieved very high-performance metrics values, with an accuracy and f1-score of 92.41 and 92.43%, respectively. 4. Proposed student stress management methods We propose stress management activities and programs that can be done by educational institutions, educators and students at large. They not only cater to the typical student population but also serving underrepresented student groups like international students and students with disabilities. 5. Encouraging future research This research provides new insights in the field of student stress and how it has evolved over the years. Researchers will have a better understanding of factors of student stress and how they differ across the world. It will also offer a path for developing more advanced solutions and also encourage researchers to conduct more inclusive research, especially of underrepresented groups.

2 Literature Review In order to choose the best machine learning models, programming languages and methods for the research, we reviewed other various research articles including [20– 24], were many ML algorithms and performance metrics for a similar task were compared and evaluated. We also reviewed the other articles to investigate the best models to combine to create a highly efficient hybrid machine learning model. Two hyperparameter tuned hybrid machine learning models, one of tree-based models (RF, GB) and the other of two linear models (LR, SVM) using a soft voting approach were compared and used to perform stress prediction in [25]. The RF and GB hybrid model outperformed the other hybrid model with an impressive accuracy score of

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0.99. An ensemble learning approach for student stress level classification is proposed in [26], where the stress levels were divided into three distinct categories: highly stressed, manageable stress, and no stress. We identified questionnaires and student surveys as am effective way to obtain stress related data from students as shown in many other researches [27–29]. This is why we used a questionnaire-based dataset for our study. To create an academic stress classifying model using machine learning algorithms, the authors in [30] used data from students at many colleges of UT Chandigarh. The data was based on the Questionnaire of Academic Stress in Secondary Education [31] scaled by the Student Academic Stress Scale and consisted of affective, behavioral, cognitive, and physiological factors. To show that stress was activity-induced, researchers investigated university students’ stress levels during a Sudoku game, under various conditions [32]. LRCN (Long-term Recurrent Convolutional Network) [33] and self-supervised CNN models were used to model the data obtained from photoplethysmography, electrocardiograph and electroencephalogram signals to perform stress analysis. We observed the strong correlation between stress, anxiety and depression (SAD), as shown in many previous research studies [34, 35]. A 2020 research article aiming to predict anxiety, depression and stress using machine learning model is presented in [36]. The data was obtained from the Depression, Anxiety and Stress Scale questionnaire [37] and the Random Forest classifier had the highest total f1-score for all three classes. The COVID-19 pandemic [38] was one of the main factors that fostered more research into student stress, especially in recent years after the way it disrupted schooling all around the world. Similar studies to assess COVID-19 pandemic’s impact on college students’ stress levels using machine learning were also done in [39, 40].

3 Materials and Methods 3.1 Data Acquisition The data used in this research was obtained from the Student Stress Factors: A Comprehensive Analysis dataset [41] from the Kaggle repository. To create this dataset, the author, Chhabi Acharya visited various high schools and colleges in Dharan, Nepal spreading mental health awareness and likewise conducted a general survey of student stress related questions. The author created this dataset after having been inspired by two previous publications [42, 43]. The dataset in our study was based on a survey that took 5 months lasting from June to October 2022. The dataset consists of 1100 instances, one multiclass target outcome and 20 features, which are some of the main factors that influence student stress levels. These features are divided into five subgroups namely; psychological (e.g.; self-esteem), physiological (e.g.; sleep quality), social (e.g.; peer pressure), environmental (e.g.; living conditions),

Classification of Student Stress Levels Using a Hybrid Machine … Table 1 Dataset features

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Types of student stress factors Dataset features Psychological factors

• • • •

Anxiety level Self-esteem Mental health history Depression

Physiological factors

• • • •

Headache Blood pressure Sleep quality Breathing problem

Environmental factors

• • • •

Noise level Living conditions Safety Basic needs

Academic factors

• • • •

Academic performance Study load Teacher-student relationship Future career concerns

Social factors

• • • •

Social support Peer pressure Extracurricular activities Bullying

and academic (e.g.; study load), each subgroup containing four variables each as shown in Table 1. To determine the different ranges of the variables in the dataset the author used a combination of clinical rating scales like Generalized anxiety disorder (GAD), Rosenberg Self Esteem Scale (RSES), Patient Health Questionnaire (PHQ-9) subjective scales and the Likert scale. GAD-7 [44] is a scale used to screen for anxiety or gauge its severity. It consists of 7 questions which can be assigned scores of 0–3, representing the following categories: “not at all,” “several days,” “more than half the days,” and “nearly every day.” The overall sum score of GAD-7 ranges from 0 to 21, a total of the four categories. Rosenberg Self Esteem Scale [45] is a simple, succinct, and practical way of measuring one’s self-esteem levels. It consists of 10 statements (5 positive and 5 negative), each coupled with four possible answers (“strongly agree”, “agree”, “disagree”, “strongly disagree”), scored with values 0–3, with the overall sum score of RSES ranging from 0 to 30. PHQ-9 [46] is a valid and trustworthy tool for assessing depression severity. It consists of 9 statements, wherein each statement can be scored 0–3, giving PHQ-9 an overall score range of 0–27. A subjective rating scale is any rating that someone assigns based on their personal response or assessment, as well as their priorities, feelings, and/or desires. The 5-point Likert scale [47] is a rating system that evaluates beliefs, attitudes, and actions in a quantifiable manner (0–5), as shown in Table 2.

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Table 2 Scaling methods used for the dataset variables Dataset features

Scale

Anxiety

GAD-7 0–4: minimal anxiety, 5–9: mild anxiety, 10–14: moderate anxiety, 15–21: severe anxiety

Self-esteem

RSES Positively phrased statements are graded as: (opposite for reversed valence) Strongly agree =0, Agree =1, Disagree =2, Strongly disagree =3 0–10 =low, 11–20 =moderate, 21–30 =high

Mental health history

Without mental health history =0 With mental health history =1

Depression

PHQ-9 0–4 Minimal depression, 5–9 Mild depression, 10–14 Moderate depression, 15–19 Moderately severe depression, 20–27 Severe depression

Blood pressure Subjective Scale 1 =low, 2 =average, 3 =high Other features

Likert scale 0,1 =low, 2,3 to be mid, and 4,5 to be high (opposite for reversed valence)

3.2 Data Preprocessing Firstly, we load the dataset and import the csv file to a data frame format. We then implement data cleaning [48], including checking the data for any missing values using the isnull() function, which returns True or False. We also check for duplicate data, which are instances with the same values for all variables in the dataset. We use the duplicated() function to perform this task. Python code snippets of data cleaning are shown in Fig. 1. The dataset had no missing or duplicated instances. We check the balance of the dataset, and it was relatively balanced with the two majority classes having 373 instances and one minority class having 354 instances. We improve the dataset balance by oversampling the minority class to equal the majority classes using Synthetic Minority Over-sampling Technique [49]. SMOTE is a kind of data augmentation approach whereby existing minority-class samples are interpolated to create new synthetic samples. In this approach, artificial samples are drawn in the feature space following the lines that connect the nearest neighbors. We

Fig. 1 Code snippets of data cleaning

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Fig. 2 Representation of SMOTE

use the SMOTE() function from the imblearn library to perform this task. The function identifies the minority class, finds the nearest neighbors and generates synthetic samples. SMOTE is important because it increases the number of samples of data to 1119 creating a perfectly balanced dataset, enhances the model performance and prevents overfitting when the data is applied to a machine learning model. Another advantage of SMOTE is it can be set not to create duplicates; hence, fresh synthetic data is produced. Figure 2 shows a representation of SMOTE, displaying the synthetic instances.

3.3 Exploratory Data Analysis Here, we investigate and visually display the relationships between the variables and the target output. Using the info() function we analyze the shape of the dataset, the number of rows, number of columns, the features, and their data types. We then analyze each of the 5 categories, carefully evaluating all the factors in each category, to derive relationships between these variables and the target class. Figures 3, 4, 5, 6 and 7 show the ranked importance of all the five-student stress categorical factors, obtained from Random Forest classifier feature importance.

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Categorical feature importance Psychological stress factors Depression was the most prevalent psychological student stressor as it had the highest feature importance, as shown in Fig. 3. 35.30% of the participants had very high depression and 32.64% of the participants had severely high anxiety levels. This is most likely because of heavy workload, performance pressure, social exclusion, substance abuse, other underlying mental health issues and traumatic life events like parental divorce. Anxiety and depression are closely linked to stress and prolonged levels lead to poor mental health, academic performance and low self-efficacy beliefs. 24.13% of the participants had a below average self-esteem. This can be attributed to negative self-talk, academic struggles, lack of social skills, comparison to others, and body image concerns, especially through social media and parental and societal pressure on the students. 49.24% of the participants have a history with mental health issues, most probably through genetics, brain chemistry, childhood trauma, neglect and abuse, social media and substance abuse. Expectedly, students who had previous mental health history are likely prone to having higher stress levels and often use harmful coping mechanisms. Physiological stress factors Breathing problem is the most prevalent physiological student stressor, with the highest feature importance as shown in Fig. 4. Asthma, allergies, obesity, environmental factors such as exposure to dust, chemicals, and poor ventilation and emotional stress can lead to hyperventilation and other breathing problems. Chronic pain, insomnia and a weakened immune system are some common physiological

Fig. 3 The ranked feature importance of psychological stress factors

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Fig. 4 The ranked feature importance of physiological stress factors

symptoms of stress in students. 272/1119 participants experience frequent headaches. Stress, muscle tension, poor posture, eye strain, poor nutrition and environmental factors like loud noises, bright lights, strong smells are some popular causes of headaches in students. 45.13% of the participants suffered from high blood pressure. Here, factors like an unhealthy diet, obesity, lack of physical exercise, genetics and chronic stress are the main causes. 32.80% of the participants have poor sleep quality, which is likely due to excessive screen time, stress and anxiety, sleep disorders like sleep apnea, caffeine and stimulants, substance use like nicotine and a heavy study load. Environmental stress factors Basic needs is the most prevalent environmental student stressor with the highest feature importance as shown in Fig. 5. 19.30% of the participants have a severe lack of basic needs which includes anything from food, health care, clothing, social relationships to finances and community involvement. Factors like poverty, parental education level, corruption and economic inequality, cultural barriers and discrimination greatly contribute to a high inadequacy of basic needs. 18.68% of the surveyed population live under extremely bad living conditions. High cost of living often leads people to live in poor, overcrowded and dangerous neighborhoods with pollution and environmental hazards. Concerns about safety increase stress levels and create a negative learning environment. 19.66% of the participants feel very unsafe, with the probable causes being bullying, cyberbullying, gang activity, inefficient security personnel, unsafe neighborhoods, peer pressure and domestic violence. 24.66% of the participants experienced very high noise levels. This goes along with living conditions, were traffic and/or construction in one’s vicinity, lack of classroom management and larger class sizes would naturally cause a lot of noise.

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Fig. 5 The ranked feature importance of environmental stress factors

Academic stress factors Academic student stress factors often stem from the high expectations from family, friends and the competitive education system. This leads to toxic perfectionism, being overwhelmed and heightened stress levels. Study load is the most prevalent academic student stressor as shown in Fig. 6. 33.51% of the participants have very high concerns about their future career prospects, most probably caused by self-doubt and low selfesteem, fear of disappointing family members or peers, mental health issues and academic performance, lack of career guidance and job market uncertainty. 19.21% of participants have below average academic performance which may be caused by lack of motivation, poor study habits, learning disabilities like ADHD (Attentiondeficit/hyperactivity disorder) and dyslexia, ineffective teaching and family problems. 24.31% of the participants find the study load very heavy. The reasons for this could because of the course difficulty, teaching style, time management, perfectionism and extracurricular activities like part time jobs or compulsory sporting activities. 21.18% of the participants feel they have a bad teacher-student relationship, which may be because of lack of empathy from teachers, unclear instructions, unfairness, disruptive students and a previous bad reputation. Social stress factors Social support (friends and family) had the highest feature importance of the other three features in the same category hence it was the most prevalent social student stressor as shown in Fig. 7. 45.13% of the surveyed population have very low social support, most likely due to social anxiety, academic pressure, lack of inclusive activities and socioeconomic background. Feelings of isolation and pressure to fit into social norms causes fear of rejection, judgement and ultimately a lot of

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Fig. 6 The ranked feature importance of academic stress factors

stress in students. The influence of social media also significantly impacts students’ social interactions. 18.50% of the participants experience severe bullying, this can also include cyberbullying. Shy or quiet students are often perceived as weak and targeted by bullies. This may also be a result of underlying emotional issues like anger and jealousy, seeking social acceptance and ineffective anti-bullying policies in schools. 19.57% of the surveyed populace have very low participation in extracurricular activities. This is probably because the activities do not align with students’ hobbies, academic burnout, time constraints, social anxiety and student– teacher relationship. 32.08% of the participants experience very high levels of peer pressure, this is probably due to the desire for social acceptance, substance abuse, lack of confidence, social media influence and lack of parental supervision. Application of Maslow’s hierarchy of needs to students Maslow’s hierarchy of needs (MHN) [50] is a model of human needs arranged in a hierarchy of importance from bottom to top, consisting of physiological, safety and security, love and belonging, esteem and self-actualization needs. The hierarchy, as shown in Fig. 8 is not fixed as Maslow acknowledged that some needs might vary among different people. Even after so many years the MHN still remains valid till today and educational institutions can apply MHN to students in a number of ways. These include providing students with access to clean water and food, restrooms, breaks in between classes to satisfy their physiological needs. To ensure safety and security, schools should maintain an orderly classroom, set clear expectations and prevent and punish any bullying. In addition to this, a fence should be erected to set a clear boundary and safeguard from potential intruders. Educators should establish a sense of collaboration and community, encouraging teamwork through group projects to facilitate a sense of belonging. Acknowledging students’ academic success by showcasing their work

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Fig. 7 The ranked feature importance of social stress factors Fig. 8 Maslow’s hierarchy of needs

and establishing student leadership opportunities increases students’ esteem. Selfactualization in students can be bolstered by helping them explore creative interests and passion projects, encouraging challenges and setting goals. Analysis of student stressors in underrepresented world regions In this section we evaluate the student stress features from underrepresented world regions (Africa, Asia and South America), for an even deeper insight in this field. Africa A research study to investigate how external factors contributed to stress in graduate students at some Christian universities in Kenya was conducted in [51] and they concluded that lack of finances [52] was among the most prominent student stress

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factors. Likewise, the publication [53] addressed the great funding gap in Africa, Asia and South America. Having noted the massive failure and dropout rate among first year nursing students in South Africa, the authors in [54] conducted a study to determine the causing student stressors. Factors like family illness, poor study methods, insufficient money to pay for books and heavy study load were identified as the main student stressors. Research assessing the prevalence of stress in young and pregnant South African students was carried out, characterized by social stigma, academic pressure, financial challenges and relationship as the main contributing factors [55]. A study to investigate the anxiety levels related to mathematics studies among Zimbabwean students in high school was done in [56]. Among the most significant student stressors were parental and social pressure and teachers who fueled anxiety through their demeanor and an uncomfortable classroom setting. Research articles [57, 58] identify uncertain job prospects, overcrowded classrooms and history of mental illness, as dominant predictors of depression among students. Asia Although China produces high-achieving students, the nation also experiences toxic levels of student stress which are attributed to the Gaokao, the high stakes Chinese national college entrance exam. The authors call for educational reforms and targeted empirical research to reduce academic stress [59]. India’s education system is textbook-oriented that focuses on memorization of lessons and consists of long study hours hence reducing time to socialize and participate in recreation activities [60]. In Indian society good academic performance often determines students’ self-worth [61]. Corporal punishment is commonplace in Indian schools as a disciplinary measure. Although there was debate about the its positive and negative attributes. Research conducted in [62] showed that academic stress was an essential predictor of school adjustment among Korean adolescents, with academic stress being related to poor school adjustment. They propose school-based intervention to improve academic assistance. Because of the symptoms of anxiety many children and adolescents with anxiety often goes undiagnosed [63]. This study addressed the need for educational institutions to adopt methods that improve specific areas. South America An evaluation of the prevalence and anxiety-related factors among university students at Federal University of Ouro Preto, Brazil identified that students who experienced physical and/or psychological violence in childhood, or had a deceased parent had poorer academic and social performance [64]. A study done on first year students at Universidad de los Andes in Bogota, Colombia show that social capital is essential in preventing depressive symptoms and universities should invest in bringing students together [65]. A positive relationship between perfectionism and academic stress was identified among students from 55 Colombian institutions of higher education [66].

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3.4 Feature Selection Random Forest feature selection Random Forest is an embedded-based feature selection method [67, 68], meaning the feature selection process and model training are done in parallel. This approach combines characteristics of both wrapper and filter-based feature selection techniques [69, 70]. The importance score of a feature is computed by the ability of each tree in the random forest to raise the pureness of the leaves i.e.; GINI index impurity [71]. If the split segments of data contain only a single class, they are considered pure. This process is done for each tree then the mean purity increase is calculated to determine the importance score of a variable. Features at the top of the tree with higher information gain are regarded as more important than those at the end leaf nodes. The selectFromModel() object from sklearn [72] selects the features and the get_support() method then ranks the features. From the obtained feature importance, shown in Fig. 9, we observe a sharp decrease in the feature importance value, from the 15th to the 16th feature. Hence, 15 out of the 20 variables were selected and the rest were discarded. When RF is splitting a node, it looks for the best feature from a randomly selected subset of features rather than the most significant feature [73], hence producing a diverse set of selected features. Here, we can observe that the feature importance of some variables differs from the results obtained in categorical feature importance, this is because when the dataset contains two or more correlated features it assigns a high correlation on one and greatly reduces the importance of the other because the leaf purity gain from either of the features is almost equally the same. And since only 4 variables were compared for each category in Sect. 3.3.1, some variables had much higher feature importance than they did when ranked with 16 other features, because there are more correlations in the bigger set. An advantage of this feature selection method is that it is not affected by high variability because the mix of random predictor draws and bootstrap samples make the trees more independent.

3.5 Methods In this section we analyze the five base machine learning models we use to train the data for student stress level classification and create the hybrid model. Hyperparameter Optimization Hyperparameter optimization [74] is a technique to find the best combination of parameters which are tunable and affect model training values to achieve optimum model performance on the data. To tune the parameters, we implement those parameters with the greatest impact on the machine learning model. We set different values for the parameters and store them in a dictionary, creating an GridSearchCV object, assigning the parameters to it. Grid search is a parameter tuning technique for building

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Fig. 9 Random forest feature importance

and evaluating a model for varying model parameter combinations [75]. The training data is fit in the object and the best_estimator_ attribute outputs the best parameters. Now, the modified classifier with optimized hyperparameter is fit onto the training dataset resulting in improved model accuracy. Decision Tree A decision tree [76] is a supervised machine learning model with a tree-like structure where a feature is denoted by an internal/decision node, the decision rule as a branch and leaf nodes denoting the result. The root node is the uppermost node which represents the entire dataset. Decision trees implement recursive partitioning i.e., divide and conquer approach. This technique splits the data into smaller subsets of similar classes using the attribute values, choosing only the most predictive features, until a stopping criterion is reached. Gini index, Entropy and Information Gain are some of the approaches used in dividing the decision tree. This tree-like structure mimics human decision thinking, hence why decision trees are easily interpretable. Gini =1 −

n 

(p2i )

(1)

˙i=1

Entropy = −

n 

pi log2 (pi )

˙i=1

where pi is the probability of class i at a node.

(2)

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Information Gain =Entropy(parent) −

k  Ni i=1

N

Entropy(i)

(3)

where Ni is the number of samples in child node i, and N is the total number of samples in the parent node. Decision Rule: If feature ≤threshold then go to the left branch else go to the right branch. Support Vector Machine Support Vector Machine [77] is a popular supervised machine learning algorithm used in classification and regression tasks. SVM’s main goal is to define a boundary i.e., maximum margin hyperplane (MMH) that separates data points in multidimensional space into classes [78]. A margin is the distance between a data point and the hyperplane. The data points closest to the MMH are known as support vectors and each class must have at least one support vector. Support vectors offer a compact way to store a classification model, even with many features. Using the kernel trick [79], SVMs can efficiently perform a non-linear classification, implicitly mapping data inputs into high-dimensional feature spaces and drawing margins between the classes. To minimize the classification error the distance between the margin and the classes is maximized. Equation of the Hyperplane : w ·x +b =0

(4)

where w is the weight vector, x is the feature vector, and b is the bias term. Margins : |w ·x i +b ||w|

(5)

where xi is a data point. Logistic Regression Logistic Regression [80] is a supervised probabilistic based statistical model [72] that implements linear interpolation to perform classification tasks. For multiclass tasks each class a logistic regression is built for each class and predicting the class with the highest probability for that instance. LR identifies the boundary between classes and the distance from the boundary denote the class probabilities. The logistic model is based on the logical function, so for values between ±infinity, it always assumes only values between 0 and 1. Advantages of LR are it is easy to understand and implement. Softmax (Multinominal Logistic Regression) enables classification of multiple classes by computing the probability of each class using the softmax function and selecting the class with the highest probability. Equation for the logistic function:

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f (z) =

1 1 = −z −(b1.x1+b2.x2+...+bk.xk+a) 1 +e 1 +e

93

(6)

where b is the regression coefficient, x is the independent variable, f(z) is the dependent variable. Random Forest Random forest [81] is an ensemble supervised machine learning model, that combines predictions from many decision trees and computes the average to improve the accuracy score of the model. This enables RF to typically achieve higher prediction accuracy than a decision tree [82]. It implements bagging and feature randomness, creating an enhanced and diversified forest of decision trees, hence reducing the overfitting and increasing the model accuracy. In random feature selection RF calculates the importance of a feature (i.e., its ability to raise leaf pureness), by averaging among all the trees and normalizing to 1. Other benefits of the random forest algorithm are it can handle noisy and missing data, and selects the most important features in classifying the target outcome. The same equations of Gini index, Entropy and Information Gain are also used in Random Forest. XGBoost Extreme Gradient Boosting [83], is a scalable, supervised and ensemble machine learning model that enables parallel tree boosting. Ensemble models join many machine learning algorithms (weak learners) to enhance model performance. The same equations of Gini index, Entropy and Information Gain used in Decision Trees and Random Forest are also used in XGBoost. XGBoost trains several shallow decision trees, increasing the weights in each iteration to minimize the error residuals in the trees. To reduce the loss function XGB uses the gradient, just as neural networks employ gradient descent to enhance weights. The model then computes a weighted sum of all of the tree predictions. XGBoost minimizes underfitting and bias. Hybrid Machine Learning model We implemented a hybrid machine learning model by combining the predictions of five base ML models based on the performance metrics of the algorithms. Hybrid learning techniques are proven to have enhanced performance in machine learning tasks in solving regression and classification problems compared to a single model. To combine the results of the several base models, methods like voting and stacking can be implemented. In our hybrid model, each model is trained separately on the dataset and the results are combined using soft voting, where the final prediction is based on the majority vote of the individual models. The principle of hybrid machine learning algorithms is the combination of many base models to create a highly efficient model [84, 85]. Each weak learner makes its own predictions and the final outcome is computed by aggregating the outcomes from all the weak learners.

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4 Results 4.1 Feature Correlation With the use of a correlation heatmap in Fig. 10 we also investigate how factors from different categories are correlated to each other. For example, high noise levels can affect the quality of sleep and living conditions which lead to frequent headaches which leads to academic impairment. Here, we observe that factors like bullying and future career concerns are highly correlated with anxiety level, evidently because these are indeed very stressful concerns. Peer pressure is strongly related to bullying because in most cases, the consequence of trying to resist peer pressure is bullying. Self-esteem has a strong relationship with social support, this is because, often one’s confidence is based on how other people/society view them. Safety is correlated with teacher-student relationship and sleep quality, this adds up, because if a student feels comfortable with the teacher it is easier to ask questions and help, and in turn have more sound sleep. Basic needs which could be anything from finances to stationery are highly correlated to a student’s academic performance.

Fig. 10 The correlation heatmap of the features and the target outcome

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4.2 Target Outcome Correlation We identify and display the most correlated features to student stress levels with a threshold of 0.5 and higher, as shown in Fig. 11. Evidently the set of features includes psychological, physiological, environmental, academic and social stress factors. Bullying had a very close correlation to student stress level. This shows how social stress factors e.g., peer pressure influences psychological stress factors, not only in lower educational institutions but also in higher and tertiary educational institutions. Extracurricular activities have a 0.69 correlation with student stress levels, showing how social engagements or lack of, can extensively affect an individual. Another interesting finding here is how future career concerns is the most correlated academic feature to student stress levels, surpassing study load. This shows how the external pressures of life outside school has a very great impact on students. For example, a student might be the breadwinner in a family and he/she has to get a good job in order to take care of the whole family, which is adds an immense amount of pressure. Anxiety and depression both have a correlation value of 0.73 with stress level, showing how these stress features are closely related and influence each other. Headache is highly correlated to stress level. Most likely, this is also correlated to an environmental stress factor like noise level because often a physiological stress factor e.g.; breathing problems is a result of something else.

Fig. 11 Student stress level correlations

96 Table 3 Student stressors from other regions

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Region

Student stressors

Africa Asia South America

• • • • • • • • • • • •

Lack of finances Family illness Poor study methods Early/Teen pregnancy Parental and social pressure Teachers’ demeanor Uncertain job prospects Overcrowded classrooms Very high stakes exams Textbook-oriented Corporal punishment Physical and/or psychological abuse

4.3 Relation of Student Stressors in Other Regions to the Dataset Here, we highlight the common student stress factors in other regions of the world as shown in Table 3. Lack of finances is a very common student stress factor in many regions of the world, but mostly prominent in Asia, South America and Africa, and since the dataset was created in Nepal, it is highly likely that these features and those in the dataset are related. Due to the economic and political instability in these regions, there is high unemployment, general lack of funding which is often the root cause of many other stress factors. High stakes exams like the Gaokao, which has lifelong consequences on Chinese students, affecting their job prospects and social life, cause a lot of stress in students. Family illness is another major stress factor, because in these regions many students are the potential breadwinners which results in a lot of parental and social pressure. At school, some teachers tend to create a tense classroom setting by their demeanor and teaching methods. Crowded classrooms, textbook oriented education and poor study methods e.g., memorization, results in academic stress and failure. Corporal punishment and other forms of physical abuse at home or school adversely affect students in these regions and causes a lot of stress.

4.4 Classification Results To compare the five base machine learning models in this study: Decision Tree, Random Forest, SVM, Logistic Regression and XGBoost, we used the following performance metrics: accuracy, precision, recall/sensitivity and f1 score as shown in Table 4. Accuracy is a metric that measures the number of correct predictions over all predictions.

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Table 4 Performance metrics of all the machine learning models Model name Accuracy (%) Precision (%) Recall (%) F1-score (%) Base machine learning XGBoost base models RF

The hybrid ML model

91.96

91.98

91.96

91.94

91.52

91.74

91.52

91.53

SVM

91.52

92.16

91.52

91.55

LR

91.07

91.34

91.07

91.11

DT

90.62

91.03

90.62

90.64

92.41

92.65

92.41

92.43

Accuracy =

TP +TN TP +TN +FP +FN

(7)

Precision is a metric that measures the proportion of correctly predicted positively labels out of the total number of samples. Recall is a metric that measures correctly predicted the positives out of actual positives. F1-score is a metric that measures the predictive ability of a model based on the individual performance on each class. Precision =

(8)

TP TP +FN

(9)

2 ∗(Precision ∗Recall) Precision +Recall

(10)

Recall = F1score =

TP TP +FP

where TP, TN are true positive, true negative, respectively and FP, FN are false positive and false negative, respectively. The hybrid machine learning model, a combination of all the base models achieves superior results, with high accuracy, precision, recall and f1-score values of 92.41, 92.65, 92.41 and 92.43%, respectively. All the base models achieved very highperformance metric scores, with XGBoost outperforming all the other base models with an accuracy of 91.96%, 1.34% greater than the least performing model. This is because XGBoost is an ensemble model that adjusts weights and combines the predictions of numerous Decision Trees to create more accurate predictions. Just like the former, RF also joins many decision tree predictions and SVM finds the optimal boundary between the possible outputs for enhanced classification, hence both models have the same accuracy score of 91.52%, although RF is slightly better. LR achieves an accuracy of 91.07%, because it is a robust ML model and it is not affected by noise. DT which is more susceptible to overfitting had the lowest performance metrics. These experimental results demonstrate the hybrid model’s ability in efficiently classifying student stress levels, by leveraging state of the art machine learning methods.

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Student stress levels In this study we classified student stress into three levels (0, 1, 2), which can be categorized into low, middle and high stress levels. We then researched about the three types of stress, namely acute stress, episodic acute stress and chronic acute stress. The most common type of stress is acute stress which often quickly fades away and has little mental impact. It is caused by normal daily life stressors, like being late for class or getting a bad grade on an assignment. Episodic acute stress is a result of prolonged acute stress over a long period of time. It often leads to migraines and tension headaches. Chronic acute stress is caused by even longer stressful situations, such as a student failing in all their courses throughout the whole academic year. This often leads to much more serious consequences e.g.; high functioning depression, which is very hard to detect in most individuals.

4.5 Limitations of the Methodology Sociodemographic data is difficult to analyze and interpret. Socio-demographic characteristics are subjective and not objective because they are influenced by many factors, such as social, and economic factors that shape peoples’ experiences and opportunities. For example, what one student considers as heavy study load may not be the same for another student. Sociodemographic data includes sensitive information about individuals, such as race, ethnicity, and socioeconomic status. So, people are prone to lie and/or under or overestimate certain details they do not feel comfortable sharing. For example, when students answer questionnaires in groups, some downplay the state of their mental well-being for fear of being judged by their peers. All of the above make it difficult to collect accurate data and can interfere with the results.

4.6 Recommendations of Student Stress Management Methods In this section we recommend practical programs and exercises that can be used by students and schools to prevent or alleviate stress. Guided Imagery, a technique in which the individual visualizes scenes of great calm and relaxation to detach from the stressors, is one of the cost effective and convenient methods to manage stress [86]. Students are encouraged to take deep calming breaths to manage stress in tense scenarios like before or during an exam. Another effective way to deal with stress is Practice Progressive Muscle Relaxation (PMR) e.g.; Jacobson technique, a technique wherein the individual tenses and flexes their muscles until the body is completely relaxed [87].

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Schools can offer cross-cultural counseling services to students who need them and allocate adequate support workers. Thus, creating a safe space where students can seek guidance and emotional support without any judgement. This will also cater to international students who may be struggling to assimilate to the local culture, make friends and have challenges with language barriers. Educators should also advocate to remove stigma towards underrepresented groups like students with disabilities, students with albinism or young pregnant women in school. Organizing resilience building workshops where students are taught problem-solving skills and advised on healthy coping strategies, for stress and the dangers of harmful coping strategies like drug abuse. Academic support services, such as ‘study buddies’ can be set up by schools to provide extra help to students who may be falling behind in class. Students are encouraged to take 8 h of sleep per night and a few siestas during the day to manage stress and maintain a healthy sleep pattern. Regular exercise i.e.; yoga, walking, biking, the gym, a sport and even P. E. class is a recommended way to avoid or manage stress. Research also shows that students who are more physically active have lower stress than those who are not [88]. Schools can also invest in accessible wellness programs with inclusive options for students with disabilities. Students should eat a healthy diet to maintain high metabolism and regulate levels of hormones like adrenaline. What one eats determines how one’s body reacts to stress [89]. Schools can hold regular seminars on the important of sleep, regular exercise and healthy diets in managing student stress levels. Students should practice self-care like meditating, taking a bubble bath and practicing their leisurely hobbies to manage stress levels. Studies have even shown that upbeat music increases one’s processing speed and memory [90] and relaxing music helps people recover quicker from stress [91]. Students should expand their social network and interpersonal relationships to help manage stress. Going to social gatherings like board game night or participating in school events like hiking trips, are great ways students can get to connect with new people. Schools’ stress management programs should include artistic exhibitions and festivals of various art and music, both local and foreign. This creates great opportunities for students to network with their friends and new people. Accessibility assessments, language support services for international students and peer support services should also be implemented by schools to meet the needs of physically challenged students and assist them in building their social networks, respectively.

5 Discussion and Conclusion Comprehensive study is required for student stress level prediction and mitigation approaches equipping educational and health communities and the public on the causes, consequences, and solutions to this mental health issue. From this study, we identify the most relevant factors in determining student stress levels, not only from the dataset but also from underrepresented world regions identifying similarities and differences. We identified the correlations among the independent features and

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correlations between the features and the target outcome. This research can help in diagnosis, prevention, and fostering adapted treatment of specific levels of stress. The optimal amounts of factors like study load and basic needs can be determined for enhanced treatment and early intervention and not just identification. This research also provides an overview of student stressors from other regions of the world, providing an inclusive study on this topic. This study can aide in keeping track of student stress levels over time to improve stress management program and make any modifications if necessary. Furthermore, this research enables efficient resource allocation, which allows educators, schools and other state agencies to distribute support services accordingly. This study successfully implemented a highly accurate hybrid machine learning model from five base machine learning models to classify student stress into different levels, employing feature selection methods to narrow the feature space and enhance model performance. The hybrid learning model with Random Forest selected features out-performed the approaches used in other related studies. Using the principles of Maslow’s hierarchy of needs we address how Maslow’s hierarchy of needs can be implemented in educational institutions. Subsequently, this study aims to reduce the stigma surrounding mental health issues by improving stress management programs and opening dialogues on this topic. This research will help guide further studies focused on student stress classification, prevention and treatment. Acknowledgements I would like to thank my supervisor, Associate Professor Emine Yaman for guiding me throughout my thesis. We would like to show great appreciation for Associate Professor Ali Abd Almisreb, for all his guidance and support throughout the whole process of this research. To my family and friends, who have also supported me in my academic journey, I am very grateful.

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From Disease Detection to Health Campaigns: The Role of Social Media Analytics in Public Health Wael M. S. Yafooz, Yousef Al-Gumaei, Abdullah Alsaeedi, and Satria Mandala

Abstract Social Media Networks (SMNs) have become valuable tools for public health monitoring, health promotion, providing real-time data on health-related behaviors, opinions, and disease outbreaks. At the same time SMNs can leave a negative impact in the healthcare industry in ways such as misinformation. Especially when there exists no policy that controls the spread of misinformation, it can have harmful effects on both the individuals and communities. Additionally, it may increase the probability of adverse health outcomes and lead to poor health choices. An overview of the role of SMNs in public health is provided in this chapter. It emphasizes on the ways in which professionals and laypeople may utilize these platforms to monitor vaccination rates and public health initiatives, assess public sentiment, and create disease detection algorithms. Furthermore, SMNs can significantly contribute to the promotion of health by fostering professional teamwork and communication. Especially if they offer a forum for the exchange of knowledge, resources, and tactics to support public health campaigns. This chapter may be helpful to researchers and graduate students who are interested in public health initiatives and SMN content analysis. It describes how to use SMNs to enhance health promotion tactics and evaluate public health trends more accurately. Keywords Social media networks · Health misinformation · Health promotion · Public health

W. M. S. Yafooz (B) · A. Alsaeedi Department of Computer Science, College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia e-mail: [email protected] Y. Al-Gumaei Global College, Heriot-Watt University, Edinburgh, Scotland, UK S. Mandala School of Computing, Human Centric (HUMIC) Engineering, Telkom University, Bandung, Indonesia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_6

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1 Introduction Social media (SM) has revolutionized communication and information exchange, impacting almost every facet of daily life, including public health [1, 2]. On Social Media Networks (SMNs) like Facebook, Instagram, YouTube, and Twitter, where millions of people use their accounts daily, massive volumes of user-generated data are created quickly [3]. This data provides a unique opportunity for monitoring and resolving public health issues since it includes posts, comments, and discussions regarding health-related topics. The discipline of collecting and analyzing data from SMNs, known as “SManalytics,” has emerged as a powerful tool for tracking disease outbreaks, monitoring public opinion, and assessing the effectiveness of health campaigns [4, 5]. Real-time information regarding public health trends is one of the main benefits of social media analytics. Unlike traditional health surveillance methods, which often rely on incomplete or delayed data from clinical sources, SMNs offer a wealth of information that can be used to track the spread of false information, understand public attitudes towards health interventions, and identify early indicators of disease outbreaks. During the COVID-19 pandemic, for example, SMNs were critical in providing early information regarding symptoms, public reactions to government activities, and vaccine hesitancy. These insights enabled health groups to adapt their strategy and improve public communication [6]. The application of SM analytics in public health is not without difficulties, despite its advantages [7–9]. There are a lot of obstacles because of ethical worries about data privacy, the dissemination of false information, and the representativeness of SM data. Furthermore, to extract useful insights from the vast amount of unstructured SM data, advanced analytical approaches like sentiment analysis, machine learning and natural language processing (NLP) are needed. This chapter investigates the present role of social media analytics in public health. Several initiatives have been taken to exploit SMNs for public health reasons. As a result, this chapter investigates the applications and strategies that incorporate and benefit from SMNs in this domain. As seen in Fig. 1, these fall into three categories: community health promotion, public health disinformation, and health data collection and research. User-generated data can be used by researchers to better understand public opinions, attitudes, and behaviours regarding health issues while collecting and analysing health data. Platforms provide direct communication with users and the efficient distribution of surveys and polls. Furthermore, the sentiment analysis of posts helps gauge public opinion on health issues, whilst monitoring public health initiatives assesses their success [10–12]. In public health, disinformation is described as false or misleading information about health issues that spreads quickly, particularly via social media. Misinformation can come from a range of sources, including individuals, organisations, and even news channels, and it commonly causes the public to feel hesitant, fearful, and engage in harmful health practices [13–17]. Particularly during times of crisis, like pandemics, when accurate information is crucial, the ease with which false information travels may endanger public health efforts. It is vital to eradicate false information about

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Fig. 1 General view of the role of social media in health

health by constant public education, efficient communication techniques, and the promotion of reliable sources in order to empower people to make educated decisions about their health. The goal of community health promotion is to enhance the health and well-being of both individuals and communities via various programs and activities. It concentrates on empowering people to take control of their own health through environments that are supportive, educative, and awareness-raising [2, 18–21]. Activities aimed at promoting health include community efforts, public health campaigns, and legislation that supports healthy behaviour. The goal of health promotion is to reduce health disparities and foster a culture of wellbeing, both of which will improve overall health and quality of life. This chapter’s remaining sections are organized as follows: Sect. 2 looks at the ways in which social media helps academics identify and gather information regarding mental health issues. Section 3 looks at the function of social media networks in health promotion, whereas Sect. 4 covers research on health misinformation. Finally, the concluding remarks are presented in the last section of the chapter.

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2 Health Data Collection and Research Researchers may benefit greatly from SMNs as a subject of study and as a platform for gathering data. Public health is one of the most important topics in which this data is frequently utilised to address current events and pressing concerns. The effect of social media on public health has been the subject of several review studies [15, 22–27]. Therefore, the rise of social media platforms has created an unparalleled opportunity for the detection of mental illness: individuals’ posts include important information about their mental health or the sharing of information that can impact people and about a certain mental disease. Researchers used cutting-edge deep learning and machine learning techniques to construct models that can evaluate social media content which properly identifies and categorizes mental health conditions. There exists a range of scholarly approaches which have been used to identify mental health problems such as depression [11, 12, 28–35], anxiety [11, 12, 32, 33], schizophrenia [35–38], and others. These approaches were conducted on platforms such as Reddit, Facebook, Instagram and Twitter. Furthermore, by employing Machine Learning (ML) and Deep Learning (DL) models it can help public health efforts on SM by analyzing large data sets, detecting health patterns, and tracking disease spread. Kim et al. [11] investigated the detection of mental illnesses, with a focus on psychological disorders, using a dataset of 633,385 Reddit posts, specifically from r/depression, r/anxiety, r/bipolar, r/BPD, r/schizophrenia, and r/autism. The suggested program examines user posts using deep learning techniques, notably natural language processing, to detect potential mental health issues. The accuracy achieved over repeated tests is better than 90%. Similarly, Murarka et al. [12] explored the identification and classification of five mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD, using a dataset of 17,159 user-generated Reddit posts. The researchers analysed this data using a RoBERTa-based model, the precise accuracy results across all tests ranged from 74 to 89%. In the same way, Ghosh et al. [28]. discussed depression intensity estimation using data collected from Twitter. The dataset used contains 4,245,747 tweets from 6,562 users, with 1,402 labeled as depressed. The data was collected through web-crawling of Twitter user profiles. The authors propose a model based on a long LSTM network using Swish as the activation function, and a rich set of features (emotional, topical, behavioral, user-level, and depression-related). The model achieved the lowest MSE of 1.42 and an accuracy of 87.14%, outperforming baseline models for depression intensity estimation. Sekulic and Strube [29], also focusing on depression intensity, demonstrated how to assess depression intensity using Twitter data. The dataset comprised 4,245,747 tweets from 6,562 people, 1,402 of whom were identified as depressed. The information was gathered through web crawling of Twitter user accounts. The authors suggest a model with a long LSTM network, Swish as the activation function, and a diverse set of features (emotional, topical, behavioural, user-level, and depression-related). The model has the lowest MSE of 1.42 and an accuracy of 87.14%, exceeding baseline techniques for assessing depression severity. Adding on to this, Wu et al. [30] focused

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on a deep learning-based approach to depression detection called Deep Learningbased Depression Detection with Heterogeneous Data Sources (D3-HDS). Apart from publicly available living environment data from Taiwan and 873,524 Facebook posts, it utilises a dataset comprising 1,453 Facebook users who completed the CES-D depression screening exam. The model uses LSTM, a type of Recurrent Neural Network (RNN), to generate post representations. These are coupled with behavioural traits and living circumstances to predict depression. The proposed model achieves a remarkable 83.3% precision and 76.9% F1 score. Similarly, Orabi et al. [31] talked about using Twitter data to create deep learning models that can identify sadness. It uses a dataset from the collaborative endeavour CLPsych 2015, comprising 1.25 million tweets from 1,145 individuals categorised into three groups: PTSD, depression, and control. The material originated on Twitter. The CNN and RNN architectures are contrasted in the study. With an accuracy of 87.96%, a CNN with max-pooling was the best-performing model and an AUC of 0.951. In the same way, Murarka et al. [32] concentrated on using Reddit and RoBERTa data to classify mental illnesses. 17,159 posts from 13 subreddits covering five mental illnesses; PTSD, ADHD, bipolar disorder, depression, and anxiety, are included in the dataset. The recommended model, a RoBERTa-based classifier, performs better with an accuracy of 89%. Also focusing on Reddit data, Ameer et al. [33] focused on the classification of mental illnesses using social media posts from Reddit. It analyses 16,930 posts to identify five common mental health conditions: ADHD, anxiety, bipolar disorder, depression, and PTSD. The data was gathered via Reddit. The study incorporates a number of models, including traditional machine learning, deep learning, and transfer learning techniques. The highest-performing model was RoBERTa, a transfer learning model that attained 83% accuracy. Garg [34], adding to the broader analysis, employed social media posts to assess mental health, specifically stress, despair, and suicide detection. It evaluates more than 92 research publications and classifies developments in deep learning (DL) and machine learning (ML) for mental health detection. The study includes 16,613 annotated samples from social media platforms including Reddit and Twitter. Many models are investigated, with a focus on NLP approaches and a proposed taxonomy for mental health treatment. Similarly, Rao et al. [35] suggested two hierarchical neural network models, MGL-CNN and SGL-CNN, for diagnosing depression in individuals from online forum discussions. The models collect user sentiment at both the post and user levels, identifying key elements with convolutional layers and gated units. The study uses the Reddit Self-reported Depression Diagnosis (RSDD) dataset, which has over 9,000 diagnosed users and 107,000 control users, as well as the eRisk 2017 dataset, which has 486 participants. Bae et al. [36], in the same way, presented how to detect schizophrenia, a serious mental illness, using machine learning to analyse social media posts. The dataset contains 60,009 posts from the schizophrenia subreddit and 425,341 control posts from six non-mental health subreddits, which were all collected from Reddit via the Pushshift API. The authors employed a random forest model, which obtained 96% accuracy in distinguishing between schizophrenia-related and control posts. Similarly, McManus et al. [37] studied schizophrenia using a dataset that included 96 Twitter users diagnosed with

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the condition and 200 age-matched controls. The dataset comes from Twitter, and the proposed model achieved an impressive 89.3% accuracy by combining sentiment analysis techniques and machine learning classifiers. Finally, Mitchell et al. [38], also focusing on schizophrenia, analysed tweets from 174 self-reported users, removing retweets and non-original language, in order to investigate linguistic variations in schizophrenia. Utilising latent Dirichlet allocation and LIWC, among other natural language processing techniques, the researchers were able to identify linguistic indicators with an accuracy of 82.3% on the balanced test set. Table 1 shows the research works on Social Media and Diseases. Table 1 State of the art research on social media and diseases References

Diseases

Kim et al. [11]

Murarka et al. [12]

Platform

Model

Accuracy

Depression 633,385 posts Anxiety Bipolar Disorder Borderline Personality Disorder (BPD), Schizophrenia, Autism

Reddit

Deep learning with NLP

Above 90%

Depression, 17,159 posts anxiety, bipolar, ADHD, PTSD

Reddit

RoBERTa

89%

Twitter

Long LSTM 87.14% network with Swish

Basch et al. Depression [28] Muhammed and Mathew [29]

Dataset

4,245,747 tweets

Depression, 335,952 Reddit posts Reddit ADHD, anxiety, bipolar, PTSD

Hierarchical Attention Network (HAN)

F1 score of 68.28%

Ghosh and Depression Anwar [30]

873,524 Facebook posts

Facebook LSTM (Deep Learning-based Depression Detection)

Precision 83.3%

Sekulićand Strube [31]

Depression

1.25 million tweets

Twitter

CNN with max-pooling

87.96%

Wu et al. [32]

Depression, 17,159 posts anxiety, bipolar, ADHD, PTSD

Reddit

RoBERTa

89%

(continued)

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Table 1 (continued) References

Diseases

Dataset

Platform

Model

Accuracy

Orabi et al. [33]

ADHD, anxiety, bipolar, depression, PTSD

16,930 posts

Reddit

RoBERTa

83%

Murarka et al. [34]

Stress, depression, suicide detection

16,613 annotated instances

Reddit, Twitter

Various ML and DL N/A models

Ameer et al. [35]

Depression

RSDD (9,000 users), Reddit, eRisk (486 users) eRisk

MGL-CNN, SGL-CNN

Garg [36]

Schizophrenia

60,009 Reddit schizophrenia-related posts and 425,341 control posts

RF

96%

Rao et al. [37]

Schizophrenia

96 Twitter users diagnosed with schizophrenia and 200 age-matched controls

Twitter

Sentiment analysis techniques combined with machine learning classifiers

89.3%

Bae et al. [38]

Schizophrenia

174 self-reported users’ tweets

Twitter

NLP (LIWC, latent 82.3% Dirichlet allocation)

3 Public Health Misinformation As social media has become a primary source of health information, the spread of misinformation poses significant risks to public health. Researchers are increasingly focusing on understanding how healthcare misinformation spreads and how effective corrections can be implemented. In addition, the World Health Organization introduced the term “infodemic” to show how too much information, especially on social media, can lead to the spread of misinformation [39]. Social media can quickly amplify misinformation. Therefore, as these reviews [15, 40–45] illustrate a great deal of research has been examined on this topic using a variety of methodologies to show the detrimental effects that disinformation has on both people and society. By using ML/DL models to identify and counteract health disinformation on social media sites like Facebook and Twitter, we can show the advantages and disadvantages of different approaches. Bautista et al. [46] discussed how healthcare professional’s correct health misinformation on social media. The dataset includes detailed interviews with 30 healthcare workers (15 nurses and 15 doctors). Between January and March 2020, data was collected using mobile and app-based interviews (Zoom/Skype). The suggested conceptual model depicts a two-phase process: authentication (verification of social media content) and rectification (dissemination of accurate information). Similarly,

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Muhammed and Mathew [15] emphasised healthcare disinformation, notably on social media sites. It makes use of a dataset compiled from Twitter, Facebook, and other platforms, with an emphasis on misinformation transmitted during health crises such as COVID-19. The collection includes many sorts of disinformation communicated on these platforms, with a focus on healthcare topics including vaccines and infectious diseases. In the same way, Walter et al. [16] performed a meta-analysis of 24 research involving 6,086 users from social media platforms like Facebook and Twitter to investigate how health misinformation is removed from these sites. The researchers discovered that treatments are typically advantageous based on a mean effect size of d = 0.40. Also addressing health misinformation, Chen et al. [47] researched fake health information on social media using datasets from Twitter and HealthNewsReview.org, which included FakeHealth (2,296 cases). It achieves 98% detection accuracy using models such as CNN, LSTM, and BERT. Biomedical knowledge and techniques, such as graph neural networks, are applied. Adding to the topic of misinformation, Ghenai and Mejova [48] focus on social media disinformation concerning cancer, specifically disproven cures. It takes use of a dataset retrieved via the Twitter API, which includes 215,109 tweets from 39,675 Twitter users. The proposed approach, a logistic regression classifier, has an accuracy of more than 90% in identifying persons who distribute misleading information. Similarly, Bode and Vraga [49] discussed misinformation about the Zika virus, specifically the false claim that GM mosquitoes caused the outbreak. It used a dataset of 613 participants from a university’s simulated Facebook feed. The study tested algorithmic and social corrections, both of which were equally effective, with an accuracy rate of reducing misinformation beliefs by approximately 90% compared to the control group. Also focusing on social media misinformation, Jabbour et al. [50] examined the impact of social media misinformation on mental health and COVID-19 vaccination decisions among 440 Lebanese university students. Data showed that Facebook use led to worse vaccination attitudes, while Twitter use improved them. Social media exposure was linked to mental health. Varzgani et al. [51], adding another dimension, addressed health misinformation related to COVID-19, utilizing a dataset of 112 million social media posts gathered from various platforms. The authors propose a model that examines the effectiveness of warning labels based on three characteristics: background color, message abstractness, and assertiveness. Similarly, Vraga and Bode [52] investigated misinformation about the Zika virus using a dataset of 1,000 participants from Twitter. It employs the process macro model to analyze the effectiveness of corrections from credible sources like the CDC. The findings indicate an average 20% point reduction in misperceptions after corrections. Table 2 presents the related studies that focus on the misinformation on public health.

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Table 2 Studies on public health misinformation Study

Research focus/ Platform

Methodology

Accuracy (%)

Jabbour et al. [53]

Susceptibility to health misinformation

Fuzzy-set qualitative comparative analysis (fsQCA)

N/A

Varzgani et al. [54]

Reducing vaccine hesitancy

Field studies, RCT

N/A

Vraga and Bode [55]

Combating health misinformation

ML classifiers

92%

Mo et al. [56]

Vaccine misinformation Qualitative interviews N/A

Ruggeri et al. [57]

Twitter, Facebook

COVID-19 vaccine misinformation

Content analysis

Joshi et al. [58]

General social media platforms

General health-related misinformation

Literature review and theoretical analysis

Lockyer et al. [59]

Twitter, Facebook

COVID-19-related misinformation

Content analysis and surveys

Sule et al. [60]

Mental health promotion

Campaign evaluation

N/A

Polyzou et al. [61]

Heat speech

COVID-19-related misinformation

90.8%

Caceres et al. [62]

Misinformation, practitioner impact

Scoping review

N/A

4 Community Health Promotion The use of social media in public health communication is increasing, as is the relevance of its ability to refute myths, stimulate behaviour change, enhance health education and health promotion. Researchers have investigated several areas of SM use for health promotion, including behaviour change, public health protection, and mental health awareness as presented in Fig. 2. Activities that encourage healthy habits are the main emphasis of health behaviour change, especially with regard to younger populations. This includes encouraging physical exercise, eating a balanced diet, and assisting in the quitting smoking [63, 64]. While the goal of public health messaging is to efficiently inform the general population about health facts [65], it also includes crucial communications about COVID-19 standards, vaccination programs, and general health behaviour communication to promote informed decision-making. They use awareness campaigns to highlight actions targeted at increasing knowledge about certain health issues [66, 67]. This includes initiatives to raise mental health awareness, HIV prevention programs, and maternal health education, with a focus on rural people.

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Fig. 2 Community health promotion areas

While Gupta et al. [68] and Stellefson et al. [69] focused on the broader role of social media in health promotion and education via platforms like Facebook and YouTube, Rolim et al. [19] and Ghahramani et al. [20] discussed using social media to encourage hypertension self-care among vulnerable populations. In a similar vein, O’Reilly et al. [70] and Latha et al. [71] investigate mental health promotion and how social media might benefit in mental health awareness campaigns such as smoking cessation and suicide prevention. Furthermore, Al-Dmour et al. [23] looked at how social media may have improved public health protection during the COVID19 pandemic, demonstrating how platforms can affect public health behaviour and understanding. At the same time, Mheidly et al. [72] and Schillinger et al. [73] highlighted the necessity of dealing with misinformation, as well as tactics and frameworks for limiting the dissemination of information and encouraging the promotion of health messaging based on evidence-based research. The findings show that, while social media may be a useful tool for public health campaigns, its efficiency is dependent on communication and targeted techniques to reach certain populations. Rolim et al. [19] investigated the potential of social media to encourage self-care for systemic arterial hypertension (SAH) in marginalised populations. The Escola de Pacientes DF (EP-DF) provided 33 testimonial recordings for the dataset. The study used a Participatory Community Based Research (CBPR) approach to evaluate how

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well social media interventions promoted self-care habits. In a similar line, O’Reilly et al. [70] investigated how social media may benefit teenagers’ mental health. The study shows how social media may be used to increase mental health awareness by analysing focus group data from 57 participants to identify important themes around mental health and wellbeing. To elaborate, Gupta et al. [68] looked at social media marketing’s potential in the healthcare sector, specifically in terms of advancing interventions and health education. The paper includes case studies from websites such as Facebook, YouTube, and Second Life to demonstrate how companies such as the CDC have utilised social media to spread material related to health. Stellefson et al. [69], emphasised how the roles of health experts are changing as they use social media to spread health awareness. The study emphasises how social media may facilitate better public health messaging and lower obstacles to receiving treatment. Latha et al. [71], studied how effective social media campaigns are in raising mental health awareness. The study posts from social medias like Facebook and Instagram, and it demonstrates how social media can be used to take the attention of audiences and can be used to promote mental health education through initiatives such as smoking cessation and suicide prevention. Furthermore, during the COVID-19 pandemic, Al-Dmour et al. [23] looked at the impact of social media on public health protection. According to their study of 2,555 Jordanians, social media dramatically raised knowledge of public health issues and resulted in favourable behavioural changes that improved health care. Furthermore, Ghahramani et al. [20] conducted a review of 28 studies on how well social media influences behavior change. The review mainly uses posts from social media platforms like Facebook and YouTube to measure how much social media influences behavior. In keeping with this conversation, Mheidly et al. [72] conducted a study in which the authors examined challenges of the COVID-19 infodemic. in the study the authors gave health workers with a 12-point checklist for avoiding misleading information and disseminating accurate health information. Finally, Schillinger et al. [73] introduced the SPHERE concept, which is a tool for health promotion and defines social media’s role in public health as also a source of misinformation. These studies all highlight the growing importance of social media in promoting health, educating the public, and combating misinformation, while also highlighting the need for strategic interventions to maximize its positive impact.

5 Conclusion Public health is important for individuals, communities, and society as a whole. Social media networks (SMNs) provide open areas for people to share health-related information, therefore it is important for receiving information about health. This study examines the importance of social media networks (SMNs) for people, scholars, and professionals. While these platforms provide several advantages, they also bring issues. These include the rapid spread of disinformation, privacy concerns associated with data sharing, stigmatisation, cyberbullying, and insufficient rules to assure the

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quality of online health material. Misinformation may spread quickly, making it difficult for consumers to determine what is accurate. Furthermore, privacy issues are a dispute. Many consumers do not completely understand how their data is utilised. Concerns about privacy emerge when personal health information is posted online. Additionally, there’s worry about the quality of health advice, the influence of paid promotions, and the lack of proper rules to control health-related content online.

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Effect of Social Media Networks on People’ Bahaviour in the Light of the Fourth Technological Revolution—A Perspective from Their Families Chien-Van Nguyen

Abstract The technological revolution has created many positive changes in human life while bringing new experiences on social media to connect people more easily and conveniently. Researching the impact of social media networks on people’s behaviour through a survey of a number of families in Vietnam, the research results show that there is a positive impact of social media networks on kids while there is also a positive impact of family and school on people’s behaviour. However, there is no evidence to confirm the relationship between the content and frequency of social media use on people’s behaviour. Keywords Technological revolution · Media · Kids · Parents

1 Introduction The world is witnessing the rapid development of technological revolution that has fundamentally changed social life. Technology has changed human habits and behaviors through direct communication transformed into social networking platforms. With the context of the Internet, communication relationships between people are carried out on social networking platforms, thereby reducing the cost of connecting people, economic activities and increasing interactions, conveying social messages to the community. It can be said that the benefits of the fourth technological revolution are undeniable, and at the same time, technology is the fundamental foundation promoting the rapid development of social networks in the current period. Social networks are considered connect people with others through activities such as sharing images, information, sounds, finding friends. The development of society C.-V. Nguyen (B) School of Economics and Finance, Thu Dau Mot University, Thu Dau Mot City, Binh Duong, Vietnam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_7

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entails the development of mobile technology through phones, tablets, computers and social networks where people can post and connect with others through accounts created with email or phone numbers. The most popular social networks today are Facebook, Instagram, Zalo, Youtube or some other social networks that are being developed all over the world. The development of social networks has brought lots of opportunities for people and all ages from students, adults, people working in the administrative sector, businesses, and especially children. The content on social networks is increasingly diverse, increasing the ability to attract the attention of the social community, especially kids. The content on social networking platforms is often divided into two types: good news and bad news. News with good content has the ability to create positive effects on beneficiaries, and thus children are able to imitate good habits from social networks. On the contrary, news with bad content has the ability to affect children’s habits and therefore has the ability to negatively affect the formation of habits and the development of young children. Although there have been studies on the impact of social networks on children and have shown both positive and negative impacts of social networks on children’s habits, previous studies were conducted in countries with relatively different socioeconomic contexts from Vietnam. In fact, Vietnam is known as a country in the Asia–Pacific region and has a rapid economic growth rate. Vietnam’s economic development has a great contribution from a young workforce with high consumption capacity and the ability to access technology and use the internet and social networks. High internet access and high use of social networks have had a certain impact on children’s habits. Therefore, the objective of this study is to clarify the impact of social networks on children in the case of Vietnam through a survey of about 190 families in Vietnam. The research results are the basis for proposing a number of solutions to improve the effectiveness of using social networks in Vietnam in the coming time. The research paper has 5 main parts, in addition to the problem statement, the remaining parts are as follows: overview of previous studies, data collection and research methods, results and discussion of research results, general conclusion of the research.

2 Literature Review As mentioned above, social networks are becoming one of the very important technology platforms to meet the needs of enjoyment of every individual in society. Participating in social networks has the ability to meet the entertainment needs of all social components while business activities, information research and connection can also be exploited on social networks. It can be said that social networks bring many different benefits to users and the economy. Mallya et al. [4] stated that the average screen time of children and adolescents has increased significantly in recent

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years and the pandemic has especially exacerbated this trend due to the transformation of education and online activities. Therefore, improving the quality of online content becomes urgent and important to improve the quality of social media use. Furthermore, the quality of information on social media, especially for young people, has the potential to influence children’s perceptions and change their behaviors and habits. Mallya et al. [4] suggested that social media platforms should be called upon to moderate content to help individuals have a more positive life experience. It is assumed that influencers are children who participate on social media platforms and create content that attracts views and interactions. Meyerding and Marpert [5] argued that to reach young people, businesses are increasingly using child influencers to promote products to children through channels and videos related to the products and brands featured in the videos. Therefore, these activities will have an impact on children’s awareness, helping them to be able to use the brand from the business. Montag et al. [6] argued that there are billions of people using social media, including many teenagers using social media accounts on applications. According to statistics, each individual spends an average of 151 min/day on social media to participate in social activities that are regionally and globally connected. It can therefore be seen that for many adolescents and young adults who spend a lot of time on these platforms, the platforms become an integral part of their lives. As a result, adolescents may be vulnerable to the features and advertisements displayed on social media platforms. The increasing prevalence of mental illness in young people has led to recommendations for regular screening for anxiety in children and the risk of depression and suicide in adolescents. Another study, [2] suggested that young people who engage in social media may be exposed to risks of fraud, extortion, and misinformation, which increases their involuntary participation in some activities. Chonka [2] also suggested that social media has an ambiguous role in shaping the aspirations and experiences of young people, meaning that the positive impact of social media netword on youth is not always seen. In recent years, social media has become an important part of people’s lives, including children. Bozzola et al. [1] argued that during the pandemic, media devices and internet access have increased rapidly and therefore adolescents have increased their internet connection through Instagram, TikTok or Youtube channels. Indeed, using the internet allows communication with friends and online learning activities at school. Bozzola et al. [1] also warned about the use of media and some adverse consequences, especially in the most vulnerable people such as young children. The consequences can be listed as sleep, addiction, anxiety, psychophysiological problems, behavior, body image, vision, headaches and tooth decay. Therefore, public and medical awareness must be raised to find new preventive measures and increase awareness of risks associated with social media use to maintain the health of young children and prevent negative outcomes for young children. Furthermore, [7] argued that social media orientation is not easy, especially for children when the number of young people using social media platforms has increased in recent years. Ortutay [7] documented that 58% of American teenagers use TikTok daily, of which 17% of them use it with high frequency and at the same time they use Snapchat and Instagran daily

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at a high level. Therefore, each family is increasingly concerned about the impact of social media use on young people and online safety for children, while current legal regulations are still weak and not enough to protect children from the negative effects of social media. In the case of the United States, this country has set rules that prohibit children under 13 from using advertising platforms without parental consent. Specifically, protecting children’s online privacy is tied to the requirement that websites and online services publish privacy policies and obtain parental consent before collecting information from their children. In the current era of globalization, technology is increasingly asserting its importance in daily life such as in education, work, communication, entertainment needs, so many children can see negative videos on social networks. Wahyuni et al. [9] also affirmed the positive and negative impacts of social networks on human life, including children. The authors believed that children prefer to play social networks at school rather than interact with children of the same age. Using quantitative methods through data collected from online interviews, the author believes that social networks have a great influence on children’s learning at school and at home. Social networks make children learn less effectively when social networks make children busier and lack of supervision from parents makes the learning process not achieve the desired results. This result explains that the impact of social media on children’s learning process is very influential, so families should have control over their children’s participation in social media so as not to affect their development. Another recent study, [10] also agreed with the view that children are growing up in a 24/7 technological world and social media can cause sadness, anxiety and can replace sleep, movement and connection in real life, and therefore it aggravates the health crisis for adolescents. Therefore, [10] made some recommendations to improve the mental health of adolescents participating in online activities, which are better online protection policies, expanding media literacy programs. Furthermore, developing technology leaders and innovators, comprehensive research to better understand the impact of social media on young people.

3 Data and Methodology 3.1 Data In this study, we collect primary data when conducting a survey of parents to better assess their views on the impact of social media on children’s perceptions and behaviors. The study is conducted on a sample of about 193 families living in Southern Vietnam. After the survey, we eliminate the samples that do not meet the requirements, so the remaining sample size is about 190. According to the study of [3], the optimal sample size should not be less than 50 + 8 m, in which m is the number of independent variables in the model, so the minimum sample size achieves is 90. From

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Table 1 Variables in the model Variable

Sign

Expected sign

Previous studies

Dependent variable Kids’ behavior

KB

[4]

Independent variable Social media networks

MEDIA

+

[4, 6]

Family

FAM

+

[7]

Content

CONT

+

[4, 6]

Frequency

FRE

+

[7]

School

SCH

+

Author’s suggestions

Source Authors’ analysis

that, it can be seen that the selected sample size of 140 is larger than the minimum sample size, thereby confirming that the reliability level in this study is guaranteed.

3.2 Methodology We used SPSS software to perform the estimation analysis in this study. The analyses include: descriptive statistical analysis, scale reliability analysis, exploratory factor analysis and regression (Table 1). Based on the previous studies summarized above, the expected regression equation is written as follows: KB = β0 + β1 MEDIA + β2 FAM + β3 CONT + β4 FRE + β5 SCH + µ

4 Results 4.1 Cronbach’s Alpha Test According to the study of [3], any variable with a corrected-item total correlation coefficient greater than 0.3 and a Cronbach’s alpha coefficient greater than 0.6 proves to satisfy reliability. The research results in Table 2 show that all variables have corrected-item total correlation coefficients greater than 0.3 and Cronbach’s alpha greater than 0.6, so they satisfy reliability, which is the basis for EFA analysis.

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Table 2 Cronbach’s alpha test Variable

Corrected item-total correlation

Cronbach’ alpha if item deleted

Kids’ behavior–Cronbach’s alpha = 0.768 KB2

0.610

0.724

KB1

0.578

0.760

KB5

0.588

0.732

KB3

0.643

0.704

KB4

0.602

0.736

Social media networks–Cronbach’s alpha = 0.812 MEDIA4

0.642

0.772

MEDIA2

0.612

0.762

MEDIA3

0.671

0.746

MEDIA1

0.603

0.778

Family–Cronbach’s alpha = 0.792 FAM2

0.652

0.712

FAM3

0.598

0.723

FAM1

0.626

0.742

FAM4

0.692

0.727

FAM5

0.625

0.734

Content–Cronbach’s alpha = 0.844 CONT1

0.648

0.812

CONT2

0.652

0.815

CONT4

0.640

0.810

CONT3

0.689

0.798

Frequency–Cronbach’s alpha = 0.832 FRE2

0.660

0.762

FRE1

0.638

0.758

FRE3

0.630

0.754

FRE4

0.658

0.760

School–Cronbach’s alpha = 0.820 SCH2

0.652

0.786

SCH1

0.670

0.779

SCH5

0.665

0.782

SCH3

0.700

0.750

SCH4

0.620

0.790

Source Authors’ analysis

Effect of Social Media Networks on People’ Bahaviour in the Light …

127

4.2 Exploratory Factor Analysis To perform EFA analysis successfully, the KMO test coefficient must be above 0.5 to confirm the appropriateness of factor analysis. Bartlett test for Sig value less than 5% confirms that the observed variables are correlated with each other. Moreover, Eigenvalue value is always greater than 1 and is retained in the analysis model. Through the results of this study, it shows that the KMO, Bartlett test and Eigenvalue values are all satisfactory and thereby show that the analysis results are acceptable. Table 3 presents the results of EFA analysis showing that the total variance explained is greater than 50%, so it confirms that the results of EFA analysis are appropriate.

4.3 Correlation Matrix When analyzing correlation to detect the occurrence of multicollinearity in the regression model. When the independent variables have a correlation level of less than 0.8, there is no possibility of multicollinearity. The results of Table 4 show that the largest correlation coefficient is 0.715 and is less than 0.8, so it can be affirmed that there is no possibility of multicollinearity in the estimated model.

4.4 Results The regression results show that: The regression coefficient of MEDIA is positive and statistically significant, which shows the positive role of social media on children’s behavior. That is, social media plays a very positive role in shaping children’s habits and behaviors in the case study in Vietnam. This result confirms that the recent social media development policy in Vietnam has created a positive effect on forming habits for children in Vietnam. This research result is supported by [9] arguing that there is a positive impact of social media on human life, including children. In particular, the policy of the Vietnamese government can limit the impact of social media on children through social network control mechanisms and increase appropriate information access controls for children, which can make the benefits of social media best for people and children. Therefore, in addition to positive benefits, controlling negative benefits can help children form positive behaviors and habits, thereby creating future generations with great potential to contribute to the socio-economic development of the country. Learning from the experience of the United States, [7] indicated that this country has set rules prohibiting children under 13 years old from using advertising platforms without parental consent, and at the same time increasing appropriate regulations to protect children’s online privacy (Table 5). The research results show that the regression coefficient of FAM is positive and statistically significant. This result shows that the family has a positive influence on

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Table 3 Rotated component matrix Variable

Scale

Component 1

Content

Frequency

Social media networks

Family

School

2

CONT1

0.812

CONT2

0.801

CONT4

0.783

CONT3

0.735

3

FRE2

0.800

FRE1

0.785

FRE3

0.752

FRE4

0.740

4

MEDIA4

0.788

MEDIA2

0.767

MEDIA3

0.752

MEDIA1

0.729

5

FAM2

0.776

FAM3

0.766

FAM1

0.762

FAM4

0.750

FAM5

0.724

SCH2

0.752

SCH1

0.741

SCH5

0.723

SCH3

0.703

SCH4

0.687

Rotation sums of squared loadings in total

6.201

4.247

1.984

1.753

1.345

Rotation sums of squared loadings in cumulative %

25.247

45.368

48.236

60.225

68.225

Source Authors’ analysis Table 4 Correlation matrix Biến

KID

KID

1.000

MEDIA

0.542

1.000

FAM

0.477

0.715

1.000

CONT

0.135

0.242

0.165

1.000

FRE

0.103

0.491

0.391

0.105

1.000

SCH

0.201

0.310

0.168

0.281

0.318

Source Authors’ analysis

MEDIA

FAM

CONT

FRE

SCH

1.000

Effect of Social Media Networks on People’ Bahaviour in the Light …

129

Table 5 Results Variable

Unstandardized coefficients

Standardized coefficients

t

Sig.

B

S.E

_cons

3.013

0.491

6.136

0.000

MEDIA

0.321

0.094

0.342

3.424

FAM

0.278

0.086

0.298

CONT

0.251

0.258

0.286

FRE

0.206

0.283

SCH

0.178

0.060

Collinearity test Tolerance

VIF

0.000

0.908

1.1013

3.238

0.000

0.914

1.0940

0.974

0.179

0.802

1.2468

0.234

0.729

0.351

0.901

1.1098

0.197

2.973

0.000

0.895

1.1173

Source Authors’ analysis

the formation of children’s habits and behaviors. Indeed, Vietnam is a country located in the East Asia region and has a close relationship with Asian culture. In Vietnam, children are often given much attention by their parents, so parents often have a great influence on their children and thereby form habits for their children. Therefore, the influence from parents is in the same direction as the formation of children’s habits and behaviors. The study on the influence of social media on children’s behaviors and habits and this result is supported by [8] is also conducted by another study in Nha Trang, Vietnam indicating that families practice positive parenting, it is likely to impact children’s development, at the same time, development goes hand in hand with the formation of children’s personality and attitudes and ensures harmonious development for children. The research results also show that the regression coefficients of CONT and FRE are positive but not statistically significant, meaning that there is no evidence of the impact of social media content or frequency of social media participation on the formation of children’s habits. However, the regression coefficient of SCH is positive and statistically significant, which shows that there is a positive influence from school on children’s habits. This result supports the policy of investment and school development in Vietnam that has been promoted in recent times. The Vietnamese government has increased investment in school facilities and learning conditions for children while improving the quality of the teaching staff to ultimately improve the quality of learning.

5 Conclusion The world is witnessing the rapid development of the scientific and technological revolution and their great contributions to the lives of people and the economy in each country. Technological development has led to the formation and development of social networks, social media and it can be said that social networks have helped people get closer together through the ability to connect, communicate, and interact

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quickly and conveniently. Technology has made the distance between people and people, between countries and countries closer than ever. It can be said that the benefits of the technological revolution to human life are greater than ever. However, children are also the beneficiaries of technological achievements but at the same time are vulnerable when they can be exposed to negative information channels that affect their habits and behaviors. Researching the impact of social media through surveys, the research results strongly confirm the positive impact of social media on children, at the same time the positive impact of family and school on children. Research suggests that there is no effect of the content and frequency of social media use on children.

References 1. Bozzola, E., Spina, G., Agostiniani, R., Barni, S., Russo, R., Scarpato, E., Di Mauro, A., Di Stefano, A. V., Caruso, C., Corsello, G., & Staiano, A. (2022). The Use of Social Media in Children and Adolescents: Scoping Review on the Potential Risks. International journal of environmental research and public health, 19(16), 9960. https://doi.org/10.3390/ijerph191 69960. 2. Chonka, P. (2024). Social media, youth (im)mobilities, and the risks of connectivity in urban Somaliland. Mobilities, 19(1), 33–51. https://doi.org/10.1080/17450101.2023.2206042. 3. Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York. 4. Mallya, M. M., Liu-Zarzuela, J., Munoz, I. B., & Shotwell, J. (2024). Assessing the Educational Value of YouTube Kids Videos Related to GAD, MDD, and ADHD. Journal of the American Academy of Child & Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2024.05.026. 5. Meyerding, S. G. H., & Marpert, J. D. (2023). Modern pied pipers: Child social media influencers and junk food on YouTube – A study from Germany. Appetite, 181, 106382. https://doi. org/10.1016/j.appet.2022.106382. 6. Montag, C., Demetrovics, Z., Elhai, J. D., Grant, D., Koning, I., Rumpf, H.-J., M. Spada, M., Throuvala, M., & van den Eijnden, R. (2024). Problematic social media use in childhood and adolescence. Addictive Behaviors, 153, 107980. https://doi.org/10.1016/j.addbeh.2024. 107980. 7. Ortutay, B. (2024). Keeping children safe on social media: What parents should know to protect their kids. Available at https://apnews.com/article/social-media-kids-teens-instagramtiktok-parents-safety-45df57c32384207ffedc463870b8a6d2, accessed on August 2024. 8. Viet, T. H., Nanthamongkolchai, S., Munsawaengsub, C., & Pitikultang, S. (2022). Positive Parenting Program to Promote Child Development Among Children 1 to 3 Years Old: A Quasi-Experimental Research. Journal of primary care & community health, 13, 21501319221089763. https://doi.org/10.1177/21501319221089763. 9. Wahyuni, N., Putri, D. K., Widiyastuti, S., Siburian, H. K., & Saputra, D. G. (2024). The Impact of Social Media on the Learning Process of Children Aged 6–12 Years Old. Journal International of Lingua and Technology, 3(1), 29–42. https://doi.org/10.55849/jiltech.v3i1.507 10. Wulff, R. (2023). New report on how social media negatively impacts kids. Available at https://www.cbsnews.com/sacramento/news/new-report-how-social-media-negatively-imp acts-kids/, accessed on August 2024.

An Efficient High-Speed Feature Extraction Analysis Methods of Digital Images in Social Media Platforms Jawad H. Alkhateeb, Rashad Rasras, Ziad Alqadi, Mutaz Rasmi Abu Sara, Aiman Turani, and Rashiq Rafiq Marie

Abstract Digital color image retrieval and recognition are very important tasks associated with digital image processing, so increasing the efficiency and accuracy of the retrieval system is a vital issue. In fact, using an image key or signature with a very small size is the solution for reducing the image-seeking time in any retrieval system. This key can be constructed from extracted image features, and this key must be small and unique. In this paper, various methods of color image feature extraction will be studied and, experimentally tested. Finally, the obtained results will be analyzed and compared to raise some facts and recommendations. In this paper, an efficient high-speed feature extraction framework for extracting features from various social media platforms. Basically, these methods are exploited in field of social media platforms such as Facebook, Twitter, Instagram. Keywords CSLBP · Features · Fuzzy clustering · Image key · K_mean clustering · LBP · RLBP clustering · Social media platforms

J. H. Alkhateeb (B) College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia e-mail: [email protected] R. Rasras · Z. Alqadi Department of Electrical Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan M. R. A. Sara IT Department, Faculty of Engineering and Information Technology, Palestine Ahliya University, Bethlehem, Palestine A. Turani · R. R. Marie College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_8

131

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J. H. Alkhateeb et al.

Fig. 1 Color image and histograms

1 Introduction The digital color image is one of the most widely used data types, and it is used in various important applications. Some of these applications require quick image retrieval. Thus, reducing the image retrieval is a vital problem associated with increasing the retrieval system efficiency. Digital color image, especially images with high resolution, has a huge size [1–3]. Using this large number of pixels in the matching process will lead to poor efficiency [4–6]. To reduce the number of data elements that participated in a matching process. The histogram image is well utilized. It gives a clear detailed feature of the color image. These features can be used instead of directly using the image pixels [7, 8]. Figure 1 shows a color image example and the associated three colored histograms. Basically, the color image histogram is a three-column matrix [9, 10]. Each row contains 256 elements, and each element value points to the repetition of the color in the associated color channel. The three histograms can be added together in order to produce the total histogram. Figure 2 illustrates this mechanism. By using the total image histogram, deciding a decision can be faster by reducing the processed elements. Furthermore, this yields to reduce the matching time which make the matching process more efficient.

2 Processing Social Media Images Using the various social media platforms such as Facebook, Twitter, Instagram has increased recently. Various posts of images, videos, and textual messages have been posted by various users. So, recognizing and classifying the images on the various social media platforms is an important task. To do such task extracting the proper features need to be done efficiently. The proposed feature extraction methods can be

An Efficient High-Speed Feature Extraction Analysis Methods …

133

Fig. 2 Image total histograms

exploited in extracting the proper features for any content in the various social media platforms.

3 Color Image Feature Extraction Based on LBP The local binary pattern (LBP) method uses the pixel neighbors to calculate the LBP operator for each pixel. These LBP operators form an LBP histogram [9, 10]. Figure 3 illustrates the LBP operator calculation. Meanwhile, Fig. 4 illustrates an example of calculating the pixel LBP operator. Mainly, Calculating LBP operators produces an LBP image with a new LBP histogram which is summarized in Fig. 5 [9, 12, 13]. However, the LBP histogram still has 256 elements which need to be reduced in order to use the reduced histogram as an image feature [14]. The Center symmetric LBP (CSLBP) method reduces the features to sixteen (16) elements. In this case, the CSLBP operator can be calculated as illustrated in Fig. 6. Meanwhile, while Fig. 7 illustrates an example of calculating the CSLBP operator: At this moment, a method based on LBP can be introduced, which it is called the reduced LBP (RLBP) method. In this method, the color image features can be reduced to four elements. Figure 8 illustrates the RLBP operator calculation t for a pixel.

134

Fig. 3 LBP operator calculation Fig. 4 Example of calculating Pixel LBP operator

Fig. 5 Image histogram and LBP histogram

J. H. Alkhateeb et al.

An Efficient High-Speed Feature Extraction Analysis Methods …

135

Fig. 6 Calculating CSLBP operator Fig. 7 Example of calculating CSLBP operator

Fig. 8 RLBP operator calculation

4 Classification and Feature Selection Clustering is defined as the grouping of data elements into groups. The clustering idea is illustrated in Fig. 9 [14–18]. K mean clustering is one of the most popular techniques for grouping data [14, 15]. In order to use each of them as color image features, it is possible to compute the cluster centroids, the number of data items in each cluster, or the sums within each cluster. A color image histogram can be the input data set for K mean clustering, and in this case the derived features will remain

136

J. H. Alkhateeb et al.

stable and the same even if the image is rotated, as shown in Fig. 10. The histogram of the rotated image will be equal to the histogram of the original image. The steps listed below illustrate how the K_means clustering algorithms operate [13, 14]: (1) The input data set should be prepared by calculating the color picture histogram. (2) Initializing the clusters by choosing the number of clusters and the centroid of each cluster. (3) Centroids make the next pass even though the calculated values have changed [15, 16]: (a) Calculate and determine the distances between each point and the centroid of the cluster, which are equal to the absolute value of the difference between the center and the value of each data item.

Fig. 9 Grouping data Into 2 clusters

Fig. 10 Histograms for rotated images

An Efficient High-Speed Feature Extraction Analysis Methods …

137

Table 1 Pass1 and Pass2 x

Distant 1 Distant 2 Nearest New Abs(x–c1) Abs(x–c2) cluster centers Pass 1

242 192

Distant 1

Distant 2

Nearest New cluster centers Pass 2

142

2

8

42

1

154 104

54

2

123

73

23

2

227 177

127

2

191.6667

46.8235 2

194 144

94

2

158.6667

13.8235 2

116

66

16

2

80.6667

64.1765 2

4

46

96

1

209 159

109

2

173.6667

28.8235 2

13

2

77.6667

67.1765 2

156 106

56

2

120.6667

24.1765 2

201 151

101

2

165.6667

20.8235 2

235 185

135

2

199.6667

54.8235 2

188 138

88

2

152.6667

7.8235 2

44

6

56

1

103

53

3

2

67.6667

77.1765 2

238 188

138

2

202.6667

57.8235 2

233 183

133

2

197.6667

52.8235 2

54

4

2

68.6667

76.1765 1

227 177

127

2

191.6667

46.8235 2

58

113

104

63

C1 = 206.6667 61.8235 2 35.3333 22.6667 122.1765 2 C2 = 180.1765 118.6667 26.1765 2 87.6667 57.1765 2

C1 =54 C2 = 170.0556

31.3333 176.1765 1

8.6667 136.1765 2

(b) Choose the cluster to which the points belong based on distance. (c) Calculate the new center by averaging the cluster’s points. There were many passes implemented and used. Table 1 illustrates Pass1 and Passs 2. Pass 3 and Pass 4 are similarly illustrated in Table 2. Table 3 illustrates Passes 5 and 6 in detail.

5 Fuzzy C_Mean Clustering Applying the steps below, as demonstrated in the example below, will enable this method to be used and implemented. Step1: Initialization:

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Table 2 Pass3 and Pass4 x

Distant 1 Distant 2 Nearest New Abs(x–c1) Abs(x–c2) cluster centers Pass 3

242 188

Distant 1

Distant 2

Nearest New cluster centers Pass 4

71.9444

2

4

112.0556

1

154 100

16.0556

2

123

69

47.0556

2

227 173

56.9444

2

164.4000

43.6667 2

194 140

23.9444

2

131.4000

10.6667 2

116

62

54.0556

2

53.4000

67.3333 1

4

58

C1 = 179.4000 58.6667 2 62.6000 4.6000 125.3333 1 C2 = 183.3333 91.4000 29.3333 2 60.4000 60.3333 2

50

166.0556

1

209 155

38.9444

2

146.4000

25.6667 2

113

59

57.0556

2

50.4000

70.3333 1

156 102

14.0556

2

93.4000

27.3333 2

201 147

30.9444

2

138.4000

17.6667 2

235 181

64.9444

2

172.4000

51.6667 2

125.4000

4.6667 2

188 134

C1 = 77.4286 C2 = 193.9231

58.6000 179.3333 1

17.9444

2

44

10

126.0556

1

18.6000 139.3333 1

103

49

67.0556

1

40.4000

80.3333 1

238 184

67.9444

2

175.4000

54.6667 2

233 179

62.9444

2

170.4000

49.6667 2

104

50

66.0556

1

41.4000

79.3333 1

227 173

56.9444

2

164.4000

43.6667 2

X

Y

C1

C2

1

6

0.8

0.2

2

5

0.9

0.1

3

8

0.7

0.3

4

4

0.3

0.7

5

7

0.5

0.5

6

9

0.2

0.8

Step 2: Utilizing the equation, find and locate new centers:   m    m  µj yi ) yi i µj [xi ) xi Cj =   m , i   m i µj [xi ) i µj yi )

(1)

33.4286

44

76.5714

110.5714

188

19.4286

157.5714

235

227

123.5714

201

104

78.5714

156

164.5714

35.5714

113

233

131.5714

209

25.5714

73.4286

4

160.5714

38.5714

116

238

116.5714

194

103

45.5714

76.5714

154

149.5714

19.4286

58

227

164.5714

242

123

Distant 1 Abs(x–c1)

x

39.9231

135.9231

48.0769

44.0769

90.9231

149.9231

5.9231

41.0769

7.0769

37.9231

80.9231

15.0769

189.9231

77.9231

0.0769

33.0769

70.9231

39.9231

135.9231

48.0769

Distant 2 Abs(x–c2)

Table 3 Pass5 and Pass6

2

1

2

2

1

1

2

2

2

2

1

2

1

1

2

2

1

2

1

2

Nearest cluster C1 =83.1250 C2 =199.8333

New centers Pass 5

110.8750

143.8750

39.8750

70.8750

25.1250

158.8750

104.8750

151.8750

117.8750

72.8750

29.8750

125.8750

79.1250

32.8750

110.8750

143.8750

39.8750

70.8750

25.1250

158.8750

Distant 1

5.8333

27.1667

76.8333

45.8333

141.8333

42.1667

11.8333

35.1667

1.1667

43.8333

86.8333

9.1667

195.8333

83.8333

5.8333

27.1667

76.8333

45.8333

141.8333

42.1667

Distant 2

2

2

1

2

1

2

2

2

2

2

1

2

1

1

2

2

1

2

1

2

Nearest cluster

C1 =83.1250 C2 =199.8333 No change Cluster1:58,123,116,4,113,44,103,104 Cluster 2: 242, 154, 227, 194,209, 156,201, 235, 188, 238, 233,227

New centers Pass 6

An Efficient High-Speed Feature Extraction Analysis Methods … 139

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J. H. Alkhateeb et al.

1∗0.82 +2∗0.9 2 +3∗0.7 2 +4∗0.3 2 +5∗0.5 2 +6∗0.2 2 , 0.82 +0.9 2 +0.7 2 +0.3 2 +0.5 2 +0.2 2  6∗0.82 +5∗0.9 2 +8∗0.7 2 +4∗0.3 2 +7∗0.5 2 +9∗0.2 2 0.82 +0.9 2 +0.7 2 +0.3 2 +0.5 2 +0.2 2   5.58 14.28 , C1 = 2.32 2.32  1∗0.22 +2∗0.1 2 +3∗0.3 2 +4∗0.7 2 +5∗0.5 2 +6∗0.8 2 C2 , 0.22 +0.1 2 +0.3 2 +0.7 2 +0.5 2 +0.8 2  6∗0.22 +5∗0.1 2 +8∗0.3 2 +4∗0.7 2 +7∗0.5 2 +9∗0.8 2 0.22 +0.1 2 +0.3 2 +0.7 2 +0.5 2 +0.8 2   7.38 10.48 , C2 = 1.52 1.52

C1 =



C2 (4.8, 6.8) = Step 3: Find distant D1 =



(x2 −x 1 )2 + (y2 −y 1 )2

(2)

Centroid 1: (1, 6) (2.4, 6.1) =



(1.4)2 + (0.1)2 =

(2, 5) (2.4, 6.1) =



(3, 8) (2.4, 6.1) =



(4, 4) (2.4, 6.1) = (5, 7) (2.4, 6.1) =





(1.96) + (0.01) =

(0.16) + (1.21) =

(0.36) + (3.61) =

(2.56) + (4.41) =

√

√

1.97 =1.40

1.37 =1.17

√ 3.97 =1.99 √

6.97 =2.64

√ (6.76) + (0.81) = 7.57 =2.75

(6, 9) (2.4, 6.1) =

√ (12.96) + (8.41) = 21.37 =4.62

(1, 6) (4.8, 6.8) =



Centroid 2: (14.44) + (0.64) =

√

15.08 =3.88

An Efficient High-Speed Feature Extraction Analysis Methods …

(2, 5) (4.8, 6.8) =



(3, 8) (4.8, 6.8) =



(4, 4) (4.8, 6.8) = (5.7) (4.8, 6.8) = (6, 9) (4.8, 6.8) =

(7.84) + (3.24) = (3.24) + (1.44) =

141

√ 11.08 =3.32 √ 4.68 =2.16

√ (0.64) +7.84 = 8.48 =2.91



(0.04) + (0.04) =

√

0.08 =0.28

√ (1.44) + (4.84) = 6.28 =2.50

Cluster 1

Cluster 1

Data point

Distance

Data point

Distance

(1, 6)

1.40

(1, 6)

3.88

(2, 5)

1.17

(2, 5)

3.32

(3, 8)

1.99

(3, 8)

2.16

(4, 4)

2.64

(4, 4)

2.91

(5, 7)

2.75

(5, 7)

0.28

(6, 9)

4.62

(6, 9)

2.50

Step 4: Utilizing the equation, locate new centers: Use the equation below to update membership value:

µj (xi ) =

1  m−1

1 dji

c

k=1



1 dki

1 m−1

Cluster 1

µ11 =

µ11 =

1 d11

 1 1 1.40  1 1  1 1 + 3.88 1.40 

1 d11

1  2−1

=

µ12 =

1  2−1

+

1 d21

1  2−1

0.71 0.71 = =0.7 07.1 +0.25 0.96

 1 d12

1 d12



+

1 d22



(3)

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µ12 = 

1 1.17

µ13 = 

µ14 = 

µ15 = 





1 1.99

1 2.64

1 2.75

µ16 = 

1 1.17

+









1 4.62



1 3.32

µ13 =

1 1.99





+

1 2.64





+

1 2.16

1 2.91

+





1 0.281

+





1 2.50

+

1 d23

1 d24

1 d25

1 d26



1 d14



+



0.37 0.37 = =0.5 0.37 +0.34 0.71

 1 d15

1 d15



+



0.36 0.36 = =0.1 0.36 +3.57 3.93

 1 d16

1 d16

 =



0.5 0.5 = =0.5 0.5 +0.46 0.96



1 d14

 =

µ16 =

1 4.62

1 d13

 =





1 d13

 =

µ15 =

1 2.75

0.85 0.85 = =0.74 0.85 +0.30 1.15



 =

µ14 =









+



0.21 0.21 = =0.3 0.21 +0.4 0.61

Cluster 2

µ21 = 

1 1.40





µ21 =

1 3.88

+





1 3.38

1 d12

 =

µ22 =



1 d21

+



1 d21

1 d22



0.25 0.25 = =0.3 0.71 +0.25 0.96



1 d12

1 d22



+



An Efficient High-Speed Feature Extraction Analysis Methods …

µ22 = 

1 1.17

µ23 = 



1 3.32



1 1.99

+









1 3.32

1 2.16





+

1 2.16

µ24

µ24 = 

µ25

=

1 2.64



1 2.19



+





0.3 0.3 = =0.26 0.85 +0.30 1.15



 =

µ13 =

1 2.91

1 d13

1 d13

 =

=



+

1 d23

1 d24

1 d25

1 d26



0.46 0.46 = =0.5 0.5 +0.46 0.96

 1 d24

1 d14



+



0.34 0.34 = =0.5 0.37 +0.34 0.71



 =

µ25 =

143

1 d25

1 d15



+



 1 3.57 3.57 0.28    = = =0.9 1 1 0.36 +3.57 3.93 + 2.75 0.28



µ26 = 



1 4.62





µ26 =

1 2.50

+





1 2.50

1 d26

1 d16

 =



+



0.4 0.4 = =0.7 0.21 +0.4 0.61

Currently, the membership value is: X

Y

C1

C2

1

6

0.7

0.3

2

5

0.74

0.26

3

8

0.5

0.5

4

4

0.5

05

5

7

0.1

0.9

6

9

0.3

0.7

Until there are no more centroids changes, the previous processes must be repeated.

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6 Implementation and Experimental Results To put the previously mentioned technique of extracting color image attributes into practice, a Matlab code was built. Different digital color photos of varying sizes were processed using the LBP method, and the produced histograms were used as an input data set for each clustering technique. The experimental outcome of employing the CSLBP technique is shown in Table 4. Although this method has a great capacity, the large number of features makes the image retrieval or recognition process more laborious and memory intensive. The outcomes of the application of the RLBP approach are shown in Table 5. As shown in Table 5, the RLBP approach decreases the number of features to 4, however it has a limited capacity. The K mean method of clustering results are shown in Table 6, and from this table, we can see the following: • The number of features is also reduced to 4. • Depending on the amount of clusters present at the beginning, the number of features may change. • The capacity of this method is very high. • Even when the input color image is rotated, the features remain stable. Table 7 summarizes the results of the fuzzy clustering method implementation. Table 4 CSLBP method results Imgae1 Imgae2 Imgae3 Imgae4

Imgae5

Imgae6 Imgae7

Imgae8 Imgae9 Imgae10

16,768

8889

80,982

478,342 618,790 12,030

165,171 23,627

15,447

15,240

13,256

5908

32,178

371,669 310,108 6720

40,629

8433

12,971

15,298

3632

2266

5532

82,350

8684

1698

3189

4472

21,233

9257

75,706

668,997 605,574 9141

52,413

50,820

19,294

14,692

15,604

4854

2774

10,544

148,601 102,575 3862

12,844

3622

4568

3782

4374

2319

9491

135,306 88,101

3469

10,838

2966

4219

3299

3090

1875

4260

73,016

2713

7414

1373

3077

4609

15,310

7535

68,309

653,855 488,408 11,639

35,874

16,747

14,830

14,263

20,431

10,055

86,447

754,724 631,415 10,905

49,683

24,694

18,057

17,562

3139

1689

3789

65,049

28,941

1839

6470

1407

2742

4108

4478

2810

9800

142,758 90,513

4037

11,376

2919

4175

3887

4294

2161

8360

121,762 79,708

3235

9549

2395

3873

3337

14,931

7052

63,803

651,526 515,731 15,127

38,362

22,207

15,271

14,393

2603

1707

3565

65,628

5307

939

2634

Imgae10

9098

5324

25,734

371,015 390,483 23,157

37,371

10,777

17,206

15,240

7790

5105

26,784

346,580 237,353 8492

24,892

6233

12,380

15,298

51,577

25,698

2548

1871

An Efficient High-Speed Feature Extraction Analysis Methods …

145

Table 5 RLBP method results Image

Size (pixel)

Features

Extraction time (S)

1

150,849

60,594

8016

10,679

66,976

2

77,976

39,620

9300

4808

21,604

7.477000

3

518,400

184,192

40,993

42,224

243,807

49.324000

4

15,422,400

2,017,674

481,678

488,240

2,129,740

501.008000

5

4,326,210

1,947,659

385,262

192,096

1,779,645

417.427000

6

122,265

54,044

7147

5740

51,726

11.590000

7

518,400

155,976

35,656

38,073

281,511

50.009000

8

150,975

63,702

6565

8714

67,978

14.533000

9

150,975

64,205

12,682

9122

60,950

14.539000

10

151,353

56,162

10,601

11,598

69,192

14.477000

Rotated 10 90°

151,353

58,485

12,896

12,868

63,304

14.442000

Average

2,159,000

Capacity (Pixel per second)

19,725

14.188000

109.4572

7 Conclusion and Future Work Various techniques for extracting color image features were explored, and the results demonstrated that this technique is well suited to clustering algorithms. Furthermore, the size of the feature set will vary based on the number of clusters. The features set can be formed from the cluster’s centers, the number of points in each cluster, or the within cluster sums, and the extracted feature set for each color image is distinct and unique, allowing it to be used as a signature or a key to identify or recognize the image. Furthermore, because the features are dependent on the histogram, the features data set remains constant while the image is rotated. Finally, the K_mean approach is more efficient and effective, and it can also contain more and larger data.

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Table 6 K_mean clustering method results Image

Size (pixel)

Features

1

150,849

4221

585.3

358.6

981.8

0.1230

2

77,976

485

2050.1

697.6

212.0

0.1230

3

518,400

0.0686

4.8384

0.2030

0.3486

0.1260

4

15,422,400

37,312

6241

16,395

25,486

0.1260

5

4,326,210

20,943

15,519

28,485

7626

0.1350

6

122,265

38.8649

311.7544

983.0566

687.7361

0.1240

7

518,400

113,410

2920

390

1430

0.1240

8

150,975

251

814

614

14,446

0.1240

9

150,975

89.3929

903.9333

443.4796

676.6545

0.1200

10

151,353

221.9

1175

558

7402

0.1250

Rotated 10 151,353 90°

221.9

1175

558

7402

0.1250

Rotated 10 151,353 30°

221.9

1175

558

7402

0.1250

Rotated 10 151,353 10°

221.9

1175

558

7402

0.1250

Average

2,159,000

Capacity (Pixel per second)

17,272,000

Extraction time (S), including histogram time

0.1250

An Efficient High-Speed Feature Extraction Analysis Methods …

147

Table 7 Fuzzy clustering method results 1

150,849

589.0

4192.5

954.2

362.9

0.1800

2

77,976

1491.8

790

601

2767.3

0.1900

3

518,400

3471

654

48,384

2027

0.2050

4

15,422,400 6100

25,611

37,174

16,477

0.1560

5

4,326,210

7476

20,566

28,683

15,572

0.2150

6

122,265

41.8305

997.7585 338.3977 716.1241 0.2650

7

518,400

430

1520

113,410

2860

0.1670

8

150,975

611

770

14,446

238

0.1220

9

150,975

448.4286 67.9357

905.1145 702.7879 0.2050

10

151,353

222.4

1141.6

553.8

7394.7

0.1090

Rotated 10 90°

151,353

222.4

1141.6

553.8

7394.7

0.1090

Rotated 10 30°

151,353

222.4

1141.6

553.8

7394.7

0.1090

Rotated 10 10°

151,353

222.4

1141.6

553.8

7394.7

0.1090

Average

2,159,000

0.1814

Capacity (Pixel per second) 11,902,000

References 1. Ziad A. Alqadi, Majed O. Al-Dwairi, Amjad A. Abu Jazar and Rushdi Abu Zneit, “Optimized True- RGB color Image Processing, ” World Applied Sciences Journal 8 (10): 1175–1182, ISSN 1818-4952, 2010. 2. A. A. Moustafa, Z. A. Alqadi, “Color Image Reconstruction Using A New R’G’I Model,” journal of Computer Science Vol. 5, No. 4, pp. 250–254, 2009. 3. Jamil Al Azzeh, Hussein Alhatamleh, Ziad A. Alqadi, Mohammad Khalil Abuzalata, “Creating a Color Map to be used to Convert a Gray Image to Color Image,” International Journal of Computer Applications, Volume 153, Issue 2. 2016. 4. AlQaisi Aws and AlTarawneh Mokhled and Alqadi Ziad A. and Sharadqah Ahmad A, “Analysis of Color Image Features Extraction using Texture Methods,” TELKOMNIKA, volume 17, number 3, pages 1220—1225, year 2019. 5. Al-Azzeh J., Zahran B., Alqadi Ziad, Ayyoub B. and Abu-Zaher, M., “A novel zero-error method to create a secret tag for an image,” Journal of Theoretical and Applied Information Technology, volume 96, number 13, pages 4081–4091, 2018. 6. Moustafa, A.A., Alqadi, Ziad A., “A practical approach of selecting the edge detector parameters to achieve a good edge map of the gray image,” Journal of Computer Science, volume 5, number 5, pages 355–362, year 2009. 7. Al-Dwairi Majed O and Alqadi Ziad A and Abu jazar, Amjad A and Zneit, Rushdi Abu, “Optimized true-color image processing,” World Applied Sciences Journal, volume 8, number 10, pages 1175–1182, 2010. 8. Jamil Al Azzeh, Ziad A. Alqadi, Hussein Alhatamleh, Mohammad Khalil Abuzalata, “Creating a Color Map to be used to Convert a Gray Image to Color Image,” International Journal of Computer Applications, volume 153, number 2, pages 31–34, 2016.

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Cybersecurity and Digital Safety

Quantum Computing: Transforming Cybersecurity and Social Media in the Digital Age Syed Atif Ali Shah

Abstract Quantum computing is poised to revolutionize the landscape of technology by providing unprecedented computational power. While this advancement promises significant benefits across various domains, it also introduces new challenges, particularly in the realm of cybersecurity and social media. This chapter explores the potential impacts of quantum computing on cryptographic systems that secure social media platforms and other digital communication channels. It discusses how quantum attacks could compromise data privacy, integrity, and user trust, necessitating a shift towards quantum-resistant cryptographic techniques. Furthermore, the chapter delves into the transformative opportunities quantum computing could bring to social media analytics, artificial intelligence, and data mining, paving the way for smarter, more personalized user experiences. Keywords Quantum computing · Cybersecurity · Social media analytics · Threats · Quantum deep learning · Quantum artificial intelligence

1 Introduction Quantum computing represents a significant leap forward in computational power, with the potential to solve problems that are currently intractable for classical computers. As this technology evolves, it promises to transform a wide array of industries, from healthcare to finance. However, one of the most critical areas that will be impacted is cybersecurity [1], particularly the cryptographic methods that safeguard our digital world. This chapter explores the implications of quantum computing on

S. A. A. Shah (B) Bahria University, Islamabad, Pakistan e-mail: [email protected] Al-Madinah International University, Kuala Lumpur, Malaysia Integrating Solutions, Detechpro LLC, Newark, DE, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_9

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cybersecurity, with a focus on the potential threats to social media platforms and the necessity of developing quantum-resistant cryptographic techniques [2]. Quantum computing’s ability to solve complex mathematical problems at unprecedented speeds poses a direct challenge to the cryptographic systems that secure sensitive data. Traditional encryption methods, such as RSA and ECC [3], are vulnerable to quantum attacks, which could render current security measures obsolete. Social media platforms store vast amounts of user data, from personal information to private communications. The potential for quantum computing to break existing encryption poses a significant threat to these platforms, making it essential to develop and implement quantum-resistant algorithms [4]. Beyond threats, quantum computing offers opportunities for enhancing social media analytics. With its ability to process large data sets more efficiently, quantum computing could revolutionize how platforms analyze user behavior, trends, and interactions. To protect against the vulnerabilities introduced by quantum computing, a shift towards quantum-resistant cryptography is necessary. This section will discuss the methods currently being developed and how they can be integrated into existing systems, particularly in social media and other critical areas of cybersecurity. As quantum computing begins to challenge the foundation of modern cryptography, the repercussions for data security extend across all sectors, particularly social media. Platforms that billions rely on daily for communication and information-sharing are at significant risk. The next section will explore how these vulnerabilities might manifest and what can be done to mitigate them. While the potential threats posed by quantum computing are substantial, the technology also opens new doors for innovation, particularly in the realm of social media analytics. By harnessing quantum computing’s power, platforms can achieve more precise insights into user behavior, offering personalized experiences like never before [5].

2 Quantum Computing Quantum computing, why it is better than traditional computers, and what will solve for us in the future. First, it is important to talk a little bit about quantum mechanics. It is just really fascinating as the physics of extremely small particles in this case subatomic particles. For example, photons or the nuclei of atoms or electrons. They’re all relatively familiar with the laws of physics and how matter will interact in our daily lives. The interesting thing about quantum mechanics is that when things get to that subatomic level they start to play by different rules. If we throw a tennis ball against the wall we would expect it to bounce back. But if this tennis ball was an electron, well sometimes it would bounce back and sometimes it just appears on the other side of the wall. This phenomenon is called quantum tunneling and to be clear, it is not a theory. It’s an experimentally repeatable and proven behavior, scientists just expected even if it is hard for those of us living in a larger world to comprehend. There are a ton of other phenomena like that that are equally odd and just as repeatable and valid. This is because there are a few of these properties that are very important

Quantum Computing: Transforming Cybersecurity and Social Media …

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to quantum computing. It is within these weird phenomena that quantum computing gets a lot of its power. It could be ions the nucleus of an atom etc. is some sort of quantum particle. Containing them in a way that they can be manipulated and measured in traditional digital computers. Information is encoded in the form of bits a bit is either zero or one on or off and there are a lot of it. Together in our computers our phones etc. that can then be used to make calculations by changing those bits, the equivalent of those bits are called qubits or quantum bits [6]. The reason for this is that unlike traditional silicon bits the quantum bits are very easily interfered with, by any level of heat or just other subatomic particles in the environment around the computer. So to mitigate this, things are kept near 0 Kelvin, the temperature at which particles aren’t energized has very little to no movement. Taking a closer look at the qubits we might have an electron suspended using a magnetic field. For example, that electron will spin on its axis, and when it does a small magnetic field is created, similar to the magnetic field. Because it’s now also like the earth. This magnetic field ends up coming from one direction or pole and flowing to the other. The electron’s magnetic field when suspended in another magnetic field will naturally align its poles with the magnetic field’s direction. Think of it like the needle of a compass aligning with the earth’s magnetic field. So this is the natural state of the electron or qubit, requiring the least energy. This is the equivalent of the zero states in traditional bits called spin down. In this case similar to manually moving all of a compass with our finger to point S instead of north. Those electrons can be charged with energy at two points in the opposite direction. We will call this spin-up and can think of this as the one in terms of traditional bits. Now we have the two states that we’ve used in traditional computing zero and one. But unlike traditional bits, those electrons and qubits are spinning in all directions at once. There is another strange phenomenon, nonspecific to quantum mechanics called superposition. There’s a qubit that is spinning in every direction possible within a sphere; mathematically represented by fractions between zero and one that indicate the probability of it being spin up or spin down. We have the options of 00,01,10, and 11, so four different states of the system itself with two classical bits of information. But the qubits also have that superposition of each of those bits as well to work with. We get the equivalent of four classical bits of information to represent each of the possible states. We would need four numbers to know the state of the system versus the two numbers for the classical one. If we added another bit and qubit to these two systems we would get three bits of information for the traditional system but eight for the quantum system. And it’s these exponentially more bits of information that continue to the tune of 2n, where n is the number of qubits. Pretty quickly how much more power can be gained from this system is exponential. There is another quantum property that needs to occur for this to hold, and that is quantum entanglement [7]. Which is a phenomenon that two quantum entangled particles will always display the opposite of another one instantly no matter how far apart they are. For example, if we have two quantum entangled electrons and we look to see what direction one is spinning.

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Let’s say it’s spin up then the other is automatically going to be pinned down and vice versa. This will be true every single time, as long as those two qubits are entangled. Einstein called this spooky action at a distance and just like the other quantum properties. Why it happens, we also don’t need to we just know that it’s proven and that happens all the time. Early sailors did not know about the particles in the air that was moving the sales of their ships; they just knew that they could use the wind to move their sailboats. Finally, for these spooky things, the fact that quantum particles’ state is changed any active observing them so that superposition is happening. But as soon as we observe the state of the qubit it will either be up or down, we cannot observe or measure the superposition state itself. So while the computer is running superposition is a state we can use but as soon as we go to read the results millions of zeros and ones. Come up with a usable state with unique requirements and probability factor that quantum computers won’t always be faster than traditional computers in some cases. In some cases actually will be a lot slower; instead, they can solve problems with an enormous number of variables way faster than a traditional computer can. The more potential outcomes and steps in the problem the more efficient the quantum computer will be over.

2.1 Impact of Quantum Computing on Social Media Platforms Quantum computing’s potential to disrupt existing cryptographic methods poses a direct threat to social media platforms, which rely on encryption to secure user data and communications [8]. As social media networks store vast amounts of sensitive information, including personal identities, financial data, and private messages, a quantum attack could lead to massive breaches of user privacy. To prepare for this eventuality, social media companies must explore quantum-resistant algorithms to safeguard their systems against future threats, abstract model is shown in Fig. 1. Additionally, quantum computing could enhance the analytical capabilities of social media platforms, enabling more accurate sentiment analysis, behavioral predictions, and targeted content delivery [9]. However, these advancements must be balanced against the ethical considerations of user data privacy and the potential for misuse.

2.2 Quantum Artificial Intelligence The quantum artificial intelligence founding father of this new field is Seth White of MIT who invented the field a couple of years ago. But he wasn’t the first person in the field. It’s a subfield of quantum computing. A Quantum computer it’s essentially a fridge inside a fridge and an extremely cold chip. At the very core of it, this small thing

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Fig. 1 The diagram showing the impact of quantum computing on encryption, focusing on how traditional encryption methods can be compromised by quantum attacks

in the middle is where all the magic happens. One of the greatest accomplishments mankind has ever seen so who came up with this? Some people envisioned the concept of a quantum computer as a master of self-Richard Feynman until another genius came along and came up with the first algorithm that was impactful [10]. With one computer right now one would be able to break almost any RSA security on the Internet. Being able to steal people’s data would be able to listen to all kinds of communication right. That must be the reason why governments are so interested. We will have small quantum computers that will do interesting things very soon. This may sound scary but most applications of quantum computers are positive. One of the most promising early applications of quantum computer quantum simulation. Instead of going through an extremely slow frustrating and tedious process of child error in developing new drugs. We could use quantum computers that could simulate all possible drives at the same time and pick out the right quarter. Given the seats and other applications of quantum computers and quantum cybersecurity. Quantum computers cannot just break classical RSA encryption using shores algorithm. They also provide a cure to the disease and secure communication using quantum encrypted data. Social security is theoretically and practically impossible to break even with a quantum computer. The only way to break quantum encryption will be by breaking the laws of physics. The high-quantum computer works to understand this. People have to dive into another dimension, a dimension that is so small. There are mind completely fails to comprehend the world of quantum mechanics. Even Albert Einstein initially refused to believe it because this is so counterintuitive and hard to understand. A classical computer operates in bits which can be either zeros or ones and performs calculations with gates such as and or not gates. Every program on a classical computer as simple as a calculator or as advanced as a neural network seems very simple. The quantum computer, on the other hand, operates very differently instead of using classical bits that can only be zeros and ones. It uses quantum bits or qubits that can be any linear superposition between zero and one. Only once we measure, does it collapses into a classical state of zero and one certain probability. This behavior is random, there is no way to predict what is going to happen to a certain quantum program. Just as the classical program consists of a collection of logical gates quantum logical gates in

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this case. But instead of just having simple logical gates such as and or operations are disposal quantum logical gates can be much more powerful much more advanced. This result might contain the solution to all of our universe’s problems. But unfortunately, if we measure it, it is going to collapse. We will only get one classical result of zeros and ones, and it is extremely unlikely. This result is going to be the one that we were looking for the real difficulty, in developing a quantum algorithm. Finding the right combination of gates that are going to maximize the probability that we’re going to measure the right. Which is the solution to a given problem as opposed to one of the exponentially many garbage results.

2.3 Quantum Machine Learning The classical computation works with strings of ones and zeros; this is called binary code these bits are usually encoded in electrical pulses and everything that is on here and everything that a computer does can eventually be reduced down to these bits. The fundamental principles of quantum mechanics superposition. Superposition states that we can add quantum states together and break them apart into distinct other quantum states. The second part is particularly useful for quantum computing because it means that a quantum bit or a qubit can be broken down into multiple quantum states. That is it can be both one and zero or neither at any given time. This property lets us restore and process information much faster than a bit, in conjunction with another quantum property called entanglement. This lets us connect two qubits such that we can alter one and it will predictably change the other. Humans can be used to solve mathematical problems or train machine learning algorithms that might otherwise take hundreds or thousands of years using classical computing in seconds. Demonstrating this is possible supremacy and Google did it in 2019 and once the problem is solved in our weather is trained in quantum computing. Lets us turn qubits back into bits and vice versa by periodically observing the qubits to see what the output is. Now we are not going to be achieving quantum supremacy [11]. Most of us don’t have access to quantum computers, they are very hard to create. Because humans lose their quantum properties, and we observe them repeatedly due to something called decoherence. This can happen mid-training, which will result in errors, and whatever we are trying to compute, coherence can be mitigated. By keeping it extremely cold or by protecting them from vibrations. But researchers are still working on ways to create quantum computers that can stay quantum for long periods, in particular. Consumer quantum computing would be very difficult to achieve because it is not feasible for us to keep our laptops or phones in cold storage with no vibrations. However, the expansion of quantum computing is particularly interesting for the field of machine learning. It would allow us to train larger and more complex algorithms than we ever have before. This might approach something like artificial general intelligence. Although it is a bit hard to say because we do not fully understand human intelligence. So there’s no clear benchmark for when we get there.

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This is cool because it opens up some possibilities for how we can connect quantum computing devices with machine learning. One perspective is to use quantum devices for machine learning so we use a quantum device to make a classifier. The other way we can think of it is actually to use machine learning tools and differentiable programming tools to better understand quantum systems and computers. So this is what we call machine learning from a quantum perspective. They are using the tools of machine learning and applying them to quantum computing circuits [12].

2.4 Quantum Deep Learning Build machine learning models out of composable differentiable functions. That’s a very high-level summary of what deep learning means. In particular, those composable differentiable functions are originally neural networks, and the network has the following structure. It’s a function F which takes an input X as free parameters data. Take that input X and multiply it by one subset of parameters, which is called weight matrix W. Add another subset of those from its called bias vector, and then take the result of that and put it through what is called activation function Sigma. This is a differentiable transform, and it is also compostable. That is how we get multiplayer neural networks. To train a deep learning model to define a cost function which is the single scalar value. That should be optimized and typically what wants the cost function to do something like comparing the output of a model function. When given an input with the expected answer to minimize the difference between those two things. Or there are many different types of the cost function in gradient descent. The idea here is to compute the gradients of that cost function concerning each parameter. The gradient is a vector of partial derivatives and it points in the direction of the steepest descent in parameter space. Iteratively update parameters by moving them along that gradient. Eventually, it should converge to a minimum, and then the model could be used for classification or prediction. Its use has been tremendously successful over the past decade. There are three main reasons behind that. • First is hardware advancements. GPUs have been very key and deep learning requires a lot of computing to train the most powerful models. • There is also a workhorse algorithms backpropagation algorithm that is used to compute the gradients, and finally, • One thing that has enabled that field to take off is the ability for users to rapidly experiment with prototypes and try different ideas using very user-friendly software. There’s something deeper than deep learning it’s called differentiable programming this is something that deep learning falls under. But actually, we can think of it more broadly so the basic idea behind differential programming is the following any code should be trainable not just machine learning models [13].

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It should be differentiable, for instance, in python anyone to find cost functions can look very similar to a deep learning cost function. Where compare the desired results to the results achieved with a particular set of parameters. Two of the parameters and again can use gradient descent as well for optimizing this cost function. After optimization, this code should now be very well tuned to specific purposes. Which may go beyond what the machine learning model is meant to do. Which is used to make predictions or to fetch functions. So this particular example has been taken from the python library autograd. Differential program is fluid simulations and they’re optimized to produce a particular configuration [14]. Here we can see that the differential program is quite broad. Just a few here are the neural network-type model that has access to external memory. There are neural ordinary differential equations where we’re not optimizing under a neural network we’re optimizing a solver to a differential equation. There is very friendly software for differentiable programming, these are not just only for ML but extending for other things there’s covering. Python but also Julia libraries are very nice as these libraries do for us, is something called automatic differentiation. These handle all the optimization parts for us. So we can build our computation, we can build our program, and then these take care of training for us.

3 Quantum Deep Learning for Cybersecurity Traditional cybersecurity systems rely on classical machine learning models that, while effective, can be limited by the complexity and volume of modern data.

3.1 Quantum-Enhanced Threat Detection Quantum deep learning (QDL) can significantly enhance threat detection by leveraging quantum algorithms to process large datasets at unprecedented speeds. QDL models, such as Quantum Convolutional Neural Networks (QCNNs), can analyze patterns in network traffic, user behavior, and other data sources to identify subtle indicators of potential threats that might be missed by classical systems. The ability to evaluate vast amounts of data in parallel allows for real-time threat detection, reducing the time between detection and response.

3.2 Quantum Cryptography Integration Quantum cryptography, particularly Quantum Key Distribution (QKD), offers theoretically unbreakable encryption by leveraging the principles of quantum mechanics. Integrating QKD with quantum deep learning models ensures that data transmitted

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within the system remains secure from even the most sophisticated cyberattacks. This approach not only enhances data security but also enables secure communications between different components of a cybersecurity system, such as threat detection algorithms, databases, and response mechanisms. By combining quantum cryptography with quantum-enhanced deep learning models, a robust, multi-layered security framework can be established [15].

3.3 Attack Surface Reduction Identifying and mitigating vulnerabilities in complex systems is a crucial aspect of cybersecurity. Quantum algorithms, such as Grover’s search algorithm, can be applied to analyze vast and intricate systems more efficiently than classical algorithms. When integrated with QDL, these algorithms can systematically scan for vulnerabilities, identify potential weak points, and suggest mitigations. For instance, quantum-enhanced models can simulate potential attack scenarios in virtual environments, helping security professionals understand how an adversary might exploit vulnerabilities and preemptively strengthen defenses [16]. This diagram should depict the flow of data from its collection (e.g., network logs, user activity) through quantum preprocessing and into the QDL model, ultimately resulting in threat detection output. The integration of quantum cryptography at each communication point should be illustrated, along with how quantum algorithms expedite the identification of system vulnerabilities.

4 Quantum Deep Learning for Social Media Analysis Social media platforms generate enormous volumes of unstructured data, including text, images, and videos, which must be processed to extract meaningful insights. QDL can dramatically improve sentiment analysis and trend detection by leveraging quantum-enhanced natural language processing (NLP) models [17]. These models can process and analyze text data from social media posts more efficiently, detecting subtle shifts in sentiment and emerging trends across large datasets.

4.1 Sentiment Analysis and Trend Detection Quantum algorithms can explore a more extensive set of possible feature combinations in parallel, providing deeper insights into public opinion and emerging social trends, Fig. 2 describes the working of QDL in SMA.

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Fig. 2 QDL in Social Media Analysis. The diagram should illustrate the process of data ingestion from social media streams, followed by quantum feature extraction and processing through a QDL model. The output, which includes sentiment analysis, trend detection, and anomaly detection, should be clearly depicted, showing how quantum algorithms enhance each stage of analysis

4.2 Anomaly Detection Anomaly detection in social media involves identifying unusual patterns, such as the spread of misinformation, coordinated inauthentic behavior, or unusual spikes in activity [18]. Traditional machine learning models often struggle with the sheer scale and complexity of social media data. QDL models can offer a significant improvement by efficiently processing this data to identify anomalies in real-time. Quantumenhanced anomaly detection algorithms can analyze interactions between users, content dissemination patterns, and network structures to detect coordinated efforts to manipulate public opinion or spread harmful content, enabling faster intervention.

4.3 User Behavior Prediction Predicting user behavior on social media is crucial for applications ranging from content recommendation to targeted marketing. QDL models can analyze historical user data, including browsing patterns, likes, shares, and comments, to predict future behavior with greater accuracy. The parallel processing capabilities of quantum computing allow these models to consider a broader range of variables and interactions, resulting in more personalized and effective predictions. For example, quantum-enhanced recommendation systems can analyze vast datasets in real-time, suggesting content that is more likely to resonate with individual users, thereby improving user engagement.

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5 Implementation Strategy Implementing QDL in real-world applications requires specialized quantum computing hardware, such as quantum processors (e.g., D-Wave, IBM Quantum) and quantum simulators. While fully functional, large-scale quantum computers are still in development, hybrid quantum–classical systems can be utilized to bridge the gap.

5.1 Hardware Requirements These systems combine classical computing resources with quantum processors, allowing for the implementation of quantum-enhanced algorithms on a smaller scale. It is essential to consider the current limitations of quantum hardware, including qubit coherence time and error rates, and design systems that can operate effectively within these constraints.

5.2 Software Tools To develop QDL models, several quantum machine learning libraries and frameworks are available. Tools like TensorFlow Quantum, PennyLane, and Qiskit offer interfaces for building and training quantum-enhanced deep learning models. These tools allow researchers and developers to experiment with different quantum algorithms, integrate them with classical deep learning frameworks, and deploy them in practical applications. Additionally, these software tools support the simulation of quantum circuits on classical hardware, enabling the testing and optimization of quantum algorithms before deployment on actual quantum processors [19]. Scalability and Feasibility: While the potential of QDL is significant, the current state of quantum computing poses challenges for scalability. Many quantum algorithms are still in the experimental stage, and the hardware required for large-scale implementation is limited. However, hybrid approaches that combine quantum and classical methods can offer immediate benefits while we wait for more advanced quantum hardware. These hybrid systems can scale better by offloading specific tasks to quantum processors while relying on classical systems for more routine operations. It is also crucial to consider the evolution of quantum hardware and software, preparing for future developments that could enable full-scale quantum deep learning applications.

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5.3 Cybersecurity Case Study A detailed case study could explore the application of QDL in detecting a sophisticated cyberattack, such as a zero-day exploit. The case study would demonstrate how QDL models, integrated with quantum cryptography, can identify the exploit faster and more accurately than traditional methods, reducing the response time and mitigating the damage. The study would also highlight the practical challenges encountered during implementation, such as hardware limitations, and how they were overcome using hybrid quantum–classical approaches.

5.4 Social Media Case Study Another case study might focus on the application of QDL to a real-world social media dataset, such as analyzing tweets during a major event. The study would illustrate how QDL models outperform classical models in detecting emerging trends, identifying sentiment shifts, and spotting coordinated disinformation campaigns. By comparing the results with those obtained from classical deep learning models, the case study would underscore the advantages of quantum-enhanced analysis, such as faster processing times and more nuanced insights.

6 Conclusion As quantum computing moves closer to practical implementation, its implications for cybersecurity and social media become increasingly critical. The ability of quantum computers to break widely-used encryption schemes poses a significant threat to the integrity of digital communications, including those on social media platforms. To mitigate these risks, the adoption of quantum-resistant encryption methods is essential. At the same time, quantum computing presents opportunities for enhancing social media through advanced data processing capabilities, enabling more efficient content analysis, targeted advertising, and user engagement strategies. As we stand on the brink of this technological revolution, it is imperative that stakeholders in cybersecurity and social media remain vigilant, adapting to the evolving landscape to protect and optimize digital experiences.

References 1. Riedel, M. F., Binosi, D., Thew, R., and Calarco, T., “The European quantum technologies flagship program,” Quantum Sci. Technol., vol. 2, no. 3, 2017.

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2. Ul Ain, N., “A novel approach for secure multi-party secret sharing scheme via quantum cryptography,” in 2017 International Conference on Communication, Computing and Digital Systems (C-CODE), pp. 112–116, 2017. 3. S. White, “Quantum Artificial Intelligence: Harnessing the power of quantum computing for AI,” MIT, 2019. 4. Seth, L. H., “Machine learning and quantum computing integration,” Quantum Technologies Journal, 2019. 5. Ji, Z., Zhang, H., and Wang, H., “Quantum private comparison protocols with several multiparticle entangled states,” IEEE Access, vol. 7, pp. 44613–44621, 2019. 6. Djordjevic, I. B., “Cluster states-based quantum networks,” in IEEE Photonics Conference (IPC), vol. 2020, pp. 1–2, 2020. 7. J. Baloo, “Cybersecurity in the quantum computing era,” The Future of Quantum Computing, vol. 1, pp. 19–32, 2020. 8. A. Turing, “On computable numbers and the foundations of quantum computing,” Journal of AI Research, vol. 56, no. 4, pp. 10–25, 2021. 9. NTT Research, “Building the world’s fastest Ising machine,” Physics and Informatics Lab, 2021.J. L. Heisenberg, “Quantum tunneling and cybersecurity vulnerabilities,” Journal of Cyber Engineering, vol. 23, no. 2, pp. 56–78, 2022. 10. R. Feynman, “Quantum mechanics and computing advancements,” Physics Reports, vol. 75, no. 6, pp. 12–28, 2022. 11. J. Baloo, “Quantum computing will lead to new risks for cyber security,” World Economic Forum, 2022. 12. Alqahtani, S.I.; Yafooz, W.M.S.; Alsaeedi, A.; Syed, L.; Alluhaibi, R. Children’s Safety on YouTube: A Systematic Review. Appl. Sci. 2023, 13, 4044. https://doi.org/10.3390/app130 64044. 13. Csenkey, K., and Bindel, N., “Post-quantum cryptographic assemblages and the governance of the quantum threat,” Journal of Cybersecurity, vol. 9, no. 1, tyad001, 2023. 14. Lakshmi, D., Nagpal, N., and Chandrasekaran, S., “A quantum-based approach for offensive security against cyber attacks in electrical infrastructure,” Applied Soft Computing, vol. 136, p. 110071, 2023. 15. Yafooz, W. M., Al-Dhaqm, A., & Alsaeedi, A. (2023). Detecting kids cyberbullying using transfer learning approach: Transformer fine-tuning models. In Kids Cybersecurity Using Computational Intelligence Techniques (pp. 255–267). Cham: Springer International Publishing. 16. Dwivedi, A., Saini, G. K., Musa, U. I., and Kunal, “Cybersecurity and prevention in the quantum era,” in 2nd International Conference for Innovation in Technology (INOCON), Bangalore, 2023. 17. Ford, P., “The quantum cybersecurity threat may arrive sooner than you think,” Computer, vol. 56, no. 2, pp. 134–136, 2023. 18. Abdel-Wahab, A., Emara, A., Shah, S.A., Algeelani, N. and Al-Sammarraie, N., “Street-crimes modeled arms recognition technique employing deep learning and quantum deep learning,” Indones J Electric Eng Comput Sci, vol. 30, pp. 528–544, 2023. 19. Hadi, H. J., Cao, Y., Alshara, M. A., Ahmad, N., and Riaz, M. S., “Quantum cryptography on IBM QX,” in 2nd International Conference on Computer Applications & Information Security, pp. 1–6, 2019.

Factors Influencing on the Cybersecurity in an Emerging Economy Hong Thi Nguyen

Abstract Social networks play an important role in the socio-economic life of countries, including Vietnam. The objective of the study is to assess factors affecting cybersecurity in Vietnam. Based on the results of a survey of 160 young people using social networks and analysis using SPSS software, the study results show that government policies and community awareness have a positive impact on cybersecurity. However, there is no relationship between cybersecurity enforcement and monitoring, habits, and culture when using social networks on cybersecurity. On the contrary, the study also affirms that the living environment has a positive impact on cybersecurity. Keywords Cybersecurity · Social network · Government · Young generation

1 Introduction The fourth industrial revolution is taking place rapidly on a global scale, bringing many positive effects on social life to meet the growing needs of consumers and society. The fourth industrial revolution helps economies operate more optimally due to the ability to connect economies through applications and promote digital transformation, reducing transaction costs in the economy and improving operational efficiency in the economy. The fourth industrial revolution is associated with the increased application of technology in human life, bringing consumers new experiences of technology, increasing connectivity and new economic activities. The process of technological development is closely linked to the development of social networks as a channel to connect information between people, businesses and businesses, countries and countries. Moreover, social networks not only have the task of connecting information but also have the ability to increase interactions and are becoming an indispensable activity in daily life. To develop social networks, the role H. T. Nguyen (B) Thu Dau Mot University, Thu Dau Mot City, Binh Duong, Vietnam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_10

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of the Internet is indispensable in creating an online platform that easily connects everyone anywhere in the world through computers and smartphones. Popular social networks include Youtube, Facebook, Zalo or Instagram. Although social networks have become an important means of connecting information, economic, business and commercial activities on a global scale. However, social network users may also face risks related to the use of technology and the ability to use social networks. Indeed, cybercrime involves the use of cyberspace, information technology and electronic means to commit crimes. According to [1], the growth of the internet and online applications makes people increasingly dependent on technology while increasing the risk of cyber attacks and information theft. Therefore, it can be seen that cyberspace is currently facing many challenges related to security and crimes on the internet platform, which requires countries, including Vietnam, to always have solutions to improve their capacity to prevent cybercrime to ensure a clean cyber security environment. Furthermore, Vietnam is a country with a high level of internet usage and a young generation that regularly participates in social networks, so they have the ability to update information and increase interactions with the outside world. Therefore, the objective of the study is to clarify the factors affecting network security in Vietnam to provide solutions to help the Vietnamese government improve its governance capacity for social networks.

2 Literature Review The fourth industrial revolution has accelerated the development of online application platforms, including social networks. The emergence of social networks helps connect people with people, businesses with businesses, countries with countries with low connection costs. It can be said that social networks have brought many positive benefits to meet the needs of information exchange, interaction and economic development requirements. Arroyabe et al. [2] argue that the current business landscape affirms the important role of cybersecurity in business operations against attacks that can cause damage to businesses due to information theft and other threats. Indeed, businesses are increasingly dependent on technology for their operations, and cybersecurity has become an aspect that requires businesses to pay attention to in order to protect their digital assets and at the same time ensure business continuity and maintain customer trust. Arroyabe et al. [2] argue that information asymmetry in cybersecurity can lead to suboptimal cybersecurity implementation while cybersecurity incidents and the impact of cybersecurity in small and medium-sized enterprises do not promote cybersecurity implementation. Furthermore, while focusing on control systems can make a significant difference between small and medium-sized enterprises and large enterprises, having the ability to shape cybersecurity can provide value and insight to management on cybersecurity operations. Another study, [3] suggests that behavior and attitudes have the potential to influence cybersecurity behavior. Organizations that implement cybersecurity awareness have the potential to

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positively change employee attitudes and behaviors by increasing employee knowledge. Chaudhary [3] also suggested that the attributes that can influence positive changes in employees’ cybersecurity behavior could be when they receive support from senior management for their involvement in cybersecurity activities, the ability to cultivate and propagate cybersecurity as a norm within the organization, and especially the mechanism to encourage cybersecurity activities and behaviors through incentives and effectiveness in spreading messages. The increasing digitalization process has appeared in all areas of society, transportation, energy, telecommunications, health, finance or space. Chiara [4] believes that the enforcement of the basic right to cybersecurity is a solution to regulate people’s use of safe network platforms. In addition, each country must always ensure basic security rights, including cybersecurity, to protect human safety and development from possible risks when participating on social networking platforms. The development of social networks has brought certain benefits to consumers, but at the same time, there have also been shortcomings and risks related to privacy and online safety, so the enforcement of cyber security rights is necessary to help regulate activities on cyberspace to develop stably and safely. Toussaint et al. [5] also emphasize the role of the fourth industrial revolution leading to the digital transformation of the industrial world. The digital transformation process includes the widespread application of the Internet of Things, communication technology, and industrial standards that support automation and data exchange in manufacturing processes. Industrial development driven by technological advances has blurred the boundaries between the physical and digital worlds to create systems that connect machines, products, and interactions, thereby optimizing processes and making decisions faster. However, issues related to information security are becoming a major challenge in the fourth industrial revolution, such as data manipulation, which is a threat to organizations and causes significant consequences. Therefore, applying a cybersecurity framework is necessary to protect organizations from threats to minimize cyber risks. Another study, [6] also assert that data protection and data integrity are crucial in the changing world of cybersecurity, especially increasing cybersecurity awareness and training to shed light on the effectiveness in reducing costs and network resilience in the context of possible cybersecurity risks. These measures enable businesses to significantly reduce security incidents and reduce risk-related costs, which means improving business performance in the digital environment. Therefore, [6] emphasize the role of cybersecurity education and awareness that can help businesses and individuals understand cybersecurity and have appropriate plans to respond to cybersecurity-related risks. Okey et al. [7] argue that the pace of change for AI adoption has increased significantly in recent times and is driving the development of more intelligent systems in the future. AI has been applied to almost every aspect of human life including industrial activities, healthcare, education, military, and cybersecurity. At the same time, mobile internet and mobile devices are now available to users in a wide variety and thus combined with AI technology, the applications are expanded to many utilities. However, concerns about cybersecurity still exist, especially malware, cyber attacks, intelligence gathering, and fraud taking place in the online environment that affect

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information security and cybersecurity. Meanwhile, [8] also agrees that cybersecurity enhancement is an effective security awareness program, in which the assessment of cybersecurity awareness is associated with three essential aspects: password security, browser security, and social media. Specifically, password security has a significant impact on cybersecurity awareness with a regression coefficient of 0.147. Similarly, the browser security variable has a significant impact on awareness with a regression coefficient of 0.188, and finally the social media activity variable has the greatest impact on cybersecurity awareness with a regression coefficient of 0.241. Hong et al. [9] study the latent factors influencing internet security perceptions through an extended knowledge-attitude-behavior model with the assumption that the educational level of the whole society plays a moderating role in the relationship between knowledge and attitudes. In the case of China, the study found a significant negative moderating effect of work exposure, and this effect was more widespread than expected. Social influence has the potential to reshape not only the cybersecurity attitudes of highly educated people but also their knowledge and behavior. In a similar study, [10] suggest that the digital age requires cybersecurity awareness to become an urgent concern, thus enhancing digital security in educational environments helps to align online activities with strong security measures and foster a culture of cybersecurity awareness, the results show that raising awareness among the community about cybersecurity has the potential to improve security and safety practices when using online platforms.

3 Data and Methodology 3.1 Data Collection We plan to collect primary data through a Google Form survey of Vietnamese students to better understand the factors affecting cyber security for students. In particular, students are the ones who regularly use social networks, so the results of this study can better understand the factors affecting cyber security for students, which is a basis for the Vietnamese government to have appropriate solutions to improve safety in the online environment. According to research, the optimal sample size must be no less than 5 times the number of survey questions. In this study, we plan to survey through 20 questions, so the smallest sample size should be 100. To be safer, we plan to survey about 160 students.

3.2 Methodology The study uses quantitative analysis through SPSS software to clarify the factors affecting network security in Vietnam. The study conducts descriptive statistical

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analysis, factor analysis and regression. The research results are the basis for analysis and interpretation of the results. The regression equation is presented as follows: CYBER = β0 + β1 POLICY + β2 AWARENESS + β3 IMPLE + β4 ENVI + β5 CULTURE + µ where, CYBER

is a variable representing cybersecurity, a factor reflecting the level of network security, this is the dependent variable, POLICY is a variable representing government policies related to network security, is an independent variable, AWARENESS is a variable representing the level of awareness of social network users towards network security, is an independent variable, IMPLE is a variable representing the process of implementing and monitoring activities related to network security, is an independent variable, ENVI is a variable representing the living environment, which affects network security, is an independent variable, CULTURE is a variable representing habits and culture when using social networks, is an independent variable.

4 Research Results 4.1 The Test of the Cronbach’s Alpha According to the theory of reliability analysis, variables satisfy reliability when the Cronbach’s alpha coefficient is greater than 0.6 and the total item correlation coefficient is greater than 0.3. The results of Table 1 show that all variables satisfy the above conditions, or it can be said that the variables satisfy the reliability level and are therefore suitable for factor analysis. EFA analysis must satisfy the KMO coefficient above 0.5 to confirm the suitability of factor analysis, and Barlett’s test with Sig value less than 5% indicates that the observed variables are correlated with each other. Table 2 shows that the KMO coefficient = 0.854 and Barlett’s test of sphericity has Sig = 0.000 < 5%, so the EFA tests meet the requirements.

4.2 Correlation Matrix Correlation analysis aims to assess the degree of correlation between variables, especially independent variables. When the independent variables have a low correlation,

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Table 1 The test of the Cronbach’s alpha Variable

Corrected item-total correlation

Cronbach’ alpha if item deleted

CYBER–Cronbach’s alpha = 0.796 CYBER1

0.600

0.732

CYBER3

0.592

0.754

CYBER2

0.569

0.744

CYBER4

0.612

0.721

POLICY–Cronbach’s alpha = 0.803 POLICY1

0.656

0.810

POLICY3

0.638

0.798

POLICY2

0.669

0.776

AWARENESS–Cronbach’s alpha = 0.812 AWARENESS1

0.662

0.776

AWARENESS2

0.612

0.754

AWARENESS4

0.649

0.741

AWARENESS3

0.672

0.750

IMPLE–Cronbach’s alpha = 0.832 IMPLE1

0.652

0.818

IMPLE3

0.646

0.808

IMPLE2

0.628

0.802

IMPLE4

0.662

0.786

ENVI–Cronbach’s alpha = 0.861 ENVI2

0.654

0.816

ENVI1

0.626

0.821

ENVI3

0.618

0.798

CULTURE–Cronbach’s alpha = 0.832 CULTURE2

0.641

0.824

CULTURE1

0.649

0.810

CULTURE4

0.658

0.792

CULTURE3

0.683

0.776

Table 2 KMO and Barlett’s test of sphericity

Kaiser–Meyer–Olkin measure for sampling adequacy

0.854

Barlett’s test of sphericity

Approx Chi square

1463.632

Sig

0.000

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Table 3 Correlation matrix Variable

CYBER

CYBER

1.000

POLICY

AWARENESS

IMPLE

ENVI

POLICY

0.462

1.000

AWARENESS

0.238

0.175

1.000

IMPLE

0.531

0.358

0.193

1.000

ENVI

0.421

0.462

0.015

0.168

1.000

CULTURE

0.226

0.317

0.219

0.641

0.371

CULTURE

1.000

it is possible to eliminate the phenomenon of multicollinearity. Table 3 shows that the correlation coefficients are all less than 0.8 and therefore can predict that there is no possibility of multicollinearity, so the results of regression analysis are guaranteed.

4.3 Regressin Results Table 4 shows that: The POLICY coefficient is 0.289, which is positive and statistically significant, meaning that there is an impact of government policy on cybersecurity. Therefore, when the government has policies that can regulate social media behavior, it can help to improve the health of social media activities, and at the same time, the policies can prevent fraudulent behavior or cyber attacks. Indeed, [6] argues that data protection and maintaining data integrity are very important in the changing world of cybersecurity, so businesses always implement solutions to prevent security incidents and reduce risks that affect cybersecurity. Sharing the same view, [4] argues that implementing policies related to cybersecurity helps people to use the network platform safely and minimize the shortcomings and risks related to privacy and safety in cyberspace. Table 4 Regression results Variable

Unstandardized coefficients B

Std. error

_cons

1.778

0.669

POLICY

0.258

0.089

AWARENESS

0.311

0.093

IMPLE

0.168

ENVI CULTURE

Standardized coefficients

t

Sig.

Collinearity statistics Tolerance

VIF

Variable 2.657

0.000

0.289

2.894

0.000

0.986

1.0142

0.386

3.359

0.000

0.953

1.0493

0.208

0.198

0.808

0.658

0.889

1.1249

0.219

0.079

0.341

2.765

0.000

0.931

1.0741

0.216

0.207

0.247

1.046

0.215

0.914

1.0941

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H. T. Nguyen

The AWARENESS coefficient is 0.386, which is positive and statistically significant, meaning that there is an impact of community awareness on cybersecurity. That is, increasing community awareness of cybersecurity can help the community increase safety and efficiency on the network platform. This result is supported by [6] who said that cybersecurity education and awareness help businesses and individuals to be more knowledgeable about cybersecurity and thereby have a plan to respond to risks related to cybersecurity. The IMPLE coefficient is 0.198, which is positive but statistically insignificant, implying that cybersecurity enforcement and monitoring have not had an impact on cybersecurity. Similarly, the CULTURE coefficient is 0.247, which is positive and statistically significant, meaning that there is no impact of habits and culture when using social networks on cybersecurity. However, the ENVI coefficient is 0.341, which is positive and statistically significant, meaning that there is an impact of the living environment on network security. This research result confirms the role of the living environment in positively influencing network security. Indeed, when the community has close and close relationships with each other, there is a spread of positive values in the community and thereby improves the effective participation of the community in the network environment.

5 Conclusion The rapid development of science and technology has increased human activities, especially the ability to connect through social networks. It can be said that the emergence of social networks has made the connection and interaction between people, businesses and businesses, countries and countries become more transparent at low cost, so social networks have promoted faster interaction, trade and economic activities. However, social networks also have some negative aspects, especially the issue of information security, confidentiality, hackers can harm the interests of users, businesses and the country. Researching the factors affecting network security in Vietnam through a survey of 160 young people using social networks and quantitative analysis using SPSS software, the research results show that there is a positive impact of government policies and community awareness on network security. However, there is no relationship between cybersecurity enforcement and monitoring, habits, and culture when using social networks on cybersecurity. The study also confirms the positive impact of living environment on cybersecurity. From the above research results, there are some solutions for Vietnam to improve the effective operation of social networks. Firstly, Vietnam continues to improve policies related to network security, thereby helping to ensure network security, creating a healthy and effective network environment for the community, businesses and the country. Secondly, Vietnam needs to raise awareness of people and communities about network security, at the same time raising awareness of risks and hackers for the community when participating in interactions on the network environment.

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References 1. LSVN (2023). The development of technology and the current increase in cybercrime. Available at https://lsvn.vn/su-pha-t-trie-n-cu-a-cong-nghe-va-ti-nh-tra-ng-gia-tang-to-i-pham-ma-ng-hie-n-nay-1699026045.html, accessed on 1st Aug, 2024. 2. Arroyabe, M. F., Arranz, C. F. A., Fernandez De Arroyabe, I., & Fernandez de Arroyabe, J. C. (2024). Exploring the economic role of cybersecurity in SMEs: A case study of the UK. Technology in Society, 78, 102670. https://doi.org/10.1016/j.techsoc.2024.102670. 3. Chaudhary, S. (2024). Driving behaviour change with cybersecurity awareness. Computers & Security, 142, 103858. https://doi.org/10.1016/j.cose.2024.103858. 4. Chiara, P. G. (2024). Towards a right to cybersecurity in EU law? The challenges ahead. Computer Law & Security Review, 53, 105961. https://doi.org/10.1016/j.clsr.2024.105961. 5. Toussaint, M., Krima, S., & Panetto, H. (2024). Industry 4.0 data security: A cybersecurity frameworks review. Journal of Industrial Information Integration, 39, 100604. https://doi.org/ 10.1016/j.jii.2024.100604. 6. Taherdoost, H. (2024). A Critical Review on Cybersecurity Awareness Frameworks and Training Models. Procedia Computer Science, 235, 1649–1663. https://doi.org/10.1016/j. procs.2024.04.156. 7. Okey, O. D., Udo, E. U., Rosa, R. L., Rodríguez, D. Z., & Kleinschmidt, J. H. (2023). Investigating ChatGPT and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security, 135, 103476. https://doi.org/10.1016/j.cose.2023.103476. 8. Alqahtani, M. A. (2022). Factors Affecting Cybersecurity Awareness among University Students. In Applied Sciences (Vol. 12, Issue 5). https://doi.org/10.3390/app12052589. 9. Hong, W. C. H., Chi, C., Liu, J., Zhang, Y., Lei, V. N.-L., & Xu, X. (2023). The influence of social education level on cybersecurity awareness and behaviour: a comparative study of university students and working graduates. Education and Information Technologies, 28(1), 439–470. https://doi.org/10.1007/s10639-022-11121-5. 10. Bognár, L., & Bottyán, L. (2024). Evaluating Online Security Behavior: Development and Validation of a Personal Cybersecurity Awareness Scale for University Students. In Education Sciences (Vol. 14, Issue 6). https://doi.org/10.3390/educsci14060588.

Innovative Security Measures: A Comprehensive Framework for Safeguarding the Internet of Things Zeyad Ghaleb Al-Mekhlafi

and Sarah Abdulrahman Alfhaid

Abstract Intrusion detectors (IDS) are better tools in the ever-changing field of IoT security because they protect networks from criminal activity. Machine-learningbased IDS systems use algorithms to automatically identify subtle patterns of activity in network traffic data and adapt to them. Compared to traditional technologies, these systems can detect potential anomalies and intrusions more accurately and efficiently by analyzing massive amounts of data. Despite its power, intrusion detection organizations aiming for machine learning must overcome obstacles such as the lack of labeled training data, the need for scalability and resilience against adversarial attacks, and the ability to interpret model results. Join research projects involving machine learning and data privacy security to address these issues. Advanced feature engineering techniques: investigation of deep learning architectures, adaptability to adversarial tactics, online learning and adaptation, multi-modality data, privacypreserving strategies, transparent AI evaluation and evaluation applications, and everyday life deployment and validation are some of the trends. Futures in Machine Learning: Intrusion Detection Systems (IDS). The study evaluates three machine learning systems for intrusion detection in network security: decision trees, KNearest Neighbor (KNN), and logistic regression. The decision tree model has the highest training and testing scores, with a with a training score of 99.98% and a test score of 99.47%. While logistic regression obtained a training score of 92.88%, Test result: 92.31%. and KNN, train score: 98.73%, test score: 98.31%. There is potential to improve IDS’s ability to identify smaller cyber threats by adapting to machine learning. Machine identifiers provide a major boost to the development of a defense that protects critical data and digital infrastructure from attacks through use and learning. Keywords Internet of Things (IoT) · Intrusion Detectors (IDS) · Machine learning · Cybersecurity · Security · Network Z. G. Al-Mekhlafi (B) · S. A. Alfhaid College of Computer Science and Engineering, University of Háil, Háil, Saudi Arabia e-mail: [email protected] S. A. Alfhaid e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_11

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1 Introduction One of the most exciting areas of information technology study today is cyberthreat protection, while the ever-increasing quantity of tiny, networked gadgets that may upload personal information to the Internet only serves to intensify the conflict between the many parties involved. Therefore, this shield comes alive with a standard Internet-of-Things (IoT) configuration, which often consists of many IoT-based data sources interacting with the real world across multiple application domains, including vital industrial operations, home automation, health care, agriculture, etc. Regrettably, millions of IoT devices are currently in use without any hardware protection, and contemporary IoT devices frequently have extremely poor security capabilities, leaving them vulnerable to constantly evolving and more sophisticated assaults. This also prevents the anticipated worldwide adoption of IoT technology [1]. Current advancements in technology encourage users to implement defenses against damaging intrusions. In order to lower the obstacles related to IT, new capabilities such as dynamic provisioning, tracking, and executives are offered. One of the difficult jobs in DS is that attackers constantly switch up their methods and tools. Many strategies have been put into practice to protect the Internet of Things; however, certain issues are becoming worse, and the outcomes are not entirely clear. This paper claims that the network penetration detection dataset’s anomalous and typical problems were identified and classified using machine learning techniques. Prior to regulation, the data had been preprocessed using a conventional scaler function. The important characteristics in the dataset have been extracted using the random forest approach [2]. The world of linked devices is expanding quickly, thanks to common products’ involvement in networking. In every field, heterogeneous gadgets are part of the Internet of Things (IoT). The majority of IoT devices have constrained resources, and there are no clear protocols or standards for IoT connectivity. It is a very difficult, but important, effort to provide a full security solution for such devices. Now there are many IoT friendly, low-cost security options available. The current strong dangers in the cyber environment cannot be fully protected against by lightweight security methods. Moreover, implementing any conventional security system on IoT devices with limited resources is challenging. Centralized [3]. The management of computer networks is introduced via software-defined networking. Control and knowledge planes are separated by the SDN customizable networking architecture.

2 Literature Review This research evaluated how well different frameworks protect IoT systems from attacks and vulnerabilities. They investigated secure communication protocols, data encryption, access control, and authentication methods. Researchers have also looked

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at the consequences of privacy and design issues and suggested fixes and best practices to improve security. Overall, this research has shed important light on the difficulties and factors that need to be considered when designing an IoT framework.

2.1 IOT in Cybersecurity The Internet has revolutionized communication by bringing people from all over the world together on a single platform to share information. Data is an organization’s most important asset; thus, to protect it from unauthorized entry and cyberattacks like phishing attacks, hacking, surveillance, and the like, every company invests heavily in security solutions like firewalls and antivirus software. Despite the majority of these security measures, attackers continue to be able to get user passwords by taking advantage of web application weaknesses. Researchers suggest using intrusion detection systems to identify harmful activities in networks and prevent cyberattacks. Many machines learning approaches, including K-nearest neighbor, multilayered perceptual tree of decisions, Nave Bayes, and support vector, are discussed in this work [4]. In order to manage the network and examine incoming network traffic, systems for intrusion detection, or IDS, are crucial to network security. Big data, the Web, the Internet of Things (also known as the CC), and the cloud (CC) are examples of contemporary technologies that must be developed due to the massive amount of information and network traffic that exists. Three machine learning techniques that were used with the NSL-KDD data set for the IDS system are compared in this article. Choosing the right characteristics from a huge dataset is necessary to get the best accuracy. Therefore, for selecting key features, the analysis of variance (ANOVA), Ftest, and RFE (recursive feature elimination) were applied. The writers have carried out an IDS experiment using [5].

2.2 Machine Learning Techniques One of the main tasks they take on is determining which properties of the data are important for the models to function well. Consider it like selecting the proper elements to create the ideal flavor in a dish. To choose and order these qualities, they employ three distinct techniques: correlation, information gain, and chi-square. They then utilized these traits by putting them into the random forest and deep feedforward neural network classifiers. It’s similar to putting two distinct types of police officers to the test to discover which is more adept at apprehending burglars. They used three separate datasets, UNSW NB15, CSECIC-IDS018, and To-IoT, which simulate real-world network traffic, to evaluate their architecture. They also took into account versions of those datasets in the Net-Flow format [6]. The system performs four crucial tasks: When an attack occurs, the data collection unit detects malicious devices on the network, profiles the normal performance of the IoT devices linked

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to the networks and forecasts the kind of assaults that will be launched there. The suggested IDS are evaluated in terms of key metrics and validation techniques for the different IoT threat scenarios. Using the OMENT-python API, a real-time scenario and IoT networks under assault have been created. This has allowed for the analysis of the many characteristics of typical malicious nodes inside the network. In addition, the proposed model is compared with widely known datasets such as KDD, UNSWNB15, and CIDDS-001. In addition, tests were used to verify the proposed models [7]. A system for intrusion detection that can automatically and promptly identify and categorize intrusions at the host and network levels is being developed using machine learning methods. But because harmful stabbings are frequent and vary greatly, there are some obstacles that must be overcome to deliver a walkable solution. Numerous malware datasets are made accessible to the public for the cyber security community’s further investigation. To investigate, far no one has delivered a full examination of how changed machine learning algorithms perform across a number of publicly accessible datasets. Due to malware’s dynamic nature and continually developing attack techniques, publicly accessible malware datasets must be normally modernized and subjected to benchmarking. One specific deep learning model used in this work is a deep neural network [8].

2.3 Internet of Things (IoT) An innovative security framework for safeguarding the Internet of Things (IoT) involves cutting-edge approaches like loud technology, blockchain, machine learning, and Security Level Certificates (SLC) assignment. Cloud technology offers a four-phased security paradigm for securing IoT data transmitted to fog servers [9]. Combining software-defined networks (SDNs) and blockchain technology creates a scalable distributed framework for secure IoT networks in smart cities [10]. The proposal of assigning SLCs to IoT objects based on their hardware capabilities enhances secure communication among devices [11]. Additionally, a machine learning-based system can automatically detect and classify IoT devices, ensuring security by identifying unauthorized devices [12]. By integrating these innovative measures, a comprehensive security framework can effectively protect IoT devices and data. A Security-Enabled Safety Assurance Framework for IoT-Based Smart Homes integrates security measures to safeguard IoT systems, ensuring safety through monitoring, analysis, and communication of system status and recommended actions [13]. An Efficient Intrusion Detection Framework for Industrial Internet of Things Security provides a robust IDS utilizing machine learning algorithms to safeguard IoT infrastructure against severe cyber-attacks effectively [14].

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2.4 Intrusions Detection System (IDS) An Intrusion Detection System (IDS) for the Internet of Things (IoT) is crucial due to the vulnerability of IoT networks to various attacks [15]. IDS in the IoT utilizes deep learning models like deep neural networks to identify and classify malicious network traffic [16]. These systems operate in stages to differentiate between normal and attack traffic, determining the type of attack for enhanced security [17]. To improve accuracy, IDS techniques combine machine learning with heuristic algorithms and logistic regression, achieving high attack detection rates [18]. Testing IDS using IoT datasets like UNSW-NB15 ensures accuracy in detecting intrusions, with experiments confirming the effectiveness of IDS in both false positive and false negative tests. Overall, IDS in IoT networks plays a vital role in safeguarding against cyber threats and ensuring network security. An Intrusion Detection System (IDS) is a crucial defense mechanism against cyber threats [19]. IDSs utilize machine learning (ML) and deep learning (DL) techniques for enhanced security. These systems require enough samples to maintain accuracy, with DL-based IDS models outperforming traditional ML approaches [20]. IDSs monitor network events for potential security breaches, distinguishing between normal and malicious activities [21]. Additionally, IDSs can be complemented by intrusion prevention systems (IPS) that not only detect but also take action against malicious activities. Despite challenges like insufficient data samples, IDSs play a vital role in safeguarding computer systems and networks. The continuous evolution and growth of IDS technologies highlight their increasing importance in today’s interconnected digital landscape. An IDS (Intrusion Detection System) monitors network traffic for suspicious activities, detects anomalies, and can take actions like blocking traffic from suspicious IP addresses using data mining techniques. Intrusion detection systems (IDS) are crucial for identifying malicious activities in networks. The paper provides an overview, architecture, classification of threats, and a proposed framework for IDS implementation [22]. Intrusion Detection System (IDS) is a tool or piece of software that monitors system activities to detect malicious behavior, helping prevent various types of cyber-attacks and ensuring digital information security [23]. An Intrusion Detection System (IDS) is a network security tool that detects and tracks intruders in a network, as discussed in the paper on IDS and techniques [8].

3 Materials and Methods In the current study, you can see the methodology demonstrated in Fig. 1. The methodology employed in this study consists of three primary stages, decision trees, KNearest Neighbor (KNN), and logistic regression. This dataset is designed to support the development and evaluation of intrusion detection systems (IDS) for the Internet of Things (IoT) domain. It contains network traffic data that simulates different types of IoT devices and their interactions, including normal and malicious activities. The

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language is Python, Google Collab Consider it online coding playground. Collect relevant data sources for training and testing the IDS. This may include network traffic data, system logs, security alerts, and other relevant information. Ensure that the data is representative of both normal and anomalous behavior. Deploy the trained IDS model into the assembly surroundings for real-time examination and detection of invasions. Put together the IDS with existing security organizations, such as firewall systems or incident response workflows. Execute mechanisms for alerting, registering, and responding to actions based on detected intrusions. Divide the preprocessed data into training and validation sets. Train the chosen machine learning algorithms on the training data using fitting optimization techniques (e.g., gradient descent). Tune hyperparameters through techniques like grid search,

Fig. 1 Proposed framework

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random search, or Bayesian optimization to optimize model performance. Model evaluation is the technique and process of examining the operation and productivity of a machine learning model based on its calculations adjusted to the exact outcomes in a dataset. It relates to using different systems of measurement and procedure to test how well the model simplifies to new, not-seen data and to identify any likely limitations or areas for goodness. The main purpose of model estimation is to ensure that the model is accurate and suitable for its predicted purpose. Fine-tune the hyperparameters of the selected models to boost performance and generality. Experiment with different relationships and settings to classify the best-acting model variants.

4 Results and Discussion In this paper, the implications of the study are presented, which confirm the functionality and efficiency of the machine learning model applied to the task at hand. This section summarizes the efforts in data collection, preprocessing, model training, and evaluation. Start by giving an overview of the new setup, outlining the datasets used for training and testing, as well as the preprocessing procedures applied to confirm data quality and consistency. In addition, the hyperparameters and configurations used for each model during training will be detailed. The results of any hyperparameter optimization or model selection operations that will be performed to improve the performance of the algorithms will be presented. This includes comparisons between different configurations and optimization strategies to reveal the most effective approach. Finally, propositions of the findings are discussed in the context of the research objectives, highlighting notable trends, strengths, and limitations in the performance of the models. By critically analyzing the results. The research aims to provide valuable insights and references for future research orders in this field. Hence, this paper serves as a complete examination of the empirical results, highlighting the effectiveness and applicability of machine learning models in addressing the research problem at hand. The key performance metrics used to assess the intrusion detection system (IDS): Accuracy, Precision, Recall, and F1-Score. These metrics provide a comprehensive evaluation of the IDS model’s performance in terms of its ability to correctly identify normal and attack instances, minimize false positives, and maximize true positive detections. Analyzing these metrics together gives insights into the overall effectiveness and reliability of the intrusion detection system.

4.1 Results Graphs and Outcomes The results and conclusions of the investigation into creating an intrusion detection system, or IDS, with machine learning techniques are presented in this paper goal is to

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determine how well the IDS performs and how successful it is in identifying different kinds of network intrusions. Start by describing the performance metrics accuracy, precision, recall, and F-1 score—that are used to assess the IDS examine how well the machine learning algorithms that were taught to identify intrusions performed. This entails assessing each model’s precision and effectiveness in identifying network traffic as malicious or genuine as shown in Figs. 2 and 3 and Table 1.

Fig. 2 Normal and anomaly data points

Fig. 3 F1 score and confusion matrix of all models

Innovative Security Measures: A Comprehensive Framework … Table 1 Comparing KNN, logistic regression, and decision trees

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Model

Train score

Test score

KNN

0.981173

0.976184

Logistic regression

0.931326

0.924848

Decision trees

0.999943

0.993914

5 Conclusion In this study, the value of three machine learning systems was studied for the detection of intrusions (IDS) problem: the network security decision tree, the K nearest fellow citizen (KNN), and logistic regression. The usefulness and applicability of these models in classifying and classifying rare network activity were demonstrated through comprehensive testing and judgment. The results show that each of the models can identify intrusions; however, their ability varies depending on their capacities. The decision tree model showed strong performance in identifying complicated patterns in the data because of its logical decision-making process and interpretability. On the other hand, it showed signs of overfitting, especially when multidimensional or noisy datasets were used. On the other hand, the KNN model demonstrated compliance and flexibility in identifying local patterns within the feature space, which makes it a good fit for situations where features and the target variables have nonlinear correlations. However, because of its high computational difficulty and sensitivity to hyper parameter selection, real-world implementation requires careful study. Additionally, it was discovered that logistic regression was a dependable and effective option that provided interpretability and simplicity, along with competitive classification accuracy. It is particularly well-suited for situations where features such as interpretability and processing speed are important because of its probabilistic structure and capacity to mimic linear connections. In all things studied, research emphasizes how vital it is to use machine learning models that are suitable for the needs and limitations of the IDS domain. Even though every model has advantages and difficulties, taken as a whole, they help to improve network security and reduce the dangers brought on by cyber-attacks.

References 1. G. D. C. Bertoli, L. A. P. J´unior, O. Saotome, A. L. Dos Santos, F. A. N. Verri, C. A. C. Marcondes, S. Barbieri, M. S. Rodrigues, and J. M. P. De Oliveira, “An end-to-end framework for machine learning based network intrusion detection system,” IEEE Access, vol. 9, pp. 106 790–106 805, 2021. 2. M. Eskandari, Z. H. Janjua, M. Vecchio, and F. Antonelli, “Passban ids: An intelligent anomalybased intrusion detection system for iot edge devices,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6882–6897, 2020.

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3. Z. K. Ibrahim and M. Y. Thanon, “Performance comparison of intrusion detection system using three different machine learning algorithms,” in 2021 6th international conference on inventive computation technologies (ICICT). IEEE, 2021, pp. 1116–1124. 4. M. Imad, M. Abul Hassan, S. Hussain Bangash, and Naimullah, “A comparative analysis of intrusion detection in iot network using machine learning,” in Big Data Analytics and Computational Intelligence for Cybersecurity. Springer, 2022, pp. 149–163. LNCS. 5. B. Jothi and M. Pushpalatha, “Wils-trs—a novel optimized deep learning based intrusion detection framework for iot networks,” Personal and Ubiquitous Computing, vol. 27, no. 3, pp. 1285–1301, 2023. 6. P. K. Keserwani, M. C. Govil, E. S. Pilli, and P. Govil, “A smart anomaly-based intrusion detection system for the internet of things (iot) network using gwo–pso–rf model,” Journal of Reliable Intelligent Environments, vol. 7, no. 1, pp. 3–21, 2021. 7. J. Manhas and S. Kotwal, “Implementation of intrusion detection system for internet of things using machine learning techniques,” Multimedia Security: Algorithm Development, Analysis and Applications, pp. 217–237, 2021. 8. G. Rathee, F. Ahmad, N. Jaglan, and C. Konstantinou, “A secure and trusted mechanism for industrial iot network using blockchain,” IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1894–1902, 2022. 9. Z. G. Al-Mekhlafi, “Software-defined vehicular networks (sdvn),” International journal of computer science and network security: IJCSNS, vol. 22, no. 9, pp. 231–243, 2022. 10. N. Saran and N. Kesswani, “A comparative study of supervised machine learning classifiers for intrusion detection in internet of things,” Procedia Computer Science, vol. 218, pp. 2049–2057, 2023. 11. M. Sarhan, S. Layeghy, and M. Portmann, “Feature analysis for machine learning-based iot intrusion detection,” arXiv preprint arXiv:2108.12732, 2021. 12. G. Thamilarasu and S. Chawla, “Towards deep-learning-driven intrusion detection for the internet of things,” Sensors, vol. 19, no. 9, p. 1977, 2019. 13. S. Ullah, J. Ahmad, M. A. Khan, E. H. Alkhammash, M. Hadjouni, Y. Y. Ghadi, F. Saeed, and N. Pitropakis, “A new intrusion detection system for the internet of things via deep convolutional neural network and feature engineering,” Sensors, vol. 22, no. 10, p. 3607, 2022. 14. R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep learning approach for intelligent intrusion detection system,” Ieee Access, vol. 7, pp. 41 525–41 550, 2019. 15. A. Wani and R. Khaliq, “Sdn-based intrusion detection system for iot using deep learning classifier (idsiot-sdl),” CAAI Transactions on Intelligence Technology, vol. 6, no. 3, pp. 281– 290, 2021. 16. A. Jahangeer, S. U. Bazai, S. Aslam, S. Marjan, M. Anas, and S. H. Hashemi, “A review on the security of iot networks: From network layer’s perspective,” IEEE Access, vol. 11, pp. 71 073–71 087, 2023. 17. S. Bettayeb, M.-L. Messai, and S. M. Hemam, “A robust and efficient vector-based key management scheme for iot networks,” Ad Hoc Networks, vol. 149, p. 103250, 2023. 18. Y. R. Siwakoti, M. Bhurtel, D. B. Rawat, A. Oest, and R. Johnson, “Advances in iot security: Vulnerabilities, enabled criminal services, attacks, and countermeasures,” IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11 224–11 239, 2023. 19. V. Cp, S. Kalaivanan, R. Karthik, and A. Sanjana, “Blockchain-based iot device security,” in 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2022, pp. 1–6. 20. T. Nalini and T. M. Krishna, “Analysis on security in iot devices an overview,” The Industrial Internet of Things (IIoT) Intelligent Analytics for Predictive Maintenance, pp. 31–57, 2022. 21. Z. Fang, H. Fu, T. Gu, P. Hu, J. Song, T. Jaeger, and P. Mohapatra, “Iota: A framework for analyzing system-level security of iots,” in 2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI). IEEE, 2022, pp. 143–155. 22. I. Cvitić, M. Vujićet al., “Classification of security risks in the iot environment.” Annals of DAAAM & Proceedings, vol. 26, no. 1, 2015.

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Adaptive Social Network Quality (ASNQ) Model: A Conceptual Quality Model Gana Sawalhi and Mohammad Abdallah

Abstract Social networking sites (SNS) have revolutionized communication, information sharing, and community building for billions of users globally, becoming integral to daily life. Platforms like Facebook, Instagram, LinkedIn, and Twitter are crucial for business, political, and personal interactions. Existing research has identified several key factors influencing SNS quality, such as usability, privacy, social connectedness, and system efficiency. Traditional models like the Social Network Adaption (SNSA) model emphasize elements such as critical mass, perceived utility, playfulness, and trust. However, with advancements in data analytics and real-time interaction technologies, users increasingly demand personalized experiences and real-time adaptability. To address these evolving expectations, the Adaptive Social Network Quality (ASNQ) model is proposed, incorporating personalization, adaptability, and proactive engagement to provide a more dynamic and comprehensive understanding of SNS quality. This model enables platforms to better meet user-centric demands in the rapidly evolving digital landscape. Keywords Social network · Quality model · Quality factors · Adaptive model

1 Introduction Social networking sites (SNS) have transformed communication, information sharing, and community building for billions of users globally. They are now an essential part of everyday life. Social media sites like Facebook, Instagram, LinkedIn, and Twitter are now indispensable for business networking, political conversation, and personal relationships [1, 2]. The criteria governing these platforms’ quality and degree of user satisfaction have come under closer examination as they continue to expand in terms of both size and influence. Research already conducted has revealed a number of critical factors that affect SNS quality, including social connectedness, G. Sawalhi · M. Abdallah (B) Al-Zaytoonah University of Jordan, Amman, Jordan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_12

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usability, privacy, efficiency, security, and high-quality content. These elements have been essential to the development of frameworks and models like the Social Network Adaption (SNSA) model and frameworks like WebQual and SNSQUAL [3–5]. The SNSA model emphasizes how users’ decisions to adopt and stick with SNS platforms are influenced by elements including critical mass, perceived utility, playfulness, and trust [3]. Similar to this, additional research has stressed the significance of system quality, emphasizing technical elements that improve user experience, such as speed, dependability, and ease of use [6]. It has also been demonstrated that community-driven features that encourage peer connection and social influence have a major impact on user engagement. Even though these models have provided insightful information on the variables affecting SNS acceptance and usage, they frequently ignore the quickly changing expectations of contemporary users in favor of concentrating mostly on static characteristics of platform quality. Users want more and more personalized experiences in today’s digital world, catered to their own tastes and habits. User expectations have changed dramatically as a result of developments in data analytics, machine learning, and real-time interaction technologies. SNS platforms are now under pressure to offer more personalized experiences and react quickly to user feedback [5, 7–9]. However, this movement is not fully captured by traditional models, creating a vacuum in our knowledge of how platforms might change to meet users’ evolving expectations. Our proposal, the Adaptive Social Network Quality (ASNQ) model, fills this gap by adding new characteristics that take into consideration the dynamic nature of SNS quality. Personalization, adaptability, and proactive engagement are some of these characteristics. In order to create a more relevant and interesting experience, personalization which it refers to the platform’s capacity to deliver customized content and interactions based on each user’s unique preferences and behaviors. The ASNQ model highlights unique user experiences as a key component in establishing SNS quality, in contrast to previous models that regard users as a homogeneous set. Another crucial factor is adaptability, which emphasizes how crucial it is for platforms to continuously modify their features, interface, and content display while also reacting in real-time to user feedback. Because of its responsiveness, platforms are guaranteed to change in accordance with user expectations—a potential that older models undervalue. Ultimately, proactive engagement highlights how the platform actively promotes user connection by providing tailored alerts, notifications, and content recommendations. The ASNQ model contends that platforms themselves need to be more actively involved in maintaining user engagement, whereas previous models have concentrated on peer-driven social influence. In conclusion, the Adaptive Social Network Quality (ASNQ) model addresses the changing demands of contemporary SNS users, building upon and expanding upon previous models. This paradigm, which incorporates personalization, adaptation, and proactive engagement, provides a more dynamic and comprehensive view of SNS quality, allowing platforms to better fulfill the expectations of a digital landscape that is becoming more and more user-centric, the paper starts with an introduction that explores the evolution of social networking platforms and the important

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need for models that can respond to fast changing user expectations. The literature review then reviews current frameworks such as the Social Network Adaption (SNSA) model and other well-known quality models, finding major areas where they fall short. The suggested ASNQ paradigm is then presented, highlighting its key components: personalization, adaptability to user feedback, content relevance, proactive engagement, and cross-platform interaction. Each of these parts is thoroughly investigated, with hypotheses produced to support the model’s claims, the discussion part discusses the ASNQ model with existing frameworks, highlighting its ability to address modern user expectations in a rapidly changing digital context. Finally, the conclusion highlights the ASNQ model’s usefulness in increasing user satisfaction and engagement on SNS platforms, while also providing future research directions for validating and developing the model.

2 Literature Review Several models and frameworks have been presented to investigate the impact of quality elements on user engagement and satisfaction on social networking sites (SNS). These characteristics have been investigated extensively and have been found to have implications for organizations as well as individual users. The Social Network Adaption (SNSA) model, which looks at important elements such perceived playfulness, critical mass, and trust, is one of the well-known models in this field. According to research, users’ intentions to use social networking sites (SNS) are highly influenced by playfulness, which is defined as the enjoyment users anticipate from utilizing these environments. The idea of critical mass, which is reached when there is a sufficient number of active users to draw in new members, is also important. Adoption of social networking sites is also largely dependent on trust, which includes safety, dependability, and normative pressure (social influence). Moreover, it has been repeatedly shown that perceived utility and simplicity of use have a significant role in influencing users’ decision to keep using SNS features [3]. Numerous studies have looked at how SNS can enhance organizational performance in addition to individual user performance. These platforms facilitate the exchange of knowledge among businesses, aiding them in overcoming environmental obstacles. Innovation capability, which is further subdivided into operational and product development capabilities, is essential to optimizing SNS’s effectiveness for businesses. The former prioritizes the development of new products, whereas the later places more emphasis on process optimization and gradual innovation. Researchers contend that a firm’s approach for adopting SNSs is greatly influenced by the external environment. To keep a competitive edge, businesses must continue to be flexible and adjust their plans in response to changes in their environment. The environment-strategy-performance framework, which examines the connection between SNS capacities, innovation capability, and company performance [10], emphasizes how crucial this is for managing environmental turbulence.

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Additionally, SNS have been shown to improve life satisfaction, especially for older users. SNS use is positively correlated with self-assessed life satisfaction, even after adjusting for sociodemographic and economic factors, according to a study on older Europeans, the psychological advantages of social interaction made possible by SNS highlight how crucial they are to improving older persons’ wellbeing [11]. Another study that looks at privacy concerns and user resistance with internal social networking sites (ISNS), which are exclusive networks that are exclusively accessible by employees of a company, an investigation including 253 working professionals from various sectors and company sizes has shown that privacy concerns play a major role in ISNS avoidance. The study demonstrated that perceived utility and simplicity of use are significant factors influencing the inclination to interact with ISNS. These factors were shown to have strong predictive power (R2 = 0.731), which helped to explain the usage pattern of ISNSs [12]. Recent research has discovered a number of elements that influence the success of social networking sites: Efficiency, user-friendliness, real-time updates, security and privacy, layout, and navigability are the most important components, according to an online survey, the main factors influencing users’ decision to stick with SNS are enjoyment, usefulness, and the quantity of peers they have [13, 14]. Subjective norms, perceived critical mass, gratifications, and privacy concerns were found to be important factors in determining users’ intention to stick with SNS in a different study [15]. According to studies on user switching behavior, dissatisfaction with member policies and peer influence are important motivators for switching between SNS platforms. While switching expenses were shown to have little impact, users mentioned mobile capabilities and real-time access as the most compelling reasons for moving to mobile-based SNS programs [16]. According to research on question-asking behavior on social networking sites, users are driven by trust, personalization, social connection, perceived speed and quality, and minimal effort [6]. The function of trust in social networking sites has been extensively explored, particularly in terms of information sharing. Trust in the platform has been recognized as a strong predictor of users’ willingness to provide information. Privacy concerns, trust tendency, and social presence (a website characteristic) have all been identified as major predictors of trust in SNS [17]. Several studies have also looked into the factors that influence user satisfaction on social networking sites, and they discovered that system quality, information quality, and privacy protection all play an important part in continuous platform usage. Younger people value response time, navigation, and interactivity, whereas elderly users are more concerned with efficiency and interaction quality. This behavioral disparity highlights the need of SNS platforms tailoring their features to distinct user demographics [18, 19]. SNS platforms have also emerged as important tools for social commerce, with studies indicating that social support—measured by informational and emotional support—has a beneficial impact on users’ intentions to continue engaging with SNS for commerce purposes. Furthermore, connection quality acts as a mediator,

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increasing long-term SNS engagement, while website quality also helps, albeit to a lesser amount [20]. When investigating the service quality of SNS, numerous models have been presented. The SNSQUAL approach, for example, stresses criteria such as usability, trustworthiness, personalization, integration, and reliability. These aspects improve the perceived quality of SNS platforms and influence future usage intentions [5]. Furthermore, models such as HWebSQ emphasize the importance of system quality, information quality, and privacy protection in influencing user satisfaction and the advantages of social interaction on SNS [21, 22]. Comparative studies of SNS platforms such as Facebook, Twitter, and MySpace have thrown light on how different features effect user engagement. Navigation, interactivity, and source credibility have been cited as essential factors in assuring consumer satisfaction and retention across different platforms. To improve the user experience, SNS designers must improve real-time updates, content sharing, and interaction choices [23]. This paper proposed a new quality model for social media websites based on an extensive literature review. According to factors prominent in previous research and their relevance to user satisfaction, they found that user friendliness, communitydrivenness, website appearance, entertainment, security and privacy, efficiency, and navigability are most important in the context of social media platforms [24]. Further research has looked into how website quality elements and cognitive absorption affect SNS acceptance. According to studies, ease of use and usefulness are important factors in attracting consumers to social networking sites. Furthermore, increased enjoyment, curiosity, and temporal dissociation were discovered to improve users’ intents to use SNS [25]. Other studies have looked into virtual communities and discovered that elements including personal design, experience value accumulation, instant messaging, voice/ video messaging, and personal connection maintenance have a key role in influencing user engagement and happiness [7]. In the academic context, research on elearning portals has found that students are particularly concerned with usability and related characteristics, as their educational processes frequently rely on these platforms. Studies undertaken in the education sector suggest that reliability, efficiency, usability, and satisfaction are significant quality aspects for assuring the success of e-learning systems [26, 27], In the academic context also, the authors of this paper objective were to understand factors influencing e-learning adoption and identify strengths and weaknesses in the updated e-learning system, aiming to offer insights for other institutions in Jordan and the Middle East. Conducting an online survey targeting 500 students, they found that perceived benefits had the most significant impact on e-learning system usage, students related high satisfaction with the system [28]. E-Service Quality (e-SQ) Customers prioritize access, ease of navigation, efficiency, flexibility, personalization, security/privacy, and responsiveness when evaluating online service quality [29]. And since that people are frequently the weakest link in cybersecurity because they make mistakes or fall for tricks like scams or phishing emails [30]. The majority of the models we discussed consider factors such

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as privacy and security to be significant quality factors in systems. Additionally, as suggested in this paper [30], one solution to preventing cyber threats beside systems having strict privacy and security protocols, is to educate and train people to be more aware of these threats and how to avoid them. Several articles in the field of e-commerce have looked into the different aspects of website service quality and how they affect user satisfaction. Research has identified crucial variables such as website design, customer service, assurance, order management, and privacy protection as critical to user satisfaction and loyalty [31, 32]. These findings are consistent with previous research that emphasizes the relevance of usability, design, information quality, trust, and empathy when evaluating e-commerce websites [33]. Overall, the literature emphasizes the importance of user-centric design in increasing engagement, satisfaction, and continued usage on social networking sites. Trust, ease of use, system and information quality, and privacy issues have all been identified as major factors of SNS success in the research reviewed. Platforms must constantly adapt to changing user expectations by improving mobile access, realtime functionality, and social commerce capabilities to keep their competitive advantage. These findings are valuable for SNS developers and organizations looking to strategically use social platforms.

3 Adaptive Social Network Quality (ASNQ) Model Based on a thorough analysis of the existing research and emerging trends in social networking platforms, we offer the Adaptive Social Network Quality (ASNQ) model. This model is intended to capture the changing dynamics of user interactions with social networking sites (SNS) and provide a comprehensive framework for assessing platform quality. The model addresses crucial aspects of user experience, technology integration, and social interaction, all of which are required for SNS platforms sustained success. The Adaptive Social Network Quality (ASNQ) paradigm highlights the value of personalization, adaptation, and proactive participation in providing a high-quality SNS experience. In a world where user expectations are continually changing, platforms must be flexible and responsive in order to maintain user engagement and satisfaction. The ASNQ model includes five essential dimensions that influence the user experience: • Personalization and customization Personalization is the platform’s capacity to customize content, interactions, and suggestions based on individual user choices, habits, and demographics. Personalization promotes deeper engagement by providing relevant material and connections, resulting in higher user satisfaction. Personalized experiences have frequently been proved in research to boost user engagement and loyalty to SNS platforms [3, 29, 33–36].

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Hypothesis 1 (H1): Personalization improves user engagement and perceived platform quality. • Adaptability to user feedback Adaptability is described as the platform’s ability to dynamically respond to user feedback and alter its features, content, and design to meet changing user needs. Platforms that use real-time feedback systems tend to have higher user satisfaction because they are perceived as more responsive and user-centric [12, 13, 37, 38]. SNS platforms can increase overall usability and encourage continuing use by responding rapidly to user preferences. Hypothesis 2 (H2): The ability to respond to customer feedback improves user satisfaction and platform responsiveness. • Content relevance and curation Material curation refers to the platform’s ability to filter and display high-quality, relevant material to its users. With so much information published on SNS, people want platforms to prioritize useful and trustworthy material. According to research, content relevance is a significant predictor of user trust and engagement [6, 11, 39, 40]. Hypothesis 3 (H3): Content relevance and quality curation improve user satisfaction and platform credibility. • Proactive engagement Proactive engagement means that the platform actively encourages user activity through features such as notifications, alerts, and suggestions. SNS platforms can increase user connection and engagement by sending out timely reminders or highlighting significant updates [3, 41]. Proactive engagement tools increase the user’s sense of connection to the platform, which leads to higher retention. Hypothesis 4 (H4): Proactive engagement strategies improve user retention and platform use. • Cross-platform integration Cross-platform integration aims to provide a consistent user experience across several devices (e.g., mobile and desktop) as well as interoperability with third-party services. In a multi-device environment, users demand the same functionality and design no matter how they access the platform. Providing seamless transitions across devices increases user comfort and happiness [39]. Hypothesis 5 (H5): Cross-platform integration improves user satisfaction by ensuring a consistent experience across all devices. While previous models stress elements like usability, privacy, entertainment, and community participation, the ASNQ model incorporates emerging technologies and dynamic user demands to provide a more comprehensive picture of platform quality in the changing digital ecosystem (Table 1).

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Table 1 The quality factors of adaptive model Quality factor

Definition

Hypothesis

References

Personalization and customization

Platform’s ability to tailor content, interactions, and recommendations based on individual user preferences, behaviors, and demographics

Personalization improves user engagement and perceived platform quality

[12, 13, 37, 38]

Adaptability to user feedback

Platform’s ability to respond dynamically to user feedback and adjust its features, content, and design to align with evolving user needs

The ability to respond [12, 13, 37, 38] to customer feedback improves user satisfaction and platform responsiveness

Content relevance and curation

Platform’s capacity to filter and present high-quality, relevant content to users

Content relevance and [6, 11, 39, 40] quality curation improve user satisfaction and platform credibility

Proactive engagement

It involves the platform actively encouraging user interaction through features like notifications, alerts, and recommendations

Proactive engagement [3, 41] strategies improve user retention and platform use

Cross-platform integration

Focuses on ensuring a seamless user experience across different devices (e.g., mobile, desktop) and compatibility with third-party services

Cross-platform integration improves user satisfaction by ensuring a consistent experience across all devices

[39]

4 Discussion As mentioned in the literature review, prior models, such as the Social Network Adaption (SNSA) model and frameworks investigating system quality, trust, and efficiency, have made major contributions to understanding the quality of SNS platforms. The SNSA model emphasizes playfulness, critical mass, trust, and normative pressure as drivers of user engagement [3]. Furthermore, research on internal and external SNS platforms has highlighted the significance of privacy concerns, content quality, and user usability as essential quality criteria [12, 13]. However, as user behaviors evolve, these existing models may fail to capture the complexities of modern SNS interactions, particularly those including tailored experiences, immersive technologies, and proactive engagement. The Adaptive Social Network Quality (ASNQ) model builds on existing frameworks by addressing the changing nature of user expectations and the requirement for real-time adaptability in SNS platforms. While previous models such as WebQual

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and SNSQUAL prioritize usability, privacy, and security, the ASNQ model incorporates critical elements like as personalization, adaptability, and proactive engagement that were previously missed. Personalization: Previous models, such as SNSQUAL, emphasize the significance of usability and trustworthiness but do not explicitly include personalization. According to the ASNQ model, in an era of data-driven content, social media platforms must create highly tailored user experiences to remain competitive. According to research, consumers increasingly expect platforms to adapt to their preferences, making personalization an important aspect for engagement [3]. Adaptability to User Feedback: Traditional models focus on system quality and reliability, but they frequently overlook the necessity of real-time adaptability. The ASNQ model emphasizes platforms’ ability to swiftly integrate user feedback and alter features or content depending on changing user needs, which is becoming an important driver of user happiness [12, 13]. Content Relevance and Curation: While various studies have identified content quality as a crucial component [6, 11], the ASNQ model prioritizes the curation of relevant content. It contends that the sheer volume of information on SNS platforms necessitates increasingly advanced filtering algorithms to produce meaningful, highquality content that is relevant to user interests [6]. Proactive engagement: Existing models, such as the SNSA model, recognize the importance of social influence and critical mass in promoting platform adoption, but they fail to address how proactive engagement methods (e.g., notifications, reminders) can boost user activity and retention. The ASNQ model presents this concept, claiming that platforms must actively encourage users to connect by providing timely updates and calls to action [3]. Cross-Platform Integration: Previous research has demonstrated that users increasingly use SNS platforms across numerous devices (mobile, desktop, etc.), but few models have explicitly included cross-platform integration as a quality component [39]. The ASNQ model emphasizes this aspect, saying that a consistent, seamless user experience across devices is critical for preserving user happiness in today’s multi-device era. In contrast to traditional models that prioritize usability, privacy, and system quality, Adaptive Social Network Quality (ASNQ) proposes a forward-thinking method to assessing and improving SNS platform quality. The ASNQ concept focuses on personalization, adaptability, and proactive engagement. This model provides a more thorough framework for evaluating user satisfaction and engagement on modern social networking platforms, providing significant data for both researchers and platform developers looking to improve the user experience (Fig. 1).

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Fig. 1 Adaptive Social Network Quality (ASNQ) model

5 Conclusion The proposed Adaptive Social Network Quality (ASNQ) model addresses existing framework constraints by considering modern SNS users’ growing expectations. Traditional models, while useful, frequently neglect the dynamic and personalized nature of today’s digital experiences. The ASNQ model provides a more comprehensive method to evaluating the quality of social networking sites by including crucial factors such as personalization, adaptability, and proactive taking part. This approach highlights the importance of platforms providing tailored content, responding in real time to user feedback, and actively encouraging user engagement through continuous interaction. The ASNQ model not only improves our understanding of SNS quality, but it also has practical implications for platform developers looking to increase user satisfaction and retention. Future study should experimentally evaluate this model to improve its applicability and investigate how these aspects influence long-term platform success in a variety of digital environments.

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Advanced Technologies and Their Broader Impacts

Authorship Attribution: Performance Model Evaluation Bodor Shalbi and Tawfeeq Alsanoosy

Abstract The rise of the internet and social media has fundamentally transformed how communication occurs, enabling the rapid and widespread dissemination of information, often anonymously. This prevalence of anonymous information has led to an increase in the unacknowledged copying of text, posing significant risks of copyright infringement, where the authenticity of information is paramount. Authorship attribution (AA), is a crucial classification challenge in Natural Language Processing (NLP), aims to determine the authorship of texts, addressing these concerns by identifying the original creators of content. While extensive research has been conducted on long texts, the AA of short texts such as tweets remains challenging due to their concise nature and diverse styles. This chapter investigates the performance of machine learning (ML) methods with different feature extraction techniques for short texts. We employed three ML algorithms: Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) compound with three feature extraction techniques: Bag of Words (BoW), TF-IDF, n-grams. The highest accuracy achieved with ML methods was 92.34% using an SVM with TF-IDF. This research not only advances the technical capabilities of AA but also extends its practical applications, providing tools that can be adapted across various domains to enhance the security and integrity of digital communications. Keywords Natural language processing · Authorship attribution · Machine learning classifiers · Twitter · Evaluation

B. Shalbi (B) · T. Alsanoosy Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia e-mail: [email protected] T. Alsanoosy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_13

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1 Introduction In the digital era, data volume has exponentially increased across formats like news articles, web pages, blogs, books, scientific papers, videos, images, audio, and social media platforms. This expansion has led to unattributed content, where original authors are not acknowledged. Social media, especially platforms like Twitter, has intensified the spread of misinformation and propaganda, challenging the accuracy and reliability of information sources [1]. Grier et al. [2] highlights that 84% of spam accounts on Twitter are compromised, with only 16% being fake. Spammers use these accounts to connect with or follow verified profiles, including celebrities, to gain legitimacy [3]. Consequently, cybercrime and copyright violations are rising, emphasizing the need to identify true authors of texts. Developing new computational tools is crucial for searching, organizing, and attributing data to its rightful authors, which the authorship attribution (AA) model aims to achieve. Stylometry, a branch of computational linguistics, quantitatively assesses linguistic features in texts to analyze an author’s unique writing style [4]. It applies to various NLP tasks, including AA, which identifies a text’s author from a group based on writing style [5]. This task is a key NLP application using ML methods for text recognition and attribution, and has been widely studied [2, 6, 7]. AA involves extracting text features from large (books or novels) or short (reviews, online posts, tweets) datasets, training an ML model, and using it to determine the author of a given text [8]. Twitter, a major source of public posts, is an active research area. It is the fourth most popular social media platform in the USA [9], with about 330 million monthly active users and 500 million tweets per day. AA on Twitter is used in forensic investigations to identify authors of threatening or criminal tweets [10], which can be evidence in court. It is also used in plagiarism detection to identify text reuse or copying [11]. Despite its potential, AA on Twitter is challenging due to informal language and slang. Our research aims to develop an AA model specifically tailored to identify the authors of tweets. A crucial aspect of AA approach lies in the selection and extraction of meaningful features from short texts, such as those found in twitter datasets. We will explore various feature sets, including character n-grams, Bag of Words (BoW), TF-IDF, to analyze their effectiveness through rigorous computer experiments [12]. We will employ a comprehensive array of ML techniques, including Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR). By evaluating the performance of these techniques, we aim to shed light on the quality of features extracted from Twitter datasets and their impact on AA tasks. The outcomes of this research hold significant implications. Firstly, enhancing the accuracy of AA methods could have practical applications in crime investigation and literary authentication. Secondly, gaining insights into the factors that influence model accuracy could inform writing instruction practices and aid in the development of more effective plagiarism detection methods.

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Outline: This chapter is structured as follows. Section 2 is a background foundational concepts and definitions that underpin this research. Related works are disused in Sect. 3. Section 4 describes the research methodology. In Sect. 5, we present the findings of our experiments and analyzes their significance. Finally, Sect. 6 presents conclusion and future works.

2 Background Natural Language Processing (NLP) is a field in computational linguistics which is concerned with development of algorithms and software that enable computers to understand, process, and generate human languages [8]. The success of NLP algorithms or systems is determined by their ability to achieve their intended goals, with each task having its own criteria for evaluation. Authorship attribution (AA) is one important task of NLP. This research investigate the effectiveness of various ML methods in the context of AA, aiming to enhance the accuracy and reliability of AA on short texts, particularly on social media platforms like Twitter.

2.1 Authorship Attribution (AA) AA is the task of determining the author of a given text [13], whether it’s a single document or a collection of datasets [4], involving a few or many candidate authors [14]. It transcends language barriers, despite English being the most utilized [14]. AA serves various applications across disciplines, including forensics [15], literary studies [16], copyright [11], cloning [8], plagiarism detection [17], cybersecurity [18], and cybercrime investigation [19]. In forensics, AA aids in identifying authors of disputed documents like wills or contracts, crucial in legal contexts questioning document authenticity [20]. For example, in literary studies, AA tracks the evolution of an author’s writing style over time, illuminating their creative process and idea development. Thus, the primary goal of AA is accurately attributing contentious works to their authors from a list of potential candidates and text samples [13], emphasizing the fidelity of representing authors’ personal styles [5]. Despite challenges posed by evolving styles and shared writing characteristics among authors [7], advancements in machine learning techniques are enhancing accuracy, especially with short text datasets [18]. Effective AA methods hinge on feature extraction quality [18], the number and types of features extracted, text size for training [21], and classification methods [19]. Research explores diverse features, methods, and languages in AA on short texts [22, 21, 17, 14], categorizing features into lexical, character, syntactic, semantic, and application-specific categories [8]. Studies combine these features to improve identification accuracy [23]. Following feature extraction or selection, texts are prepared for ML algorithms. Numerous text classifiers, employing ML techniques have shown

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significant success in AA [5, 14, 24]. Researchers employ diverse models integrating ML techniques tailored to enhance AA task performance. This diversity reflects strategies aimed at surpassing benchmarks and achieving superior results in AA tasks, a topic further explored in subsequent sections. In this research, we selected the most popular and effective ML classifiers: NB, SVM, and LR. The following is a brief explanatory summary for each, clarifying their effectiveness in AA tasks.

2.2 Machine Learning (ML) Several papers have proposed ML techniques for AA tasks, achieving high accuracy [9, 25, 7]. The performance analysis of different supervised ML classifiers involves evaluating their efficiency through the implementation on the acquired dataset. By tuning the parameters of each classifier using a training and validation set, the most optimized classifier is selected and deployed on a test set to assess its performance [25]. Here we provide a brief description of each used classifier from ML methods. – Naive Bayes (NB) NB, also known as independent Bayes or simple Bayes, applies Bayes’ Theorem under the assumption of feature independence. It is widely favored in text classification for its simplicity and effectiveness [9], despite criticisms for its imperfect text modeling. Various papers have proposed corrections to enhance its performance and mitigate these limitations. P(c|x) =P(x|c)P(c)/P(x) where P(c|x) represents the probability of class x given class c, p (c) represents the probability of class c,p(x) represents the probability of class x, and P(x|c) represents the likelihood of class c given class x [7]. – Support Vector Machine (SVM) SVMs are widely used in text classification, including authorship identification. They aim to find an optimal hyperplane to separate data points in n-dimensional space into classes [26]. While SVMs excel in precision, they may have low recall. Adjusting the threshold can balance precision and recall [9]. SVMs construct an optimal hyperplane to classify patterns, represented by the following equation: aX +bY =C Here, X and Y represent features, a and b denote coefficients that determine orientation, and C represents a constant term. The hyperplane functions as a decision boundary, maximizing the margin between support vectors of different classes for improved generalization and classification [7]. During training, coefficients are

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adjusted to assign weights to features, and C determines the hyperplane’s position, facilitating effective class separation while maximizing margin. – Logistic Regression (LR) LR is a discriminative classifier that learns functions for classifying data. It models the probability of a certain class given the input data using the logistic function, which transforms the output to a valid probability value using the exponential function. This allows for effective classification with output probabilities that range between 0 and 1. The equation for LR is represented as: P(C|X ) =1/(1 +e

(−z)

)

Here, P(C|X) denotes the probability of class C given the input X. z represents the linear combination of weights and features in the equation. By applying the exponential function, we transform the output to a valid probability value between 0 and 1.

2.3 Feature Extraction in AA Feature Extraction is a crucial step in the machine learning workflow [27], converting text into feature vectors that classifiers use to build classification models and enhance ML efficiency and effectiveness. Selecting effective features is vital [28], as some may improve results while others may hinder them. Stylometric features analyze writing style using different linguistic aspects and consist of four types: lexical, character, syntactic, and semantic. Lexical features, like word n-grams, treat text as sequences of tokens and vary in accuracy based on n-gram length [17]. Character features, such as character n-grams, view text as sequences of characters, with studies showing their effectiveness in identifying writing styles; methods like text distortion and feature tuning can enhance this [5]. Syntactic features refer to sentence structure and grammar patterns, serving as unique “fingerprints” of writing style, with POS tags and syntactic errors used effectively [19]. Semantic features involve the meanings and relationships between words, revealing unique patterns in an author’s writing [26]. Feature selection algorithms help reduce dimensionality and avoid overfitting [8]. In this chapter, three different types of stylometric features which are known to be useful to determine the author writing style in AA were used: – Bag of Word model (BOW) The BoW features involve the process of converting text data into a comprehensive dictionary that includes all the words present in the texts [20]. This model is commonly utilized in text classification tasks, enabling the classification and analysis of diverse collections of texts. When given a set of documents as input, BoW generates a table that displays the frequency counts of each word across the documents.

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In this model, each word in the lines of text is treated as a feature. Using the dictionary, we can generate separate feature vectors by counting the occurrences of words in each document. – Term Frequency-Inverse Document Frequency (TF-IDF) TF-IDF calculates the frequency of a word appearing in a document relative to its frequency across all documents [9]. When the dataset was represented by its word unigrams, bigrams, and trigrams, the frequency of each word n-gram in the entire corpus was calculated [29]. In some studies, TF-IDF is used to calculate the score for each feature [18]. – N-gram The frequency of words in a script is known as an N-gram. The terms “unigram” and “bigram” describe the sequential occurrence of one and two words in a script. This might be a defining characteristic of AA. Previous studies have found that word and character-level n-grams are the most effective features for identifying authors. Word n-grams can represent local structure of texts and document topic. On the other hand, character n-grams have been shown to be effective for capturing stylistic and morphological information [30].

3 Related Works This section offers an insight into the classification techniques applied in AA tasks, focusing on evaluating the accuracy ML methods, particularly for short texts. Within the realm of AA, numerous studies have explored diverse approaches employing ML techniques. We provide the relevant related work as follow: Most AA research has been conducted in English, although there have been studies in other languages such as Arabic and Bengali. For example, Abuhammad et al. [29] achieved outstanding results using an SVM model with 45 authors. They sourced short Modern Standard Arabic texts from Twitter and employed features like BoW, TF-IDF, and word n-grams, achieving accuracies of up to 99.24%. Similarly, Rabab’Ah et al. [31] collected 38,386 tweets from 12 users across six domains via the tweepy API. The tweets were sourced from the Top 100 Arab users on Twitter based on followers and Arabic tweets. After performing text cleaning for feature extraction, some tweets were excluded, resulting in a dataset of 37,445 tweets. They employed BoW and stylometric features to construct classification models for short datasets like tweets, achieving the highest accuracy of 68.67% using the SVM classifier. In Bengali literature, Hossain et al. [32] utilized SVM and multilayer perceptron classifiers. The SVM model achieved 70% accuracy (linear kernel), while the MLP model attained 76.4% accuracy (logistic activation) and 75% accuracy (relu activation). Employing a voting system, the MLP classifier exhibited superior accuracy compared to the SVM, with 85% accuracy (logistic), and 83.2% accuracy (relu).

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Also, Chowdhury et al. [33] explored traditional classifiers including NB, SVM, and flat neural networks (NN) using a web crawler dataset comprising 2400 articles from six authors across three public blogs. The flat NN demonstrated superior performance over other classifiers when using the (1, 2) n-grams feature set, achieving an accuracy of 88.9%. while SVM achieved 87.9%. In English language AA studies, researchers extensively explored techniques and methods. For example, Aborisade and Anwar [9] focused on classifying authors of tweets using ML classifiers LR and NB. They collected Twitter data with a maximum of 3000 tweets per author, distinguishing between known (celebrities and prominent users) and unknown authors (regular users). The study involved tweet fetching, preprocessing, feature extraction, and classification algorithms, achieving 91.1% accuracy with LR and 89.8% with NB. Chen et al. [23] proposed AA models for identifying Twitter message authors using NLP techniques to extract lexical, syntactic, and semantic features. These features were input into NB, SVM, and NN multi-class classifiers. Their models, distinguishing tweets from six different authors, showed that almost all variants outperformed the baseline NB model, which achieved an accuracy of 84%. Rocha et al. [20] conducted a survey of state-of-the-art techniques for AA tasks, utilizing SVM and RF with BoW feature representation. Their method’s performance was evaluated across multiple authors and messages, yielding unsatisfactory results. They achieved less than 65% accuracy on a new experimental dataset with 50 authors, which dropped to less than 45% when scaled to 1000 authors. These findings underscore the ongoing challenges in PAA and the need for continued research and improvement. Schwartz et al. [34], SVM was employed along with character and word n-grams as a K-signature to capture the unique writing style found in 1000 tweets from 50 authors. The study achieved an accuracy of 50.7% on a dataset containing 50 authors, with 50 training tweets per author. Furthermore, an accuracy of 71.2% was achieved when using 10,000 training tweets per author. Joshi and Heywood [35] investigated the use of word embedding with TFIDF weighting for feature extraction in AA. They enhanced the existing flexible patterns approach and proposed an NN architecture integrating these features. Their model outperformed other systems in testing criteria and dataset performance. They extended their approach to another dataset, employing SVM, NB, RF, and MLP for classifying micro-texts. The results showed that the MLP classifier achieved the highest accuracy, indicating its effectiveness for classifying tweets based on their authors. Various studies show that AA methods’ performance hinges on language, dataset size, features chosen, and the number of authors involved. Datasets from sources like Twitter and articles in languages such as English, Arabic, and Bengali are commonly used. Techniques like LR, SVM, NB, MLP, and NN are applied, with SVM being the most utilized and effective. Features like BoW, TF-IDF, word and character ngrams, stylometric, linguistic, and sentiment features yield robust results. Accuracy ranges widely, from 55 to 99.24%, highlighting the critical role of model selection and feature extraction techniques.

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4 Research Methodology This section details the research methodologies and technologies selected for the project’s implementation. It outlines the AA task process, starting with an overview of the dataset, followed by a description of the preprocessing steps to clean and prepare the data for analysis, and the feature extraction process. It also presents the methodologies and technologies implemented and concludes with a summary of the evaluation criteria. The AA task in our project involves five key steps, as summarized in Fig. 1. These steps will be illustrated for ML methods in the following sections, following an overview of the dataset.

4.1 Dataset Selection We utilized the dataset provided by Schwartz et al. [34], sourced from the Twitter social media platform, where users express their opinions and thoughts within limited character posts. This dataset has been previously employed in several studies [35, 30, 36]. We opted to conduct our experiments on it with the aim of refining results and exploring diverse ML implementation methods. The dataset comprises approximately 15% of all public tweets created from May 2009 to March 2010. It includes a total of 7,000 authors, from which randomly selected 50 authors. Each author contributed 1,000 tweets to the dataset. Fig. 1 The main steps for our AA evaluation performance model

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4.2 Data Pre-processing Text preprocessing is essential for extracting features from text data and utilizing them in calculations. The specific approach to preprocessing may vary across different research studies. Typically, it involves removing undesirable elements like hyperlinks, stop words, and outliers that lack relevance in text classification [9]. Previous research has highlighted the significance of handling stop words URLs, and other symbols [36]. In our text classification task, we initiated the preprocessing stage by importing Python libraries. The primary libraries used for preprocessing short text datasets include: NLTK, Scikit-learn, SpaCy, TextBlob. These libraries offer functionalities for tokenization, stop word removal, stemming, lemmatization, and vectorization, which can be customized based on specific task requirements. We used the preprocessing function “preprocess text” performs the following steps on the input text: – Tokenization: Break down the text into individual tokens or words. – Remove Stop words: Each token is checked to determine if it is a stop word (common words like “is”, “the”, “and”, etc.). Tokens that are not stop words are retained, and those that are stop words are filtered. – Lemmatization: For each non-stop word token, its lemma (base or dictionary form) is extracted. This helps in reducing inflectional forms to a common base form. – Joining tokens: The preprocessed tokens are joined back together into a single string.

4.3 Features Extraction In feature extraction process, we experimented with three techniques: BoW, TF-IDF, and N-grams features. Furthermore, we investigated various combinations of TF-IDF with unigram +4 g. We defined a pipeline for each classifier, where each classifier consists of different combinations of feature extraction method such as BoW, TFIDF, and N-grams along with their corresponding vectorizers. We utilized Sklearn’s TfdfVectorizer, and CountVectorizer functions to produce feature sets. These combinations enable the comparison of classifier performance based on different feature representations of the input data, allowing for a comprehensive evaluation of classification accuracy and effectiveness. Figure 2 presents as an example the SVM combined to the vectorizers in the classifier pipeline.

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Fig. 2 SVM combined to the vectorizers in the classifier pipeline

4.4 Model Training We build our model using the dataset from Schwartz et al. [34]. We applied the experiment on 50 Twitter users with 1,000 tweets each. We randomly assigned 80% of the data for training, leaving the remaining 20% for validation. We randomly sampled 900 tweets from each set of 1000 tweets for the 50 authors to ensure unbiased evaluation of the model’s performance. The source codes are available at https://git hub.com/SaraML00/ML-authorship-attribution.git. We utilized a classifier Pipeline to configure multiple classifiers and vectorizer combinations for text classification. Each classifier in the pipeline was paired with specific vectorizers: BoW (CountVectorizer()), TF-IDF (TfidfVectorizer()), N-gram (1,2) (CountVectorizer(ngram_range =(1, 2))), and Combined (TF-IDF +N-gram (1, 4)). The ML classifiers in the pipeline include NB (MultinomialNB()), SVM (LinearSVC()), and LR (LogisticRegression()). Each classifier represents a distinct ML algorithm, each vectorizer represents a method for text representation. This setup allows for testing different ML classifiers with diverse feature extraction techniques to assess their effectiveness in text classification.

4.5 Model Evaluation Once the model has been trained, it is important to evaluate its performance on a held-out test set of text documents. This will help to ensure that the model is not overfitting the training data. Our proposed methodology for determine the accuracy of the authors for the given dataset is evaluated using Precision, Recall and F1score: These metrics are calculated based on the number of true positives, false positives, true negatives, and false negatives in the classification of AA dataset [14]. The evaluation metrics are presented as follows in equations. Precision = Recall =FP



TP TP +FP (FP +TN )

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 Accuracy = (TP +TN ) (TP +TN +FP +FN )  F −measures =2 ∗ (Pr ecision ∗Recall ) (Precision +Recall )

5 Results and Discussion This section outlines the outcomes of our experiments. We aimed to assess the performance of diverse ML classifiers in constructing an AA model. We employed the standard Twitter dataset referenced in [34]. We utilized three ML classifiers: NB, SVM, and LR, coupled with three feature extraction techniques BoW, TF-IDF, and N-grams with combination of TF-IDF and N-gram. In our experiments, we used the random state parameter with a fixed seed to generate consistent samples each time we ran the code, ensuring result reproducibility. The accuracy results, derived from the confusion matrices for the ML methods experiments, are as depicted in Table 1. In the initial series of experiments, the BoW technique was utilized for each classifier. The findings indicated that the SVM classifier achieved the highest accuracy at 90.95%, followed by LR at 89.01%, while the NB classifier achieved 84%. In the same pipeline, an experiment was conducted using the TF-IDF technique, revealing an enhancement in accuracy for some methods. The SVM classifier again attained the highest accuracy at 92.34%, LR achieved 87.22%, surpassing NB by 0.44%. Utilizing the combination of N-grams (1, 2) vectorizers, the SVM classifier demonstrated the highest accuracy at 91.11%. LR followed with 89%, and 86.67% for NB. In the final experiment, TF-IDF was combined with (1, 4) grams. The result achieved accuracies of 90.67%. Figures 3 presents a visual comparison of the methods’ performance using BoW, TF-IDF feature extraction, and illustrates that SVM attains the highest accuracy when TF-IDF, and (1, 2) grams were applied. Overall, our study investigated the performance of different ML models with various feature extraction methods for AA using a Twitter dataset and found that SVM achieved the highest accuracy of 92% with optimized feature extraction. Both LR and NB performed well in specific scenarios, highlighting the effectiveness of traditional methods with careful feature engineering. By employing strategic approaches such as using large datasets, minimizing preprocessing, and selecting features like BoW, TFIDF, and n-grams, we found that n-grams effectively captured unique writing styles Table 1 Accuracy results of NB, SVM, and LR classifiers Model

BoW %

TF-IDF %

N-gram (1, 2) %

TF-IDF +N-ram (1, 4) %

NB

84.55

86.78

86.67

88.28

SVM

90.95

92.34

91.11

90.67

LR

89.01

87.22

89.00

86.70

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Fig. 3 NB, SVM, and LR methods accuracies using BoW, TF-IDF, and N-grams

while TF-IDF distinguished between common language usage and specific stylistic choices. Compared to related studies, our SVM model using BoW and stylometric features outperformed Rabab’ah et al. [31], who achieved 68.67% accuracy, and Hossain et al. [32], who achieved 70% accuracy, though Chen et al. [23] reported a higher accuracy of 87% using BoW, POS, and Word2Vec features. Our NB model achieved slightly lower accuracy than Chen et al. [23] but outperformed Aborisade and Anwar [9] by 1.5%. Overall, SVM consistently demonstrated superior performance, especially with TF-IDF combined with n-grams, emphasizing the potential of tailored feature engineering to enhance model accuracy in AA tasks [22].

6 Conclusion AA tasks are crucial for analyzing various content types to ensure authenticity and detect fraudulent activities. In this chapter, we evaluated the performance of three ML classifiers on Schwartz et al. [34] Twitter dataset. It contains 1,000 tweets each from 50 users. We focused on NB, SVM, and LR with text-based features like BoW, TFIDF, and N-grams due to their established effectiveness. Notably, we also included TF-IDF with N-grams of length 1–4. Among the evaluated methods, SVM achieved the highest accuracy of 92.34% using TF-IDF features. Interestingly, LR reached a superior accuracy of 98.01% with BoW features, while NB 88.28% accuracy using a combination of TF-IDF and 1–4 N-grams. Overall, ML methods proved effective in uncovering complex textual patterns, with preprocessing minimizing enhancing model performance. Choosing between methods depends on project needs, resources, and the balance between accuracy and complexity, essential for robust social media analysis and content integrity enhancement. In future work, we plan to conduct experiments using more ML classifiers such as random forest or decision tree and

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using Deep Learning (DL) models such as CNN, RNN, and LSTM. Our objective is to compare the performance of more ML and DL models on AA take.

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Digital Storytelling: Developing Twenty-First Century Skills in Arabic Language Education Mahyudin Ritonga, Asrina, S. Purnamasari, Adam Mudinillah, Bambang, and Yayan Nurbayan

Abstract Digital Storytelling is an application of technology that is positioned to help educators overcome barriers to the use of technology in the classroom into a productive thing by creating stories or fairy tales digitally. Digital Storytelling can be used as a way to convey stories, both fiction and reality, which can be combined with images, text, audio and video. The purpose of this study is to determine the role of utilizing technological progress through Digital storytelling in education, especially in the field of Arabic. The method used is a qualitative method with an approach through literature studies (literature review). The form of application of Digital Storytelling in Arabic language learning is to tell popular stories that are adjusted to the cognitive level of students so that the material provided is easy to understand. The result of the research is that Digital Storytelling plays a role or has a positive impact on Arabic education and can also be used as an alternative in increasing students’ interest in learning, especially in Arabic subjects. Keywords Digital storytelling · Arabic language · Education 21st century M. Ritonga (B) Muhammadiyah University of West Sumatra, Padang, Indonesia e-mail: [email protected] Asrina Universitas Islam Negeri Imam Bonjol Padang, Padang, Indonesia e-mail: [email protected] S. Purnamasari Universitas Islam Kalimantan Muhammad Arsyad Al Banjari, Banjarmasin, Indonesia A. Mudinillah Sekolah Tinggi Agama Islam Al-Hikmah Pariangan, Banjarmasin, Indonesia e-mail: [email protected] Bambang Universitas Muhammadiyah Sumatera Barat, Padang, Indonesia Y. Nurbayan Universitas Pendidikan Indonesia Bandung, Bandung, Indonesia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_14

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1 Introduction As time goes by, the progress of the times in the field of technology is growing. The revolution reaches all aspects of life [1, 2]. Changes in one field affect changes in other fields because each other is an interrelated system. The revolution includes starting in the economic, social and also educational fields [3, 4]. Advances in technology are inevitable in life. Dynamic technological advances pioneered the birth of advanced technological products [5, 6] as a means of meeting human needs that are increasingly complex. In essence, technological progress and development are indeed indispensable. Technological advances spearheaded the birth of millions of brilliant innovations that would provide positive benefits for human life. If you look deeper into technological advances, it actually has many benefits, including making it easier, faster, and more efficient [5, 7] to activate a lot of work. But on the one hand, technological advances can have negative impacts, such as damaging the environment by producing technology that endangers the scarcity of human life. It is well realized that technological advances not only have a positive but also negative impact but all the impacts of these technological advances are what determines it. The twenty-first century is also often known as the digital era because, in the twenty-first century, there is a rampant and rapid development of technology and information [8, 9]. The development of increasingly modern technology makes everything can be reached easily and quickly. The development of technology and information leads us to various challenges [10, 11], who are ready to block the development of technology and information has an impact on the human mindset. The more technologically innovative, the more chaotic the human mindset in life. This is a challenge for twenty-first century humans to continue to struggle with the challenges of the times. The form of challenge that is now deeply felt is the number of jobs that are routine and continuous work so that they begin to be replaced with machines, both production machines and computers. Likewise, in the world of education, it also gets an impact from technological developments. Technological literacy is one way to face challenges in the twenty-first century [12] because developments in the world of education are very fast so they have to adjust to the curriculum in order to compete in the era of globalization. In addition, schools as one of the institutions that oversee education must be able to prepare students to face the real world which is full of challenges. Not to forget, educators must also understand the standards as educators through mastery of new technological models in the field of education. According to Law No. 2 of 1985, the purpose of education is to educate the nation’s life and also develop the whole person [13]. The whole person referred to as in the Law is, in addition to, a human being who is devoted to Allah Almighty, has a noble character, is physically healthy and also has knowledge and skills. One of the means of gaining knowledge and skills is at school. In general, schools are educational institutions designed as teaching efforts for students under the guidance of educators [14, 15]. Schools can be in formal and non-formal forms [16]. Formal schooling is a structured and tiered educational path consisting of Primary education, Secondary education, and Higher education. Meanwhile, non-formal education is an education

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path outside of formal education that can also be carried out in a structured and tiered manner. Indonesia itself has many types of education. One of them is religious education which also consists of several levels of education ranging from basic to higher education. Religious education, especially Islam, is under the auspices of the Ministry of Religion [17]. Kemenag stands for Ministry of Religion [18], which used to be the Ministry of Religion of the Republic of Indonesia or known as Depag, which is engaged in religious affairs. One of the urgent things in Education, which is also an element of Education, is the curriculum. The curriculum comes from the Greek “curir”, which means runner, as well as “curere”, which means a place to race [19]. The curriculum is a set of systems of plans and arrangements regarding learning materials that are used as guidelines in the learning process [20, 21]. Director of Curriculum, Facilities, Institutions, and Student Affairs (KSKK) Madrasah A. Umar said that Madrasahs, both Ibtidaiyah Madrasah, Tsanawiyyah Madrasah, and Aliyah Madrasah, use the new curriculum for Islamic Religious Education and Arabic [22]. The Ministry of Religion has issued KMA No. 183 of 2019 concerning the Curriculum for Islamic Religious Education and Arabic in Madrasah [23]. In addition, KMA 184 of 2019 was also published concerning guidelines for Curriculum Implementation in Madrasah [24]. Regarding the curriculum of the Ministry of Religion and the Ministry of Education and Culture, of course, there are significant differences. The Ministry of Education and Culture houses the curriculum of general subjects, while the Ministry of Religion houses the Islamic Religious Education curriculum consisting of Fiqh, Quran Hadith, History of Islamic Culture, Akidah Akhlak and also Arabic. Although there are differences in the general curriculum and religion, one goal is still to educate the next generation of the nation. Arabic is one of the subjects contained in the Madrasah curriculum. Starting from the elementary, middle, and high levels. If you look deeper, Arabic has been included as part of the curriculum since 1964 until now [25]. In 1964, 1947, and 1984 curricula, Arabic was taught with a partial approach [26], meaning that in the curriculum, there was each subject between elements of language and skills such as nahw, shraf, balaghah, adab and Arabic itself with different themes. In 1994 it was already seen that the curriculum was taught with a unified approach between language elements and language skills, and in 2004, Arabic language learning was directed at mastering the four maharahs or skills. On the other hand, in the curriculum of 1964, 1974 and 1984, the time for learning Arabic is very much. Unlike the previous year’s curriculum, in 1994 and 2004, the time for learning Arabic was very short, with only three to four meetings with a duration of 45 min. The curriculum changes are based on the awareness that the developments and changes that occur are relatively adapted to the needs and also the changes caused by global changes and the development of science and technology. Arabic language learning is learning aimed at encouraging, developing, guiding abilities and fostering a positive attitude towards Arabic [27]. The ability that is the goal of learning Arabic is receptive and productive ability [28]. Receptive ability is the ability to understand the speech of others [29] and also to understand readings. The productive ability is the ability to practice Arabic [30]. In addition, Arabic learning

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in Madrasah also aims to develop the ability to interact with Arabic both orally and in writing [31]. The ability includes four skills (maharah) in Arabic. The four skills include listening skills (istima’), speaking skills (Kalam), reading skills (qiraah), and writing skills (kitabah) [32]. These four skills are the main things learned in Arabic. Arabic language learning in Madrasah is also aimed at fostering students’ awareness of the importance of Arabic as one of the international languages that is the main tool in communicating, especially to face the challenges of the times in the era of globalization. However, in general, Arabic language learning in Madrasah is prepared for the achievement of basic language competencies that include four language skills [33]. The four important pillars in Arabic or in Arabic speech are called Al-Maharat Al-Lughowiyah. Maharah Istima’ or listening skills, are basic skills that language learners must have because listening is the first means of communicating [34, 35]. Listening or listening skills are individual abilities in digesting or understanding words and sentences spoken by the interlocutor. Listening skills (maharah istima’) are included in language skills that are actively receptive to listening skills. The individual must activate his mind to be able to identify, understand, and interpret the meaning and sound of the language conveyed by the interlocutor. According to Tarigan, listening is an activity of listening to oral symbols that are carried out deliberately, attentively, accompanied by understanding, appreciation and interference to obtain messages, and information, understand the meaning of communication and respond to contained in the oral symbols that are listened to [36, 37]. In general, listening skills are the skills of listening to what is said verbally accompanied by understanding so as to achieve an understanding of the intentions of the interlocutor. Speech skills (maharah kalam) are the ability to express articulated sounds or words to express thoughts of, ideas, and opinions to the interlocutor [38]. Once good at listening skills, learners will enter the kalam or speak stage. Kalam learning aims to enable learners to be able and able to communicate well in Arabic. At the same time, writing skills (maharah kitabah) are the ability to describe or express ideas, and opinions in the form of writing, ranging from simple aspects to complex aspects [39]. Acef Hermawan defines maharah kitabah as the ability to describe or express the contents of the mind [40]. Our maharah learning starts from the knowledge of how to write, connect letters, write words and sentences, and write without looking at the text to the point of pouring thoughts or ideas into writing. According to Amin Santoso, the purpose of studying maharah kitabah, in general, is to copy the sounds of letters, words, phrases, and sentences [39] by paying attention to the rules of correct writing in order to be able to express ideas in writing and tell stories in writing the messages in the text [40]. Reading skills (maharah qiraah) is the ability to read Arabic texts fluently in accordance with the makhrijul of letters and the proper rules of Arabic [41]. Maharah qiraah must be based on the ability to speak (maharah kalam) and the ability to hear (maharah istima’). The four maharahs are the basic things or pillars that must be possessed in learning Arabic. However, it is very unfortunate if the students are not interested in learning Arabic with alibis that are difficult to understand, boring, etc. Therefore, the use of technological advances in the field of education is

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very necessary. One of them is the use of Digital Storytelling as a creative learning medium. Digital Storytelling or commonly abbreviated as Digital Storytelling, is one of the breakthroughs in learning media that educators can use to present more interesting learning. Digital Storytelling is a way to tell a story through a computer application, such as video or animated film [42]. There are many advantages offered by using digital storytelling. In addition to its attractive appearance, educators can also predict the topics to be discussed, as well as increase the confidence of educators to publish works in Arabic learning. So that learning becomes more interesting, and students are enthusiastic and can better understand the subject matter presented.

2 Method The research method used is research with a qualitative approach [43] through a Literature study. Researchers use qualitative research because of data sources and literature research results in the form of descriptions of words [44]. Qualitative research is a type of research whose results are not obtained from statistical data or in other forms of calculations. Qualitative research seeks to understand the funds of interpreting the meaning of an event or human interaction in a particular situation according to the perspective of the researcher [45]. The research conducted with the object of study uses library data, namely by reading, studying and analyzing various existing literature. The research stage is carried out by collecting literature sources, both secondarily. Research with the library research method or library study is carried out in several ways. Researchers use keyword searches according to the topic of discussion to obtain information about the problems discussed. Then search for research subjects and proceed with the search for books and scientific articles with the latest references. Researchers search for sources in various existing literature by looking for existing scientific sources to be used as references. Researchers use library studies because, in addition to being ready to use, Library data is also not limited by space and time.

3 Result and Discussion Digital Storytelling or simply interpreted by storytelling digitally [46, 47] namely by combining video, audio, images and text to convey stories and information. Digital storytelling is one of the powerful ways to stimulate students’ interest as well as a way for students to be connected to all areas of literacy [48, 49]. Storytelling has conditions that change every year. The purpose of storytelling is to meet the basic needs of individuals and society. Humans are social beings who coexist with each other with many events that occur in life. The number of events that occur in life makes humans unable to walk alone. Human beings impeach others so as to make them equal to the events they experience, which is conveyed in the form of a story.

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So that other individuals can understand each other, they tell the story with full expression, imagination, impression, trust and hope. Storytelling is also the art of conveying stories, both reality and fiction or real with the help of writing, sound, and images through the art of storytelling [50]. Short story stories set in bosa technology background are also called digital storytelling, which is in usual, digital storytelling is personal or varied factual material. According to George W. Burns, it is stated that telling stories has its own benefits [51], namely: a. b. c. d. e.

Fostering discipline Develop emotions Provide inspiration Make changes Improves cognitive and physical strength.

In ancient times storytelling had become a habit. For example, a child who falls asleep by listening to stories from his mother [52]. While listening to other people’s stories, an individual will function the sense of sight as well as the sense of hearing. Digital storytelling has several advantages. This method can be applied because it is biased to accommodate various learning techniques, develop activeness and motivate students in the lessons taught. In addition to the advantages of digital storytelling, it also has disadvantages, namely the need for computer mastery and recognition of characters by mentioning the identity of the so-called biography and the advantages of the character so that they choose the character. Not to forget, in the application of digital storytelling, it is also necessary to pay attention to the accuracy of speech or pronunciation, word choice, fluency, topic mastery, pressure placement, backsound and use of images. According to Engel, in applying this method, there are seven things that must be considered, namely point of view, dramatic question, emotional content, voice, soundtrack, economy, and pacing [53]. As for making digital storytelling media, broadly speaking, there are three stages that must be passed, namely, the planning stage, the production stage, and the presentation stage [54]. Storytelling activities have several positive things in their application. Positive things that can be obtained from storytelling activities include [55]: Other: a. Educating the delinquents Through digital storytelling, indirectly, listeners can find out the value or meaning hidden in a story. The positive meaning in the story must be conveyed to the listener so that they can take lessons from the story we tell. b. Improves cognitive abilities After understanding all the messages contained in the story conveyed, it is hoped that listeners will be able to realize them in their lives to avoid negative things. Based on the theory, the story is able to change the mood of the feelings of the person who

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hears it. Meanwhile, on the other hand, psychomotor, after hearing the story, there is a tone of demand in oneself to interpret the good teachings contained in the story. c. Honing focus When listening to the story, an individual will focus on the story being told in order to understand the storyline well. d. Honing imagination and creativity When listening to a story or when a story is in progress, listeners usually often fantasize according to what is told. And some of the listeners made the story they heard into a picture and writing. e. Practice speaking and listening skills This will be felt by the person who is abusive because by telling stories, you also train your speaking and listening skills so that it becomes a complex language and also becomes a listening or istima’ exercise for listeners. f. Fostering interest in reading Through storytelling activities, it fosters interest and a sense of awareness or curiosity. The higher the sense of straight-stemmed appendage with the high interest in reading books. Thus, storytelling is one way to foster the reading interest of students and listeners. The author classifies the introduction of storytelling into several parts, namely: A. Implementation of the Teaching and Learning Process Wulandari, Adhitya, Paramita, Astuti and Susilo organized literacy and storytelling classes to increase and foster students’ interest in expressing themselves [2]. From the research, the results were obtained that the implementation of successful storytelling activities made participants interested in what was presented by the researcher. Meanwhile, Krisnawati and Julianingsih, in their research, suggest that there are differences between learning that uses digital storytelling and learning that uses storytelling as a learning medium. The existence of this difference is marked by the increasing value of motivation to learn from the student by using storytelling media. These results were obtained from student learning motivation before the use of storytelling media by 28.44%. Meanwhile, after using storytelling media, a motivational value of 47.48% was obtained. From here, the bias is seen that students’ motivation is higher, and they are more interested in using digital storytelling media. According to Pratiwi, who has applied learning using storytelling to students in grade II students of SDN S4 Bandung, the results that learning using digital storytelling techniques can improve students’ speaking skills by up to 87% [56]. Other researchers argue that storytelling is another initiative to support the teaching and learning process in the twenty-first century. Participants who become consumers of digital storytelling will be better able to process and compile useful digital content. In his analysis, Tanjung mentioned the application of storytelling in gameplay which is told first in the plot of the story. This can improve historical and

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cultural learning. Furthermore, based on research conducted by Asri, Indrianti, and Perdanasari, applying the digital storytelling method to English learning in the Informatics department is able to support and improve student abilities [57]. On the other hand, the research results of Hannele Niemi, NIU Shuanghong, Mariana Vivitsou, and Li Baoping concluded that digital storytelling could be used as a method to increase the interest in youth literacy, especially in children. Another benefit felt from digital storytelling is that it can also be a guide for educators to make digital storytelling a learning medium in schools. Through digital storytelling, not only do educators provide stories, but students are also biased to make a story in accordance with their own version. The same is felt day by day, the depletion of the students’ literation. So it is the task of the teacher to make a new breakthrough in facing the changing in the education system that is adapted to the progress of the times and technology. One way is to make digital storytelling a place to attract students’ reading interest again. A researcher Helen Barret suggested that digital storytelling be applied in educational institutions. Because from his point of view, the holding of digital storytelling in educational institutions will have a positive impact on student learning outcomes. In addition, the existence of digital storytelling can help and motivate educators to improve learning strategies. In the research conducted by Atiqah, Titien, and Nancy on the application of storytelling in learning English in Information Management Studies, it was found that students who are students can make storyboards about the content of videos that will be made quite well because some people have previously learned to link storyboards [58]. Story boards that can be derived from drafts per scene or also known as video frames, are accompanied by an explanation of the idea of content and duration plan. The research conducted by the tutor stated that teaching English in the Information Technology study program using storytelling media can be done well. In addition, students also feel that they have received an increase, especially in the field of information technology. Ribeiro’s research has resulted in the fact that the media is able to support educators to be more creative in making learning media. The tutor stated that digital storytelling provides reflective benefits to students who do not have competence in the field of writing [59]. Novari, Ardini, Rostiana, Meliyawati, Widiatmoko, Rohimajaya, Gumelar and Sauri carried out storytelling activities in Meldasari Village [60]. From this activity, it was found that more than 50% of the children in the village had good grades. In the application of the digital storytelling method in the skill of telling idol figures of subjects Indonesian grade VII students of SMP Negeri 1 Kedamen, Gresik conducted by Wina Heriyana, Irena Y, Maureen before the implementation of the research was carried out in several stages, starting from the preparation stage which also consisted of five research steps [61], they are preparing research classes, research time, and compiling lesson plans, the next step is to introduce students to digital storytelling and make educators prepare examples of digital storytelling. Then proceed to the research implementation stage with several meetings and the post-field stage to report the results of the research by conducting data analysis and getting the results that it is known that there are 29 students participating in the learning and it can be concluded that the application of digital storytelling learning

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was carried out well during two meetings and from this application produced story drafts, storyboards and digital storytelling results. Furthermore, in a study conducted by Endang Sulistianingsih on the effectiveness of a digital fable-based learning model in improving the emotional intelligence of students, it was concluded that the learning model using digital fable or digital storytelling is effective in improving the emotional intelligence of students in elementary schools in Tegal Regency [62]. This is evidenced by the increase in the average pre-test value of 121.78–134.5 in the post-test, which increased by 12.72. From the results of the SPSS calculation through the calculation of the pre-test and post-test t-test, a significance of 0.000 was obtained with a significance of 5%. From that fact, it can be concluded that there are significant differences in the intelligence of students before after learning using digital storytelling B. Availability of content in internet search There are several contents or storytelling content that can be used as a reference and are booming among young people [63]. 1. Blog Video In this age of technological progress, video blogs are one of the interesting and booming contents in the hearts of various groups, especially young people. There are several video blog contents that are currently trending, namely: a. Daily vlogs containing the creator’s activities for a whole day b. Tips and tricks that contain content that educates about ways to do things in a simpler and more interesting way. c. Beauty and lifestyle are also one of the vlog video content that many young people love, especially women. Because in the vlog, video content will be presented about beauty, one of which is about how to dress up, skincare or what is known as skincare and body care both inside and out. d. This content food vlog is no less competitive than other vlog content. The reason is that this culinary content will be presented all the ins and outs of culinary that will attract attention and arouse the taste buds e. Children’s Vlogs that contain children’s content in their daily lives. f. Entertainment Vlogs that contain content about music, hobbies, as well as jokes and so on. Many of the video blog content is accompanied by stories from creators, even though most of them use stories from creators. Not infrequently, creators also provide positive energy to the audience. As much as 46.6% of the influence of digital storytelling on Youtube channels is increasing the desire to learn viewers [64]. When watching an interesting video, that is where the audience will pay attention and can last for hours to listen, watch and follow the storyline from the crew to the end. Likewise, with the habit of reading texts that sometimes creators also insert in their content. The existence of vlog content that is presented creatively and innovatively, that discusses various things that cover almost all aspects of life can

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affect the behaviour of the audience. But sometimes, what is obtained is considered unsatisfactory, so the audience looks for other information in various ways. 2. Podcast Podcasts are one of the media that has also recently boomed. Podcasts are media that use voice. This media is new that has only been known to the public since 2014 [65]. Until now, podcasts have become something familiar and have become a trend in society. According to Fadhilah, Yudhapramesti, and Aristi podcast is an audio or video that can be searched on the internet in the form of premium or free applications. Although it is something new, the existence of this podcast is very easy to accept in society, especially in the millennial generation, because it is wrapped in such a beautiful way that it makes it attractive. This was also strengthened by research conducted by Bongry, Cizaldo and Kalnbach, who provided podcasts to students at universities in the United States. Not long after that, podcasts spread throughout the world, one of which was Indonesia. Originally the podcast was available on Spotify in 2018, and after that, it went viral. In 2020 Indonesia became the first-ranked podcast listener in Southeast Asia [66]. Podcasts are divided into three forms, namely podcasts in MP3 format, podcasts in MP4 format, and audiovisual podcasts like on Youtube [67]. Different creators also differ in the way the content is presented. In the era of the onslaught of technological advances, podcasts are one of the new pillars of utilizing digital storytelling. Podcasts are designed from various topics that can be freely accessed anywhere and anytime. And in this day and age, the spread of podcasts has spread widely on several platforms such as Spotify, Anchor, YouTube, as well as other music applications. Indonesian podcast producer Lisa Siregar stated that podcasts seem to attract listeners to listen to podcasts. Podcasts are something that is commonplace in society as a means to get knowledge or news that was biased in the past. According to Margaret Hurley, the absorption of podcasts and the ratio in learning for participants becomes more practical and exciting, making students easy to accept, understand, and digest what is conveyed [68]. Things that Support digital Storytelling to Raise the Spirit of Youth Literacy [69]: a. It’s cool and not boring. Learning materials that use digital storytelling are packaged as attractively as possible to have the most interesting impression possible. It is very different from reading a book that is only focused on reading writings that make students bored. Meanwhile, with digital storytelling, students will be able to enjoy and listen to what is conveyed more. b. Unattached. Not bound or free means anyone can create content without the need for content selection or official content selection. So that anyone can be creative according to their own imagination. By using this digital storytelling, everyone can share their knowledge and experiences. Nevertheless, all must be in accordance with the applicable rules. Vice versa, audiences can freely choose what content they want to listen to without seeing who the creator is. c. Realistic. It is mentioned as realistic because the audience instantly knows the intonation of the audio as well as the expressions supporting the story directly.

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So that it makes the story feel real and the audience can understand well the storyline conveyed. d. Needs. For young people nowadays, digital media is a necessity. Nowadays, anything will be searched on social networks or the internet. It is also possible to fill their empty time surfing on cyber platforms as a substitute for entertainment. From this, digital storytelling has a greater opportunity to attract the attention of young people. It is undeniable that although they will prefer something instantaneous yet they will be much more interested in something in which there is a story. e. Develop Literacy and listening skills. Young people who participate in the world of digital storytelling will certainly not be unfamiliar with the language and speaking skills. f. It’s easy to remember. Favourite content will tend to be easier to remember and stored in long-term memory. Usually, these stories are humorous stories or sad stories, or memorable stories. Experts agree that emotional intelligence is not derived from derivatives [70], but it can be developed. On the other hand, Dessy Wardiah, after conducting research, argues that storytelling can affect students’ writing, reading and emotional intelligence. Not only that, but the characters in the story also inspire students with equalized kindness and values. Meanwhile, Asfandiyar argues that storytelling is the art of storytelling which acts as a place to cultivate values without the impression of patronizing students. Digital storytelling, it provides a golden opportunity for students to be able to learn to make their own version of digital storytelling. Through this process, it can develop the ability of participants to communicate and compiling ideas brilliantly.

4 Conclusion Based on research that has been carried out by researchers using the literature study method, it can be seen that digital storytelling is an application of technology that is positioned to facilitate work, which includes the collaboration of videos, images, or text to convey stories and information. In addition, it can be concluded that digital storytelling can be used as a medium and also a new strategy in learning. In addition to being a stimulus for students, digital storytelling can also make the learning atmosphere in the classroom more enjoyable. Even so, this study does not claim absolutely that digital storytelling is a method or medium that guarantees the improvement of participants’ learning in the past. However, this research only intends to show that in this era of technological and information advancement, digital storytelling can be used as an alternative to new breakthroughs in the world of education. In addition, this research is also aimed at providing knowledge that digital storytelling can arouse literacy interest in the younger generation and also as an encouragement for educators to be more creative in presenting learning media.

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5 Suggestion The researcher hopes that the younger generation can take advantage of the sophistication of technology, especially in the world of education and also in other fields, to provide benefits to the surroundings as a motivation. For educators, they can try to apply digital storytelling as a medium and strategy in learning to stimulate students’ literacy interests.

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Effective Techniques in Lexicon Creation: Moroccan Arabic Focus Ridouane Tachicart, Karim Bouzoubaa, and Driss Namly

Abstract Natural language processing (NLP) has seen significant advancements due to the growing availability of data and improvements in machine learning techniques. A critical task in NLP is lexicon creation, which involves developing comprehensive and accurate dictionaries of words and their meanings. Traditional methods, such as manual creation or expert acquisition, are often time-consuming and limited in scope. This paper explores the challenges and best practices in lexicon creation and presents a case study on developing a Moroccan Arabic lexicon using a hybrid approach that combines manual annotation and machine learning. We address issues of subjectivity, ambiguity, data quality, scalability, and the ethical implications of lexicon creation. The case study demonstrates the hybrid approach’s effectiveness in enhancing lexicon accuracy and coverage, emphasizing the balance between manual annotation and machine learning. This paper provides valuable insights for NLP practitioners and researchers, showcasing efficient and effective lexicon creation techniques. Keywords Lexicon creation · Moroccan Arabic · Natural language processing · Machine learning · Orthographic variation · Lexical resources

R. Tachicart (B) LARGESS, FSJES El Jadida, Chouaib Doukkali University, El Jadida, Morocco e-mail: [email protected] K. Bouzoubaa · D. Namly Mohammadia School of Enginners, Mohamed V University in Rabat, Rabat, Morocco e-mail: [email protected] D. Namly e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_15

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1 Introduction In recent years, the field of natural language processing (NLP) has witnessed tremendous growth and advancement [1], thanks to the increasing availability of large-scale datasets and the development of powerful machine-learning algorithms. One critical aspect of NLP that has received significant attention is lexicon creation. Lexicons are comprehensive databases of words and their meanings, organized into structured formats, and play a vital role in many NLP tasks, including machine translation, sentiment analysis, and speech recognition. The need for lexicon creation arises from the necessity for a deep understanding of language and its nuances in various NLP tasks. With the explosion of social media and other web-based platforms, there is an enormous amount of unstructured textual data that requires effective processing and analysis [2]. Consequently, researchers and practitioners have recognized the importance of developing lexicons specialized for different domains, languages, and applications. Traditionally, lexicon creation has relied on manual efforts by linguists and lexicographers. However, the advent of large-scale data and advancements in machine learning have revolutionized this task [3]. Data-driven approaches now enable the automatic creation of lexicons by analyzing vast amounts of textual data to extract insights about word usage, semantic relationships, and other linguistic properties [4]. Despite these advancements, several challenges remain, such as ensuring data quality and avoiding linguistic or cultural biases in the resulting lexicons. This paper explores the challenges and best practices in lexicon creation, presenting a case study on developing a Moroccan Arabic lexicon using a hybrid approach that combines manual annotation and machine learning techniques. Section 2 explores the methods involved in creating effective lexicons, followed by identifying related challenges in Sect. 3. Section 4 presents best practices for quality control, bias reduction, ethical considerations, and interoperability, and discusses the use of machine learning algorithms and crowdsourcing for automated lexicon creation. The case study method for creating a Moroccan Arabic lexicon and the effectiveness of the hybrid approach in improving lexicon accuracy and coverage are detailed in Sect. 5. The paper concludes with a comprehensive understanding of lexicon creation and its implications.

2 Lexicon Creation Techniques Lexicon creation is an important component of natural language processing tasks [5, 6]. It involves the development of a structured database of words and their features such as meanings, forms, morpho-syntactic tags, and relationships, which serves as a foundational resource for various NLP applications such as machine translation, morphological analysis, and information retrieval [7]. With the increasing

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availability of big data and advances in artificial intelligence, new emerging techniques for lexicon creation can overcome the limitations of traditional approaches [8]. Machine learning algorithms such as word embeddings [9] as well as crowdsourcing [10] are some of the methods that can be employed for lexicon creation. Additionally, social media data can serve as a valuable resource for constructing and enriching lexicons [11, 12]. The choice of technique and data source depends on the specific requirements and constraints of the lexicon creation task.

2.1 Manual Annotation Manual annotation is a traditional approach to lexicon creation that involves human experts annotating a corpus of text to identify and define the relevant terms [1]. This approach is particularly useful for creating domain-specific lexicons, where experts have knowledge of the relevant vocabulary [2]. The manual annotation process is usually carried out by trained annotators who carefully review the text and assign labels to each term based on their understanding of the context and the meaning of the term [13]. Manual annotation can result in high-quality lexicons with precise definitions and can capture subtle nuances and context-specific meanings of terms especially when developing Gold Standards [3]. However, it can be time-consuming and labor-intensive, especially for large lexicons [4], and is prone to subjectivity and inconsistency, as different annotators may have different interpretations [5]. To illustrate this approach, we can cite the work of [14]. In this work, authors proposed NileULex [7], which is a sentiment lexicon that includes both wordlevel and phrase-level annotations for Egyptian and Modern Standard Arabic. Their approach involves manual annotation of sentiment polarity labels for words and phrases extracted from a large corpus of text. The resulting NileULex contains more than 17,000 word-level entries and 6,000 phrase-level entries, covering a range of domains and topics.

2.2 Machine Learning Machine learning-based methods are an increasingly popular approach to lexicon creation that uses algorithms to automatically extract and classify terms from a corpus of text [6]. These methods can be supervised, unsupervised, or semi-supervised, depending on the availability of annotated data. Supervised methods require labeled data, where each term is annotated with its corresponding label, to train a model to classify new terms. Unsupervised methods do not require any labeled data, and instead, rely on clustering or other techniques to group terms with similar characteristics. Semi-supervised methods combine both approaches by using a small amount of labeled data to train a model and then using it to label the remaining data automatically. Machine learning-based methods can handle large volumes of data quickly

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and efficiently, generalize to new domains or languages, and be less subjective and more consistent than manual annotation. However, they may not capture the subtle nuances and context-specific meanings of terms as well as manual annotation and may require significant computational resources and expertise to implement and fine-tune the models. One work that falls in this approach is where authors built MoArLex [15] which is an Arabic sentiment lexicon using automatic lexicon expansion techniques. To this end, the authors used automatic lexicon expansion using the NileULex lexicon and word embedding. In total, MoArLex contains over 26,000 Arabic words that have been annotated with their sentiment polarity.

2.3 Corpus-Based Generation Corpus-based generation is a semi-automatic technique used to create a lexicon by analyzing a large corpus of text [16]. The process involves identifying and extracting relevant terms from the corpus and then filtering and refining the list to create a final lexicon. Corpus-based generation is widely used in natural language processing (NLP) applications such as text information retrieval, classification, and sentiment analysis [17]. There are many steps involved in the corpus-based generation to create a lexicon. It starts with corpus collection to collect a large corpus of text that is relevant to the domain or topic of interest. Once the corpus is collected, it needs to be preprocessed to remove noise and irrelevant information. The next step is to identify the relevant terms in the corpus. This can be done using techniques such as frequency analysis, part-of-speech tagging, and named entity recognition. After identifying the terms, they need to be filtered and refined to create a final lexicon. This can be done using various techniques such as removing duplicate terms, filtering out irrelevant terms, and manually reviewing the list to ensure accuracy and completeness. The final step is to validate the lexicon to ensure that it is accurate and relevant to the domain or task. This can be done by evaluating the lexicon using various metrics such as precision, recall, and F1 score. One of the main advantages of corpus-based generation is that it can be used to create a lexicon that is specific to a particular domain or task. It also allows for the identification of new or emerging terms that may not be present in existing lexicons. However, it can be time-consuming and requires a large corpus of text to be effective. In this direction, the work of [18] proposes a corpus-based approach to create a sentiment lexicon for the Arabic language. The authors use a large corpus of Arabic text to identify words and phrases with positive or negative sentiments. They also consider the effect of negation and intensifiers on sentiment, which is common in the Arabic language. The lexicon is evaluated using a dataset of Arabic social media content and is found to perform better than other Arabic sentiment lexicons created using manual annotation or automatic methods alone.

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2.4 Crowdsourcing Crowdsourcing is another technique used for lexicon creation, especially for underresourced languages [19]. This method involves outsourcing the annotation task to a large number of individuals, usually through online platforms [11]. Crowdsourcing can be a cost-effective and scalable way to annotate large amounts of data quickly, especially for tasks that require subjective judgments, such as sentiment analysis or emotion classification. Crowdsourcing can also provide a diverse range of perspectives and reduce the risk of bias that may arise from relying on a single annotator. However, the quality of the annotations may vary widely, depending on the expertise and motivation of the crowd workers, and the quality control mechanisms may require significant resources to implement effectively [20]. In addition, crowdsourcing may not be suitable for all types of lexicon creation tasks, especially those that require specialized knowledge or domain expertise. In general, the crowdsourcing approach can be a useful tool for creating lexicons at scale, but it requires careful planning, monitoring, and evaluation to ensure the quality and reliability of the results. Following this approach, the work of [21] proposes a crowdsourcing method for lexicon acquisition. The authors introduce the concept of “pure emotions” and develop a lexicon of such emotions that can be used for sentiment analysis. The lexicon is built by crowdsourcing emotional associations with various words, and the resulting emotions are classified as positive, negative, or neutral. The work also evaluates the effectiveness of the pure emotion lexicon in sentiment analysis and finds that it outperforms traditional lexicons. Overall, lexicon creation is an essential task in NLP applications, and there are several approaches to it, each with its advantages and limitations. Manual annotation can result in high-quality lexicons but is time-consuming and subjective. Machine learning-based methods can handle large volumes of data quickly but may not capture the nuances of language as well as manual annotation. Hybrid approaches can leverage the strengths of both but may require significant resources to implement. Corpus-based generation and crowdsourcing have their advantages and limitations as described above. The choice of approach will depend on the specific requirements of the application and the available resources.

2.5 Hybrid Approach The hybrid approach to lexicon creation integrates manual annotation, automated processes, and machine learning techniques, harnessing the strengths of each to create a more robust and comprehensive lexicon [8]. In this approach, human experts annotate a subset of the data to train a machine-learning model, which can then be used to annotate the rest of the data automatically [9]. This approach can combine the precision of manual annotation with the scalability of machine learning, reducing the subjectivity and inconsistency of manual annotation [10]. The hybrid approach

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can be customized to specific domains or languages and can result in high-quality lexicons. However, it may require significant resources and expertise to implement and fine-tune the hybrid models, and it may still be affected by the quality and representativeness of the training data. Overall, the hybrid approach can be a powerful tool for creating lexicons that are both accurate and scalable. One example of a work that uses the hybrid approach is the work of [22]. The authors propose a hybrid approach for creating a sentiment lexicon specific to healthrelated content. The authors combine manual annotation by health experts and automatic methods based on machine learning to create a lexicon containing positive and negative health-related terms. The lexicon is evaluated using a dataset of healthrelated tweets and is found to outperform other lexicons created using either manual annotation or automatic methods alone. The study demonstrates the effectiveness of combining human expertise with machine learning-based methods for sentiment analysis in the context of health-related content.

3 Challenges and Best Practices Lexicon creation is a critical task in natural language processing (NLP) that involves developing comprehensive and accurate dictionaries of words and their meanings. Lexicons are essential for many NLP applications, such as named entity recognition, machine translation, and sentiment analysis [21]. However, lexicon creation is a complex and challenging task that requires careful attention to several factors. In this section, we will explore some of the key challenges and best practices in lexicon creation and discuss possible solutions.

3.1 Challenges • Subjectivity and ambiguity One of the main challenges in lexicon creation is the subjectivity [23] and ambiguity [24] of natural language. Many words and expressions have multiple meanings and connotations depending on the context and the speaker’s intention. This makes it difficult to develop clear and consistent definitions for each word in the lexicon. Moreover, there may be differences in interpretation and usage across different regions, dialects, and cultures. To address this challenge, lexicon creators need to develop comprehensive annotation guidelines that capture the nuances and complexities of the language and account for cultural and linguistic diversity. • Quality of the data Another challenge in lexicon creation is the quality of the data. The accuracy and completeness of a lexicon depend on the quality and representativeness of the

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underlying data [25]. However, digital text data is often noisy and heterogeneous, containing misspellings, grammatical errors, slang, and other forms of non-standard language. In addition, digital text data may not be representative of the target population, especially if it is collected from social media or other online sources. To address this challenge, lexicon creators need to use domain-specific sources of data to improve the accuracy and coverage of the lexicon and develop preprocessing and cleaning techniques that can filter out irrelevant or low-quality data. • Interoperability Interoperability is a major issue in lexicon creation, as different NLP systems may require different formats, sizes, and content of lexicons. The lack of interoperability can make it difficult to reuse and integrate existing resources, resulting in significant duplication of effort and reduced efficiency [26]. To address the interoperability issues, it is important to consider standardization, modularization, and metadata as key solutions. Standardization of lexicon format and content can ensure consistency and compatibility with other NLP resources, while modularization can enable the creation of lexicons with different levels of granularity to meet the varying requirements of different applications. Additionally, including metadata in a lexicon can provide important contextual information about the lexicon and its contents, facilitating its wider use and integration into different NLP systems. • Scalability and efficiency Another challenge in lexicon creation is the scalability and efficiency of the annotation process [27]. Traditional manual annotation methods can be time-consuming and labor-intensive, and may not be feasible for large-scale lexicon creation tasks. Machine learning-based methods can help to automate the annotation process, but they require large amounts of labeled data to train accurate models. To overcome this challenge, researchers are exploring semi-supervised [28] and unsupervised machine learning techniques [29] that can learn from partially labeled or unlabeled data, and active learning strategies that can select the most informative samples for annotation. • Privacy and ethical concerns Finally, lexicon creators need to consider the ethical and social implications of their work. Lexicons can be used for a variety of purposes, some of which may have unintended consequences, such as reinforcing stereotypes, perpetuating biases, or infringing on privacy rights. To address this challenge, lexicon creators need to develop ethical guidelines [30] and evaluate the potential impact of their lexicons on different stakeholders and communities. In conclusion, lexicon creation is a complex and challenging task that requires careful attention to several factors. By developing comprehensive annotation guidelines, using domain-specific sources of data, and leveraging machine learning and other innovative techniques, we can create lexicons that are both accurate and scalable. Moreover, by considering the ethical and social implications of our work, we can ensure that our lexicons are used responsibly and for the benefit of society.

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4 Case Study: Moroccan Arabic Lexicon Creation Creating lexicons for a low-resource language such as Moroccan Arabic can be a particularly challenging task due to the limited availability of annotated data and linguistic resources. In this section, we present a case study that focuses on the creation of a lexicon for Moroccan Arabic, a dialect of Arabic spoken in Morocco. The case study outlines the methodology used to collect and annotate the data, the challenges encountered during the creation process, and the evaluation of the resulting lexicon.

4.1 Overview of Moroccan Arabic and Its Linguistic Features Moroccan Arabic, also known as Darija, is a dialect of Arabic spoken in Morocco by approximately 35 million people. It is the most widely spoken language in the country and has a unique set of linguistic features that distinguish it from other Arabic dialects. One of the most notable features of Moroccan Arabic is its use of Tamazight, Spanish, and French loanwords. Tamazight, a language spoken by indigenous populations in North Africa, has had a significant influence on Moroccan Arabic vocabulary. Similarly, due to the country’s historical ties to France and Spain, many French and Spanish words have been adopted into the Moroccan Arabic lexicon. The following table illustrates a sample of Moroccan Arabic vocabulary. Table 1 presents a selection of Moroccan Arabic vocabulary, showcasing its diverse origins.

4.2 Data Collection and Preprocessing To create the training dataset, we utilized the Moroccan UGT corpus (2.1 million words), composed in Arabic script and gathered in a prior study [31]. We augmented the original data by incorporating texts sourced from Moroccan websites and blogs, resulting in a total of 3.6 million words. To ensure uniformity, we employed an automated normalizer to remove numbers, special characters, and non-Arabic letters.

4.3 Machine Learning Integration In this section, we describe the specific machine learning models employed in the creation of the Moroccan Arabic lexicon, their parameters, the rationale for choosing them, and the performance metrics used to evaluate their effectiveness. For the task of creating a lexicon of Moroccan orthographic variations (OV), we selected FastText [32], a library for efficient text classification and representation

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Table 1 Sample of Moroccan Arabic lexicon

MA word ‫ﻓﺎﻟﺼﻮ‬ [fAlSw] ‫ﺑﺮﯾﻜﻮﻻج‬ [brykwlAj] ‫ﻣﺶ‬ [m$] ‫ﻛﺘﺐ‬ [ktb] ‫ﻛﻼ‬ [klA] ‫ﻛﻮﻧﺠﻮﻻ‬ [kwnjwlA] ‫زﻛﺎ‬ [zgA] ‫ﺑﻮﻣﺎﺿﺔ‬ [bwmADp] ‫ﻟﺤﻢ‬ [lHm]

Word origin

Language origin

English translation

falso

Spanish

Scammed

bricolage

French

DIY

‫ﻣﻮش‬

Tamazight

Cat

‫ﻛﺘﺐ‬

Arabic

To write

‫أﻛﻞ‬

Arabic

To eat

congeler

French

To freeze

‫ﯾﺰﻛﺎ‬

Tamazight

To calm

Pomada

Spanish

ointment

‫ﻟﺤﻢ‬

Arabic

meal

learning. FastText was chosen for its ability to handle large datasets and its effectiveness in capturing word similarities through n-grams and neural network embeddings. FastText’s n-gram model is particularly suitable for handling the morphological richness of Moroccan Arabic, where words can have multiple orthographic variants. Training We employed an unsupervised model as a crucial element in constructing the OV lexicon. Initially, we adhered to the FastText guidelines, specifically utilizing FastText’s autotune feature to automatically optimize and determine the most suitable hyperparameters for our dataset. The following specifications were configured during the training process: • Window size = 2: This denotes the size of the word context. • Number of epochs = 5: This parameter governs the total number of iterations the algorithm performs during training across the entire dataset. • Embedding size = 300: This represents the dimension of the embedding space. • Batch size = 201: This indicates the number of tuples on which the neural network operates in each training step.

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Table 2 Evaluation metrics for the Moroccan Arabic lexicon model Metric

Precision

Recall

F-measure

Error rate

Value

0.91

0.87

0.89

0.0865

After the training process, two files were obtained: “model.vec,” containing only the aggregated Moroccan word vectors, and “model.bin,” which includes the vectors for all the Moroccan (UGT) words n-grams. We then evaluated the model by computing the precision and recall. It should be noted that model optimization can be performed using the binary file. Evaluation To evaluate the effectiveness of the machine learning approach, we employed the following performance metrics: • Precision: The proportion of relevant orthographic variants among the retrieved variants. • Recall: The proportion of relevant orthographic variants that were correctly identified. • Error Rate: The proportion of orthographic variants that were incorrectly identified as relevant. After training the FastText model on the Moroccan Arabic dataset, we conducted a comprehensive evaluation to quantify its performance using precision, recall, and F-measure. In Table 2, we provide the results of evaluating the output model. The Precision score was 0.91, indicating that 91% of the identified variants were relevant. Recall was measured at 0.87, demonstrating that the model successfully captured 87% of all relevant variants present in the data. The F-measure (or F1 score) for our model was 0.89, reflecting a high level of accuracy and effectiveness in identifying orthographic variants. The Error Rate was calculated at 0.0865. These metrics highlight the model’s robustness and reliability in enhancing the Moroccan Arabic lexicon through the integration of machine learning techniques.

4.4 Lexicon Inference Utilizing a Moroccan Reference Vocabulary (MRV) comprising 4.5 million normalized Moroccan words [33], we examined the binary file to identify the nearest neighbor vectors for each word within the vocabulary. Assuming that the extracted vectors correspond to orthographic variations of the words, our findings revealed that 53.14% of the normalized Moroccan words possess at least one orthographic variant. However, the described process, conducted without any refinement tasks, was unsuccessful in extracting orthographic variants for the remaining 46.86% of the reference vocabulary. Figure 1 presents a sample of the identified orthographic

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Fig. 1 Model’s prediction results of the word: ‫طﻮﻣﻮﺑﯿﻠﺔ‬

variants for the Moroccan normalized word ‫ طﻮﻣﻮﺑﯿﻠﺔ‬/car/(tomobila), along with the corresponding orthographic similarity rate.

4.5 Lexicon Refinement The orthographic variations extracted exhibited a degree of noise, with nearly 35% of these variants not relevant to their corresponding Moroccan normalized words. This discrepancy arose because the FastText nearest neighbor feature not only identifies orthographically similar words (utilizing n-gram information) but also includes semantically close words (using full word context). For instance, in Fig. 1, the words

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‫( ﻟﻮطﻮ‬loto)/car/, ‫( طﺎﻛﺴﻲ‬taksi)/taxi/, and ‫( ﺗﺎﻛﺴﻲ‬taksi)/taxi/, though synonyms, are not considered orthographic variants of ‫ طﻮﻣﻮﺑﯿﻠﺔ‬. To refine the OV lexicon, we employed a character-level rule-based technique that assesses orthographic similarities between words. In addition to considering the Levenshtein distance [34], we eliminated candidate orthographic variants that do not share a significant number of sub-n-grams with the Moroccan normalized word. The n-gram similarity score, denoted as Sngram [35], is computed as follows: Sngram = α/β. where: α

represents the number of unique sub-n-grams shared between the Moroccan Reference Vocabulary (WMRV ) and the candidate orthographic variant (WOV ). • βis the total sum of unique sub-n-grams in both W MRV and WOV . •

Sngram is calculated based on word bi-grams, and only candidates with Sngram < 0.5 are discarded. After implementing these rules, the set containing at least one orthographic variant candidate was reduced to 30.13%. It is worth noting that the process of lexicon refinement proved to be time-consuming compared to all the preceding stages combined.

4.6 Discussion The development of the Moroccan Arabic lexicon using a hybrid approach combining machine learning and automatic refinement techniques has proven to be effective in improving the accuracy and coverage of lexicons. Our study indicates that the hybrid approach successfully identifies a significant portion of orthographic variants in Moroccan Arabic, with 53.14% of normalized words possessing at least one variant. This demonstrates the potential of combining manual efforts with machine learning to enhance lexicon creation. The refinement process, which reduced the set of candidates to 30.13%, highlights the importance of meticulous post-processing to ensure quality. However, several challenges were encountered, including ensuring the quality and uniformity of the data. The preprocessing steps, such as normalization and cleaning, were crucial in preparing the dataset for effective training, yet the presence of noise and irrelevant variants in the initial extraction highlighted the limitations of automated techniques and the need for robust preprocessing methods. Additionally, while FastText proved effective in identifying orthographic variants, the inclusion of semantically similar words as variants indicated a limitation of the model, necessitating additional refinement steps that were time-consuming but essential for improving precision. Balancing manual annotation with automated efforts posed a significant challenge. While machine learning can efficiently process large datasets, manual annotation remains crucial for ensuring accuracy and addressing nuances that automated methods may miss. Our hybrid approach aimed to leverage the strengths of

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both, but finding the optimal balance required careful consideration and iterative refinement. The development of an accurate and comprehensive Moroccan Arabic lexicon has important implications for various NLP applications, such as machine translation, sentiment analysis, and speech recognition systems, particularly for languages and dialects with limited existing resources. The process of lexicon creation must also consider ethical and social implications to ensure that the lexicon does not reinforce linguistic or cultural biases, emphasizing the need for ongoing evaluation and refinement to maintain fairness and inclusivity in NLP applications. The scalability of the hybrid approach suggests that similar methods could be applied to other languages and dialects, enabling researchers to create high-quality lexicons for under-resourced languages and contributing to the broader field of NLP.

5 Conclusion The field of natural language processing has advanced significantly in recent years due to the availability of big data and advances in machine learning and AI techniques. One crucial aspect of NLP is the creation of accurate and comprehensive lexicons. However, traditional approaches to lexicon creation are often time-consuming, expensive, and limited in coverage. In this paper, we explored the challenges and best practices in lexicon creation and presented a case study of developing a Moroccan Arabic lexicon using a hybrid approach. We discussed the challenges of subjectivity and ambiguity in natural language, the quality of the data, the scalability and efficiency of the annotation process, and the ethical and social implications of lexicon creation. We also proposed best practices for developing comprehensive annotation guidelines, using domain-specific sources of data, and leveraging machine learning and other innovative techniques. The case study that we highlighted in this paper demonstrates the effectiveness of the hybrid approach in improving the accuracy and coverage of the created lexicon and highlights the importance of balancing manual annotation and machine learning techniques. Authors’ Contributions Ridouane Tachicart and Karim Bouzoubaa conceived of the presented idea, developed the theory and performed the computations. Karim Bouzoubaa and Driss Namly verified the analytical methods and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript. Funding Declaration No Funding. Availability of Data and Materials The lexicon will be available under a license. Ethical Approval Not applicable. Competing Interests The authors declare no competing interests regarding the research, authorship, and publication of this article.

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The Role of Future Technology in the Preparation of Prospective Teachers Mahyudin Ritonga, Syaipuddin Ritonga, Adam Mudinillah, Julhadi, and Ilham Eka Putra

Abstract Research Background: In this day and age, technology is critical. The reason is that all aspects of life cannot be separated from technology. Starting from the economy, communication and information, transformation, and education. In the world of education, especially for prospective teachers, understanding and utilizing this technology can be a critical factor in preparing them to become effective educators in the digital era. General Research Objectives: This research aims to evaluate the extent to which prospective teachers understand and utilize future technology in an educational context and to determine how important the role of future technology is in improving the preparation of prospective teachers as well as to identify the understanding and use of technology among prospective teachers from various educational backgrounds. Research methods: The research method used is a survey. This research involved surveying prospective teachers from various departments and educational backgrounds. Respondents were asked to fill out questionnaires that measured their understanding and use of future technology in an academic context. Data management is carried out using Google Forms survey administration software. The data collected will be analyzed using a one-way ANOVA test to identify significant differences in understanding and use of future technology between groups of prospective teachers. Research result: The research results show that M. Ritonga (B) Muhammadiyah University of West Sumatra, Padang, Indonesia e-mail: [email protected] S. Ritonga Sekolah Tinggi Agama Islam Negeri Mandailing Natal, Panyabungan, Indonesia e-mail: [email protected] A. Mudinillah Sekolah Tinggi Agama Islam Al-Hikmah Pariangan, Pariangan, Indonesia e-mail: [email protected] Julhadi Universitas Muhammadiyah Sumatera Barat, Padang, Indonesia I. E. Putra Universitas Islam Sumatera Barat, Kota Padang, Indonesia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_16

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future technology can make it easier for prospective teachers to prepare themselves as educators in the digital era. This research also indicates significant differences in understanding and use of future technologies among prospective teachers from various backgrounds. Besides that, technology is vital because it can make things easier for prospective teachers from various educational backgrounds. Research conclusions: This research shows that future technology has a vital role in preparing prospective teachers from various educational backgrounds. The difference is in the understanding of the prospective teachers. On average, prospective teachers who have an academic background from public schools understand more about the use of future technology than prospective teachers who have an educational background from Islamic boarding schools. Keywords The role of technology · Understanding and use · Prospective teachers

1 Introduction Education is a systematic process that involves transferring knowledge, skills, values, and culture from one generation to the next [6]. This can occur in various contexts, such as school, family, and community, aiming to prepare individuals to participate productively in society and achieve their potential personally and socially. Education is also a critical process in forming individuals and society [12]. More than just the transfer of knowledge, education involves the development of the social and moral aspects of the individual. It is a bridge that connects individuals with the world of knowledge, helping them understand the concepts, values, and norms that shape culture and Society [31]. Additionally, education provides individuals with the tools to understand, criticize, and contribute to social, economic, and political developments in society. Efforts to achieve educational goals certainly require aspects that support this success. One aspect that can help realize effective education is the existence of sophisticated future technology in the current digital era [10]. Technology is a collection of knowledge, tools, methods, and processes used to develop, create, and apply practical solutions in various aspects of human life [32]. Technology enables humans to increase efficiency, productivity, and quality of life through scientific discoveries and innovations in various disciplines [8]. The development of technology today is very rapid, making it easier for people to live their daily lives. Future technologies are innovative developments shaping how we live, work, and interact with the world [34]. This involves using advanced technologies such as artificial intelligence, advanced biotechnology, and high-speed internet. Future technology can change various aspects of life, from more sophisticated health care to more efficient mobility [1]. The ability to adapt to technological developments will be vital in facing an increasingly connected and complex future. The development of sophisticated technology influences almost every aspect of human life today [35]. Be it in economics, information, transformation, social

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science, or education. In education, technology plays a vital role for prospective teachers. A prospective teacher prepares to become a professional educator [28]. Prospective teachers have a strong interest and commitment to sharingsharing knowledge and guiding and shaping generations into knowledgeable and noble human beings [30]. Becoming a teacher involves formal education and skill development training that enables them to teach well. For prospective teachers, understanding and utilizing technology is no longer just an option but has become necessary. In the digital era that continues to develop, technology is critical in preparing prospective teachers to become excellent and competitive educators [4]. Prospective teachers also provide opportunities to develop other skills necessary for the teaching profession, such as good communication skills, application of technology in learning, and understanding of the role and responsibilities of teachers in educating the younger generation [7]. In addition, prospective teachers are also allowed to gain an in-depth understanding of the education system, national curriculum, and current educational policies [25]. With a good sense of these aspects, prospective teachers will be ready to face the challenges and changes that occur in the world of education [19]. Prospective teachers can also apply the theory learned practice through internships or teaching practice at schools [11]. This allows prospective teachers to experience the experience of being a teacher directly by seeing the reality of education and facing the challenges a teacher faces today. During the learning process, prospective teachers face challenges and obstacles in each process, such as needing to be more technologically literate or technologically illiterate. This problem can be overcome by frequently attending training on future technology, following developments in information technology, self-teaching, and so on [23]. Prospective teacher students must be prepared to face various challenges in learning in today’s world by improving their skills regarding future technology. They must be able to develop themselves to avoid being left behind [17]. Apart from that, prospective students must also be able to adapt to the current digital world [9]. How to face obstacles and challenges for prospective teacher students by applying advanced technology in learning also requires a comprehensive approach. The researcher raised this issue because he wanted to review the role of technology for prospective teachers. It also evaluates the understanding and use of technology for prospective teacher students [26]. Technology makes it easier for prospective teachers to go through the process of becoming real teachers. Technology facilitates communication and collaboration between teacher candidates and their mentors [39]. With online platforms, they can share experiences, get feedback, and discuss current education issues. Technology also allows prospective teachers to observe teaching conducted by experienced teachers via video recording or video conferencing [15]. All of this will enrich the learning experience of prospective teachers, helping them grow and develop as more competent and innovative educators [38]. Thus, technology will play an integral role in creating prospective teachers who are ready to face changes and challenges in the ever-evolving world of education. This helps student teachers understand different learning methods and design teaching strategies appropriate to current technological developments [29]. In addition, using technology in learning provides hands-on experience that can improve

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prospective teachers’ digital skills [33]. Student teachers must also understand how to integrate technology into teaching to prepare the younger generation for today’s digital world. By applying technology, prospective teacher students gain knowledge about the practical application of technology in an educational context and develop the adaptability and innovation skills needed for the teaching profession in the coming digital era. Based on previous researchers’ statements, the researcher proposes an innovation to solve problems caused by a need for moreneed for more understanding and use of technology in preparing prospective teachers. Researchers reveal several ways to be done; for example, prospective teachers must increase their knowledge about sophisticated technology in the digital era. Apart from that, prospective teachers must keep up with the increasingly rapid technological developments to be included with the times. Schools also play an essential role in prospective students’ understanding of technology. The school must provide technological facilities such as computers, typing rooms, information communication technology training, etc. Research aims to determine whether implementing advanced technology for student teachers can improve the quality of education and prepare prospective teachers. This research can evaluate the effectiveness of various technology tools and applications in conveying learning theory and increasing understanding of learning concepts in integrating technology into practical teaching. Researchers hope that the research can make student teachers understand teaching and the material being taught in today’s digital world.

2 Method 2.1 Research Design This research uses a quantitative research design, allowing researchers to measure the number of creative ideas students produce in Google Forms, which can be as many as 20 items. This data can be used to measure how important the future technology will play in preparing prospective teachers. Researchers collected data from surveys of nearby educational institutions and conducted observations on students from various majors to measure the importance of the use of technology for prospective teachers. Next, we conducted in-depth interviews with students. We distributed a Google format survey with several questions about the role of technology in the world of education in the current digital era. This collection method is helpful for prospective teacher students in developing technology in the current digital era and then using statistical analysis to determine the impact between students who use information technology in the digital era and students who do not use this type of learning [21]. In this case, the data can be processed using SPSS. The accuracy of the data obtained was proven via a Google form by the researcher. The researcher also inputs the

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highest and lowest results from the questionnaire distributed to each student and concludes these statements.

2.2 Research Procedure The stages carried out by the researcher began with filling in a questionnaire and then filled in by students studying at Mahmud Yunus Batusangkar State Islamic University. The students who were used as filling objects were students from various departments, especially students from the Tarbiyah and Teacher Training faculties and different semesters. Each answer that has been answered then becomes a reference source that measures how important the use of future technology is for the prospective teacher [5]. Students who filled out the entire questionnaire were relatively fast, making it easier for researchers to discuss all matters relating to the importance of future technology in preparing future teachers in the present. Then, the researcher inputs it in Excel and tests it using the one-way ANOVA test in the SPSS application.

2.3 Research Subject The provisions for students who will be interviewed in this research are students who can provide information related to questions supplied by researchers and students who utilize technology to become real teachers. Apart from that, students who can give answers accurately and directly and actively attend lectures from various study programs. The researcher conducted this research honestly to obtain the data that had been received. This honest attitude is highly appreciated by respondents who believe in the accuracy of the data obtained so that the data can be accounted for. Not only that, this research complies with laws and regulations because researchers must be able to maintain and protect data privacy and research copyright.

2.4 Research Ethics Data that researchers have collected cannot be identified from one individual to another without permission from the researcher [13]. Research must also obtain consent from participants, especially researchers who use data or interact with individuals. Likewise, research ethics must be transparent; researchers provide information that includes the aims, methods, and potential consequences of research aimed at respondents. Researchers must comply with the professional code of ethics that applies to specific fields within the relevant institutions [27]. The data collection technique was carried out in the odd semester of the 2022/2023 academic year, then, the

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online questionnaire link was distributed to correspondents from various departments at Mahmud Yunus Batusangkar State Islamic University.

2.5 Data Collection Techniques The techniques used by researchers in collecting all data and information start from October 24, 2023, to October 26, 2023. The questionnaire data will then be downloaded in an Excel file and transferred to SPSS to analyze the data further. Final data and the number of students who obtained interviews via the link distributed using the Google Form platform. After all the data has been obtained, it will be recorded in the SPSS application. Researchers must use several procedures in data analysis researchers must use. First, the researcher conducted descriptive statistics that were useful in answering the researcher’s questions about the role of future technology in preparing prospective teachers. The data is presented as averages and percentages [24]. Before parametric and normality tests were carried out, the variables obtained were standard, which is applied to all independent variables.

2.6 Data Processing and Data Analysis Techniques The data that has been collected is then input, and then the output is produced using an application called SPSS. The data is written in tabular form, making it easier for researchers to calculate the answers obtained from students. The method used to analyze the data is by comparing technology’s role in preparing prospective teacher students in the learning process to become real teachers at Mahmud Yunus Batusangkar State Islamic University. The data is presented as an average score and a number, then tested using the one-way ANOVA test to compare the two variables obtained from Google Forms. The researcher will also present a result as a conclusion so that it is easy for student teachers to understand. The findings of this research will be made as complete as possible based on the facts collected by researchers in the field (Table 1). Table 1 List of student study programs No.

Study program

Number of participants

Percentage (%)

1

Arabic language education

21 people

30

2

Ahwal Al-Shakhshiyyah

3 people

8

3

Guidance and counseling

Two persons

4

4

Islamic education

Two persons

25

5

PGMI

Five people

10

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In the structure above, researchers divide the research flow into nine stages. In the initial stage, the researcher collected data related to the research title. Then, from the collected data, researchers created questions about the role of future technology in preparing prospective teachers. Researchers put data about the titles being researched into questions. The researcher presented 20 statements. After that, the researcher entered the 20 statement items using Google Forms, which later these questions would be distributed to UIN Mahmud Yunus Batusangkar students from various departments into the WhatsApp group to collect the most significant percentage of respondents who had answered the statement. After the data was successfully collected, researchers found that there were 33 respondents who had responded to the questionnaire. The researcher downloaded the questionnaire data using a spreadsheet and transferred it to Excel. From the Excel table, researchers enter research data into the SPSS application. Next, the final step is for researchers to manage research data using SPSS to make the data that has been found accurate. Once completed, the researcher makes conclusions from the research that has been studied (Fig. 1).

Fig. 1 Flow of data collection and data analysis

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3 Results 3.1 The Role of Future Technology in the Preparation of Prospective Teachers As can be seen in Table 2 regarding the role of future technology in the preparation of prospective teachers, it can be seen that understanding technology is no longer an option but has become a necessity in the current technological era. The use of technology in preparing prospective teachers is beneficial because it can facilitate students in the process of becoming actual teachers. Technology can also help students who are undergoing the process of becoming real teachers. Apart from making it easier for prospective teacher students, the use of technology also functions so that teaching staff do not experience being left behind by developments that are becoming more sophisticated from time to time. This generally shows that students who participated in filling out the questionnaires given by researchers felt that technology played an essential role in preparing prospective teachers. Detailed results for each item can be seen in Table 2. The table above results from an assessment from a questionnaire given to student– teacher candidates from various departments at the Mahmud Yunus Batusangkar State Islamic University. Responses or responses provided by prospective teacher students regarding the role of future technology in preparing prospective teachers. This assessment has four assessment categories: strongly agree, agree, disagree, and strongly disagree. Based on this table, the first highest assessment result was 62.9% with the strongly agree category. Meanwhile, the second assessment was 60% with the agree assessment category. The use of technology has an impact on prospective teachers in today’s sophisticated era. Teachers must also be able to keep up with the times and be aware of technology becoming more sophisticated daily. Most provided positive responses to the questionnaire that the researcher has given. Technology plays a vital role for students and teachers from various majors. Student teacher candidates realize that technology can make it easier for prospective teachers to become real teachers (Table 3). Next, the researcher will describe the results of filling in the questionnaire; for the statement with the highest results, the first of the twenty statements submitted to students is found in the question item. Understanding technology is essential in the Table 2 Details of the research sample

No.

Selected category

1

Strongly agree

>90

2

Agree

70–80

3

Disagree

50–60

4

Sangat disagrees

0–40

Total

Level number (%)

100

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Table 3 The role of future technology in the preparation of prospective teachers No. Statement

Strongly agree (%) Agree (%) Disagree (%) Strongly disagree (%)

1

Technology plays an 57.1 essential role in the preparation of prospective teachers

40

2.9

0

2

Understanding technology is essential in the world of education

62.9

34.3

0

2.8

3

Technology is 54.3 essential for prospective teachers from various educational backgrounds

45.7

0

0

4

Good use of 48.6 technology can facilitate the process of becoming a teacher

51.4

0

0

5

A good understanding of technology is essential in the digital era, especially in the world of education

51.4

45.7

0

2.9

6

Technology is one of the means to prepare yourself to become a teacher

48.6

45.7

2.8

2.8

7

Prospective teachers 51.4 are expected to understand and understand technological advances today

45.7

2.9

0

8

Technological 48.6 sophistication must be utilized as best as possible by prospective teachers

51.4

0

0

9

Schools need to 57.1 provide technology services for students

40

0

2.9

(continued)

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Table 3 (continued) No. Statement

Strongly agree (%) Agree (%) Disagree (%) Strongly disagree (%)

10

Technology is beneficial in the process of becoming a teacher

45.7

51.4

0

2.9

11

Prospective educators need to understand technology

45.7

57.1

0

0

12

Technology needs to 42.9 be understood by prospective teachers from various educational backgrounds

57.1

0

0

13

Educational background influences understanding of technology

60

5.7

0

14

Prospective teachers 28.6 from public schools understand technology better

57.1

11.4

2.9

15

Technology is 42.9 equally essential for prospective teachers from various backgrounds, and what makes the difference is in terms of understanding

54.3

0

2.8

16

Social media can be 37.1 used to share experiences and knowledge between prospective teachers

60

13.3

0

17

The background of 34.3 prospective teachers makes a significant difference in their understanding of technology

54.3

11.4

0

18

Prospective teachers 42.9 need to develop solid digital literacy

57.1

0

0

34.3

(continued)

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Table 3 (continued) No. Statement

Strongly agree (%) Agree (%) Disagree (%) Strongly disagree (%)

19

Prospective teachers 42.9 need to continually update their technology skills to keep up with the times

57.1

0

0

20

Technology can improve the quality of prospective teachers

45.7

0

0

54.3

world of education. Namely, it has the highest number of agree categories, 62.9%. Understanding technology in education is critical, especially for prospective teachers. Understanding this technology can make things easier for prospective teachers by facilitating communication with other prospective teachers. Apart from that, a good understanding of technology can also improve the quality of learning and provide a more interactive and enjoyable learning experience for students. The second most dominant result is regarding social media, which can be used to share experiences and knowledge between prospective teachers. This statement received the second-highest score in the agreed category, 60%. Social media is a very effective means for prospective teachers to share experiences and knowledge. Prospective teachers can exchange ideas, share teaching methods, and gain new knowledge about education through platforms such as discussion groups, forums, or professional networks. By using social media, they can support each other, inspire and expand their understanding of innovative teaching techniques, helping them become better educators and reflect rapid responses to developments in the world of education. The category that received a score of 60% is also found in statement number 13: Educational background influences the understanding of technology. Academic training has a significant impact on future teachers’ knowledge of technology. Formal education that includes the use of technology, either through curriculum or special training, can provide a solid foundation for prospective teachers to understand and integrate technology into their teaching. Experience and understanding of using technological devices during training will influence pre-teaching teachers’ attitudes, beliefs, and skills toward using technology in the classroom, potentially creating a more innovative, inclusive learning environment. Responsive to technological developments. The lowest category in the statement above is in the strongly agree category, with a figure of 28.6%. Say that prospective teachers from public schools understand technology better. Basically, educational background influences prospective teacher students’ understanding of technology. For prospective teachers, academic training influences their understanding of technology. A solid education provides

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the knowledge base to integrate technology into learning. With a thorough understanding of technology concepts, pre-service teachers can create innovative learning environments using digital tools and platforms to support engaging and effective teaching. In addition, technological literacy also allows prospective teachers to adapt their teaching methods to technological developments, preparing students to face the increasing demands of technology and connectivity.

3.2 Future Technology Has a Very Important Role for Prospective Teachers Technology has brought significant changes in the world of education by providing many undeniable benefits. Integrating technology into learning allows more accessible and faster access to educational resources, including digital learning materials, e-books, and online information sources. Additionally, online learning platforms and educational applications enable students to study independently and collaborate with classmates without being limited by geographic location. Technology also enables personalized learning, where teachers can identify each student’s needs and provide appropriate support. With technology, the learning process becomes more fun and interactive, motivating students to explore, experiment, and actively participate in the learning process. Nowadays, technology has become something that cannot be separated from every human life. All existing aspects require technology. One is in the education world, especially for prospective teachers. Technology plays a vital role for prospective teachers. The reason is that technological advances can provide unlimited access to information and innovative learning methods. Prospective teachers can utilize technology to create a more interactive and comprehensive student learning experience. By using educational software, simulations, and online learning platforms, they can create engaging learning environments that allow students to explore course material more deeply. In addition, technology also helps personalize the teaching process. Using artificial intelligence and data analysis, prospective teachers can adapt their curriculum and teaching and learning methods to suit the needs of each student. An adaptive learning system will help teachers identify students’ weaknesses and strengths to provide more specific and practical assistance. This will create a more focused learning experience and support students’ personal development. Lastly, technology enables global collaboration and access to unlimited resources. Student teachers can connect with colleagues worldwide, sharing ideas, experiences, and learning resources. This will broaden the scope of their knowledge, enable the adoption of best practices from different parts of the world, and open a window for international cooperation in developing curricula and teaching methods. With technology, prospective teachers can explore, deepen their knowledge, and improve the quality of education in previously impossible ways.

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Understanding technology is a crucial skill for future teachers and educators. Technology has become inseparable from everyday life and the educational process in an increasingly digital era. Technology-savvy teachers have the opportunity to improve the quality of their teaching. They can create a more dynamic and relevant learning environment by integrating digital tools, online resources, and learning platforms into their curriculum. Technological literacy also allows teachers to communicate more effectively with students via social media, email, or online learning platforms, making discussions, assignments, and information exchange more accessible. Additionally, technological literacy helps teachers teach students digital literacy, an essential skill in modern life. Teachers who can guide students to use technology wisely, manage data safely, and understand online etiquette will give young people the tools they need to succeed in an increasingly connected world. Therefore, technological literacy increases teaching effectiveness and prepares students to become global citizens who can face future challenges. Understanding of technology can vary greatly between prospective teachers from different educational backgrounds. Teacher candidates with a background in technology or computer science may have an advantage in understanding digital tools and platforms, which allows prospective teachers to integrate technology into learning more quickly. They can also gain a deeper understanding of the security and privacy aspects of the use of technology, which are important in today’s online education environment. However, they may also have difficulty adapting to student-centered teaching methods and diverse educational programs. On the other hand, aspiring teachers with backgrounds focused on specific subjects may need more effort to develop an adequate understanding of technology in order to integrate technology into teaching effectively. However, their advantage may lie in their deep understanding of the subject matter they teach, which they can combine with technology to create a more relevant and contextual learning experience. In this case, collaboration between teachers with diverse educational backgrounds can be the key to achieving optimal learning outcomes, where technological expertise and curriculum understanding can coordinate with each other (Table 4). In the table above, you can see, the total of Sum of Square (ss) shows that with the number 9,806, the number is the difference found by the researcher and then the researcher compares the results obtained using the One-Way Anova test. The value of 9,806 indicates the variation between groups. Furthermore, df or degrees of freedom is called degrees, where the number 17 indicates the sum of the degrees of freedom described in the form of a statistical analysis formula. In addition, the df table above also shows the mean square which is obtained as a 0.577 number, a result obtained through the comparison of results carried out by researchers between diverse or varied groups with the mean square group. Then the sign without numbers or usually written with the number 0.000 explains that there are different levels of achievement and are still not relevant to achieve an acceptable level for now. This research discusses the role of future technology in the preparation of prospective teachers. This can be proven by researching prospective teacher students in the use of future technology which is shown by the One-Way Anova test which in question number 2 shows that an understanding of technology is indispensable in the world

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Table 4 One way anova test on the role of future technology in the preparation of prospective teachers ANOVA X.1

X.2

X.3

X.4

X.5

X.6

X.7

X.8

X.9

X.10

X.11

X.12

Sum of squares

df

Mean square

F

Sig.

Between Groups

9.806

17

0.577

8.899

0.000

Within groups

1.167

18

0.065

Total

10.972

35

Between groups

13.722

17

0.807

12.454

0.000

Within groups

1.167

18

0.065

Total

14.889

35

Between groups

8.972

17

0.528

Within groups

0.000

18

0.000

Total

8.972

35

Between groups

7.972

17

0.469

8.441

0.000

Within groups

1.000

18

0.056

Total

8.972

35

Between groups

14.389

17

0.846

30.471

0.000

Within groups

0.500

18

0.028

Total

14.889

35

Between groups

14.698

17

0.865

8.380

0.000

Within groups

1.857

18

0.103

Total

16.556

35

Between groups

8.806

17

0.518

4.303

0.002

Within groups

2.167

18

0.120

Total

10.972

35

Between groups

8.472

17

0.498

17.941

0.000

Within groups

0.500

18

0.028

Total

8.972

35

Between groups

13.643

17

0.803

10.644

0.000

Within groups

1.357

18

0.075

Total

15.000

35

Between groups

13.056

17

0.768

9.216

0.000

Within groups

1.500

18

0.083

Total

14.556

35

Between groups

7.905

16

0.494

13.339

0.000

Within groups

0.667

18

0.037

Total

8.571

34

Between groups

7.250

17

0.426

5.118

0.001

Within groups

1.500

18

0.083 (continued)

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Table 4 (continued) ANOVA

X.13

X.14

X.15

X.16

X.17

X.18

X.19

X.20

Sum of squares

df

Mean square

F

Sig.

Total

8.750

35

Between groups

10.222

Within groups

1.000

17

0.601

10.824

0.000

18

0.056

Total

11.222

35

Between groups

15.083

17

0.887

Within groups

3.667

18

0.204

4.356

0.002

Total

18.750

35

Between groups

13.139

17

0.773

Within groups

1.167

18

0.065

11.924

0.000

Total

14.306

35

Between groups

11.639

17

0.685

Within groups

2.000

18

0.111

6.162

0.000

Total

13.639

35

Between groups

12.222

17

0.719

Within groups

2.000

18

0.111

6.471

0.000

Total

14.222

35

Between groups

7.083

17

0.417

Within groups

1.667

18

0.093

4.500

0.001

Total

8.750

35

Between groups

8.250

17

0.485

Within groups

0.500

18

0.028

17.471

0.000

Total

8.750

35

Between groups

7.306

17

0.430

Within groups

1.667

18

0.093

4.641

0.001

Total

8.972

35

of education, with this it will be easier for researchers to determine that prospective teachers are aware of the importance of the role of technology in the preparation of prospective teachers.

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4 Discussion 4.1 The Role of Future Technology in the Preparation of Prospective Teachers Prospective teachers are those who prepare and train themselves to become professional teachers [14]. Prospective teachers have an interest and determination to enter the teaching profession with the aim of educating and guiding students to develop their potential to the maximum. Prospective teachers must go through various stages of training, which can be in the form of formal education at a college or university, internship or teaching practicum programs, and continuous training and skill development [16]. During the preparation process, teachers learn teaching methods, educational psychology, curriculum and other related topics. Prospective teachers also develop the educational and social skills necessary to create an effective learning environment and support student development. Prospective teachers are typically committed to understanding students’ individual needs, creating appropriate learning experiences, and acting as advisors and guides in students’ academic and social development [36]. Over time and experience, future teachers will become experienced teachers who are committed to advancing the education and well-being of their students. Technology plays a central role in the world of education, especially for prospective teachers, with various functions to support learning and professional development [40]. One of the most prominent functions of technology is as an educational tool. Aspiring teachers can take advantage of a variety of online learning apps, software, and platforms to create a more interactive and engaging learning experience. This includes the use of multimedia, simulations, and other digital resources to clarify concepts, explain the material, and connect learning to the real world [3]. In addition, technology also functions as an assessment and measurement tool. Prospective teachers can use a variety of software and applications to measure student progress, analyze learning outcome data, and design appropriate interventions. Using this technology, prospective teachers can provide feedback to students more accurately and effectively, identify individual needs, and improve their teaching methods. This supports a more adaptive and outcome-oriented approach to learning. In addition, technology also facilitates the professional development of future teachers. Aspiring teachers can access online courses, webinars, and other specialized resources to improve their teaching skills, classroom management, and technology literacy. With easy access to information and training, prospective teachers can continue to update their knowledge, follow the latest developments in the field of education and become highly qualified educators. Therefore, technology plays an important role in preparing future teachers to face the demands of the complex education profession. Future technologies play an important role in preparing future teachers to face increasingly complex and diverse educational challenges [41]. One of the important roles of technology is to facilitate distance teaching and online learning, which

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can help aspiring teachers develop skills to adapt to different learning environments [37]. It also provides them with access to global educational resources and facilitates collaboration between student teachers from around the world, thus enriching their perspectives and experiences. Future technologies will also contribute to the development of more dynamic learning programs tailored to student needs [20]. Through the application of technologies such as artificial intelligence, educational data analysis, and game-based learning, prospective teachers can design more engaging and effective learning experiences. Additionally, technology will help assess and track student progress more accurately and in detail, allowing teachers to provide appropriate interventions. Therefore, future technology will be an important tool to prepare future teachers in responding to changes in the increasingly complex and rapidly evolving world of education. Prospective teachers must prepare themselves well so that they can become professional teachers in the future. Of course, there are things that can support the process of prospective teachers in preparing themselves. One of them is with future technologies [18]. Nowadays, understanding and utilizing technology is no longer just an option, but has become a must for prospective teachers. In today’s evolving digital era, technology has a very important role in preparing prospective teachers to become good and competitive educators. Prospective teachers also provide opportunities to develop other skills necessary for the teaching profession such as good communication skills, the application of technology in learning, and an understanding of teachers’ roles and responsibilities in educating the younger generation [22]. In addition, prospective teachers also provide an opportunity to gain an in-depth understanding of the education system, the national curriculum, and current educator policies [2]. With a good understanding of these aspects, prospective teachers will be ready to face the challenges and changes that occur in the world of Education. Prospective teachers also provide the opportunity to apply the theory learned into practice in the form of internships or teaching practices at school. This allows prospective teachers to experience being a teacher directly, by looking at the reality of education, and facing the challenges faced by a teacher in times like today. The way that can be done so that prospective teachers understand technology is to take the following steps. First, universities and educational institutions that train aspiring teachers can integrate advanced technology training into their programs. This can include courses that specifically address the use of technology in education, internships or projects involving the use of technology, and instructor-led training that is knowledgeable in the field of educational technology provided. As a result, prospective teachers will have a strong understanding of digital tools, applications, and technology-based teaching methods. Second, prospective teachers can be inspired to actively participate in exploring and experimenting with technology. They may be given the opportunity to develop technology-based learning projects, participate in technology seminars or workshops, or work with mentors who have technology expertise. Support in the form of guidance and access to appropriate technology resources is also important in helping them understand and feel confident in adopting technology in their teaching. This approach

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can have the effect that prospective teacher students can have direct experience in integrating technology in the context of education, which will help them master technology skills better. A good understanding of technology is expected to make it easier for the prospective teacher to undergo the process of becoming an actual teacher. The results of the study show that technology has a very important role for prospective students from various majors. The existence of this sophisticated technology makes prospective teacher students feel helped to undergo the process of becoming a professional teacher.

4.2 Future Technology Has a Very Important Role in the World of Education, Especially for Prospective Teachers Technology is a science and human invention to meet needs, solve problems, or achieve certain goals. It includes a wide range of tools, machines, software, systems and processes designed to optimize processes, improve efficiency or bring innovation into various aspects of human life. Technology is rapidly evolving over time and its role is vast, covering various fields such as communication, transportation, healthcare, education, manufacturing, and entertainment. With the development of technology, human society continues to adapt and utilize it to achieve progress and a better quality of life. In today’s sophisticated era, technology plays its role in all aspects of life. Be it the economy, communication, transportation, information, social even in the aspect of education. Technology has played a very important role in changing the educational landscape around the world. One of the important roles of technology is to provide broader and equitable access to education for individuals at different levels of society. Through the online learning platform, students from various regions and backgrounds can access learning materials flexibly and effectively. This not only improves educational inclusion but also helps students overcome geographical and economic barriers that may hinder their ability to access quality education. In addition, technology allows for better personalization of learning. Technology plays an important role in improving the quality and relevance of education, thereby creating a generation that is better prepared to meet the needs of the future. Technology plays an increasingly important role in preparing future teachers, helping to improve their teaching skills and effectiveness. First, technology provides access to a wide range of educational resources, including digital learning materials, online learning platforms, and rich information resources. This allows prospective teachers to gain a deeper understanding of the subjects, teaching methods, and the latest developments in education. Aspiring teachers can access teaching examples, case studies, and other educational resources that can help them design a more diverse and engaging learning experience for students.

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Second, technology allows aspiring teachers to participate in online training and professional development. Aspiring teachers can participate in online courses, webinars, and conferences organized by various educational institutions and professional organizations. This allows them to constantly update their knowledge and develop new skills that are relevant to the ever-changing world of education. With easy access to these resources, prospective teachers can become more qualified and competent educators. Finally, technology allows future teachers to integrate digital tools and platforms into their teaching. Aspiring teachers can use educational software, apps, and online resources to create a more relevant and interactive learning experience for students. This can motivate students to be more involved in the learning process and help them develop the digital skills needed in the modern era. Therefore, technology plays a role in improving the training of aspiring teachers and helping them become more effective educators by helping students reach their full potential. The difference in technology understanding between prospective public school teachers and prospective pesantren teachers can be quite significant. Prospective teachers from public schools have better access to modern technology and technology training during their studies. Prospective teachers may be accustomed to using computers, the internet, and other technological devices in their daily lives. As time goes by, they are faced with the development of more sophisticated technology in the field of education, such as online learning, e-learning platforms, and educational applications. This means they can better understand how to integrate technology into school teaching and management. Meanwhile, prospective teachers who come from cottages may have limited access and training in terms of technology. Future technology has great potential to make it easier for prospective students in their educational journey. One of the most promising technological developments is artificial intelligence (AI) and data analytics. This technology can be used to analyze educational data in depth, help prospective teacher students identify student needs individually, measure their progress, and design more tailored teaching approaches. Using this future technology, prospective teacher students can better understand the challenges and successes of their students, thus allowing them to design a more effective learning experience. In addition, future technology will also enrich the learning experience with more varied and interactive digital resources. Prospective teacher students will have easy access to online learning resources, a dynamic digital curriculum, and game-based learning tools that allow them to create a more engaging and student-oriented learning experience. In addition, online collaboration, social platforms, and sophisticated communication methods will facilitate cooperative and constructive learning between prospective teacher students, mentor teachers, and their peers. All of this will give prospective teachers students stronger tools to prepare themselves to become qualified and adaptive educators in the face of changes in the world of education.

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5 Conclusion This study used a quantitative method by filling out questionnaires by 33 students from various departments at Mahmud Yunus Batusangkar State Islamic University. The research aims to test how important the role of future technology is in the preparation of prospective teachers. The results of the study were processed with the help of the SPSS application and tested with a one-way anova test. The findings of the study reveal a positive influence on the use of technology in the world of education, especially in the preparation of prospective teachers. This research shows that the use of future technology can make it easier for prospective teacher students to prepare themselves to become real teachers. However, the results of the study also show that there is a significant difference in the understanding of technology for prospective teachers from various different educational backgrounds. Technology will play an increasingly important role in training and preparing prospective teachers for their future assignments. Through the use of various digital tools and platforms, teacher education can become more dynamic, efficient, and relevant to the demands of the times. Technology allows aspiring teachers to access a variety of learning resources, collaborate with peers, and design engaging learning experiences for students. In addition, technology also allows for the personalization of teacher education. Using data and analytics, teacher education can tailor learning to meet the individual needs of their students, ensuring that each student gets the support they need. Technology also allows for the development of technological skills required by aspiring teachers, which will be a valuable asset in teaching and facilitating modern learning. The results of this study also emphasize that teacher education must keep up with the ever-changing development of technology. Aspiring teachers need to constantly develop their digital skills and keep up with the latest developments in educational technology. This will ensure that they are ready to face the challenges and opportunities offered by technology in the world of education. Thus, this research underscores the importance of technology integration in teacher preparation and shows that technology is a valuable tool in improving the quality of education and preparing future teachers who are ready to face an increasingly connected and rapidly changing world. So, it can be concluded that technology plays an important role in improving the accessibility and inclusivity of education. Online learning platforms, mobile apps, and digital resources have transformed the way students access information and subject matter. The research shows that technology has enabled people from all walks of life, including those living in remote areas or with physical limitations, to access quality education. This illustrates the positive impact of technology in overcoming geographical and economic barriers that can hinder access to education. In addition, the results of the study also show that technology has helped improve learning efficiency. Using data analysis and intelligent algorithms, teachers can determine the needs and progress of each student. Better personalized learning, students can learn at a level and pace that suits their abilities, thus improving comprehension and performance.

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Technology also allows for the integration of more engaging learning methods, such as game-based learning, which can motivate students and increase their engagement in the learning process. The study also highlights the importance of developing digital skills in education. Teachers and students must be fully trained to master technological tools and use them effectively in learning. The results of this study show that technology education is becoming increasingly important in preparing students to meet the demands of an increasingly connected and technology-based world. Therefore, technology has proven its important role in the world of education, improving accessibility, learning efficiency, and preparing students for a future connected with high technology. Acknowledgements The researcher is very grateful to the parties involved in this study, who have helped the researcher in exploring the research entitled “The Role of Future Technology in the Preparation of Teacher Candidates”. After the researcher conducts this research, the researcher can better understand the role of future technology in the preparation of prospective teachers, and hopes that the next researcher can continue this research for better research in the future. Then thank you also to colleagues who have provided support and realization for the achievement and readiness of this article. So that the purpose and objectives of the research conducted by the researcher are achieved. The researcher hopes that this scientific article can be useful for the public, and the next researcher can continue to research that has a wider scope and is easy to understand.

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A Website Development of UCSI e-Market Hub: Transforming Unwanted Possessions Joey Chan Yen Yun, Kasthuri Subaramaniam, Raenu Kolandaisamy, and Ghassan Saleh Aldharhani

Abstract UCSI eMarket Hub aims to create a specialized online platform for second-hand trading within the UCSI University community. The primary goal of this project is to develop a centralized and user-friendly marketplace that facilitates efficient buying and selling of second-hand items among students and staff members. UCSI eMarket Hub addresses the need for a dedicated platform where they can list and search for items such as textbooks, furniture, and electronic devices. By focusing on the needs of the UCSI community, this project is not only providing a practical solution for resource management but also trying to align with a broader sustainability goal as we encourage items recycling while fostering a sense of community engagement at the same time. The development of this project demonstrates the potential of targeted digital solutions to cater to specific needs within universities. UCSI eMarket Hub provides a model for future initiatives aimed at enhancing campus life and efficient resource utilization. Through this project, we have achieved a functional and impactful solution that benefits the university community and contributes to a more sustainable environment. Keywords UCSI eMarket hub · Second-hand marketplace · Sustainability

J. C. Y. Yun · K. Subaramaniam (B) · R. Kolandaisamy · G. S. Aldharhani Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, Malaysia e-mail: [email protected] J. C. Y. Yun e-mail: [email protected] R. Kolandaisamy e-mail: [email protected] G. S. Aldharhani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_17

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1 Introduction In recent years, second-hand marketplaces have become relatively popular. This is partly because budget-conscious consumers are looking for sustainable and costeffective alternatives to traditional and ordinary retail practices. The behavior of consumers is shifting due to several factors such as the increasing awareness of environmental impact, more economically responsible options, and so on [1]. As a UCSI student, a crucial need for a functional platform that facilitates the buying and selling of second-hand items among students and other relevant stakeholders within the UCSI community has been identified. The platform should facilitate the actions of purchasing and selling of pre-used but usable goods by creating a virtual marketplace that addresses the specific needs and preferences of the university community [2]. Known for its rich cultural diversity, UCSI is not only hosting local students but also international students from various countries who are pursuing their tertiary studies in Malaysia. Consequently, it is making the need for such a platform even more concerning. For example, as newcomers to the university, students might face a myriad of challenges such as finding accommodation, acquiring furniture, buying textbooks, and obtaining home appliances for daily living purposes. These tasks can be complicated and take a lot of effort and time, especially daunting for international students who they might encounter cultural differences, unfamiliar laws, and other regulations that differ from their home countries. For instance, importing personal belongings can be a complex process, buying new items such as furniture can be a financial burden, and they might also need to sell or dispose the items when they have completed their studies and have to leave Malaysia. Subsequent paragraphs, however, are indented.

1.1 Problem Statement In the current community landscape of UCSI University, students are facing a vital challenge in finding an effective way for buying and selling second-hand items within university. Lacking a functionable system that is specifically designed to meet this demand of the community has resulted in an inefficiency and disorganized secondhand items purchasing and selling procedure. Hence, a centralized marketplace platform will provide a way for students to address and reduce the complexities and ability of students to transact in the second-hand marketplace securely and efficiently within the community. It is fair to claim that the existing solutions which scattered across different channels such as Mudah.my or Carousell are creating a fragmented experience for UCSI’s students to navigate the marketplace. For existing students and especially newcomers in the freshman year, they are facing difficulties in finding an efficient and workable way to obtain or resell the pre-owned and unwanted items to someone who needs it to extend its lifespan and reduce wastage. A centralized marketplace will provide

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a way for students to address the complexities when they are relocating to another country or state, contribute to lessening their financial burdens as buying new items could be expensive and need to be resold again in the future at a loss. Furthermore, a centralized marketplace website will provide a way for sellers to reach the potential buyers in an efficient and secure manner among the community. In addition, the absence of platform that tailored the community is not only limiting the visibility of the available items, yet the concerns of the security and reliability of transactions are rising, and it is even more difficult for students to manage the sale and purchase of second-hand goods. This emphasizes the need of UCSI students for a specialized and integrated platform that act as a one-stop platform which provide a friendly way for them to purchase and sell used goods within the community. UCSI e-Market would not only improve the overall experience of students, but it also encourages the reuse of items that help in creating a more environmentally friendly and sustainable campus culture. As a result, it is essential to create a website that is specifically designed for UCSI community to facilitate buying and selling of pre-owned items, and lessening the difficulties involved in moving and adjusting to a new culture. We hope that this study will serve as a valuable resource that address a gap in the current ecosystem.

1.2 Aim This project aims to develop a user-friendly second-hand marketplace website application that support the community of UCSI to conduct preowned items buying and selling activities through the website.

1.3 Objectives • • • •

To study the existing second-hand marketplace systems To design a second-hand marketplace website To develop a prototype second-hand marketplace website To evaluate the second-hand marketplace websites.

1.4 Justification The decision to select “UCSI eMarket Hub” as my final year project stems from an observation and experience of an unachievable need among students in UCSI University. The gap encompasses the absence of a functional platform that tailored the unique requirements and needs to perform buying and selling of second-hand products within UCSI University. Students are lacking a pre-owned items trading

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platform that focuses within UCSI community only. It is a valuable opportunity to develop this project with the objective to address a practical issue and to improve the current situation. The idea of providing a solution for a real-world issue excites me as a student majoring in Business Information Systems course and fits with the philosophy of utilizing knowledge and skills to gain to have a concrete effect. The goal of “UCSI eMarket Hub” is envisioned to provide a centralized platform that solely caters the specific requirements of UCSI Community only. By doing this, this project wishes to promote sustainability among students by streamlining the process of trading in pre-owned products. The initiative is rooted from the desire to contribute to the reduction of resource wastage in the university ecosystem. Pre-owned goods are frequently thrown away too soon due to the lack of a suitable platform that facilitates the resale of the products. As a result, “UCSI eMarket Hub” seeks to address this predicament by offering a centralized marketplace that reduces unnecessary waste but also aligns with broader environmental conversation goals at the same time. In addition, this platform also wishes to encourage UCSI members to practice sustainable behaviours.

2 Literature Review 2.1 The Evolution of Second-Hand Marketplace The adoption of marketplaces has truly experienced a remarkable transformation with the advancement of the Internet and rising of online platforms influenced by various factors such as social, economic, and technological. Back then, the concept of trading used goods could date back to ancient civilization where barter systems were predominant. People would directly exchange goods and services in order to satisfy their needs or wants without the use of money as a medium of exchange. In more recent history, flea markets and thrift stores became popular venues for purchasing and selling second-hand goods. Flea markets and thrift stores provide a physical location for individuals to trade items where the items will normally be at a lower rate than new products. They acted and served like a community hub for community interactions for in-person experiences and shopping practices in a sustainable manner. For example, the Paris Flea Market (Marché aux Puces de SaintOuen) is one of the biggest and most famous flea markets that attracts millions of visitors to buy and sell a variety of goods, from used goods to antiques. We will dive into deeper perspectives in the following sections [3].

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2.2 Classified Advertisement In the twentieth century, classified ads in newspapers were a common and popular method among individual sellers and buyers. The reach of the audience was much further and broader than local markets as buyers could get connected with sellers across wider geographic areas. For instance, the classified section of The New York or local newspapers often featured advertisements to sell used cars, furniture, and other household items. Other than that, community bulletin boards in public places like grocery stores, community centers, and universities also served as a way to trade second-hand items in the early generation. The bulletin boards can be easily accessible to the public which allows for localized and community-based trading. Growing along with the digital era, the trading ways have undergone a change such as online marketplaces [4]. In line with the development of the internet, digital marketing has been through classified ads and online listings. The platforms offered a wider reach, and they were more convenient than traditional methods in terms of posting advertisements, browsing items, and buying items from their homes. For instance, Craigslist has provided users with a virtual space to connect and facilitate transactions more conveniently. Users can post a variety of items from furniture and clothes to job listings on Craigslist, facilitating both local transactions and community connections. Thus, the internet has laid a foundation for online marketing by enabling both individuals and marketers to carry out e-commerce beyond their local physical boundaries [5]. Later in 1995, more dedicated e-commerce platforms like eBay were founded with a more secure environment for online transactions. It seems that the concept of online auctions has further diversified the online trading experience where individuals could bid on second-hand items. This added a competitive element to buying and selling second-hand products. It results in better deals for both customers and sellers. eBay gives buyers a chance to bid on items with features like “Buy it now” for immediate purchase. Other examples of emerging platforms for online trading are Amazon and Alibaba also successfully transformed online marketplaces into a comprehensive environment by offering a wide variety of products. Moreover, the implementation of a wise logistic network further enhances the popularity of these platforms and boosts the market dynamic [6]. All in all, online marketing or e-commerce is a kind of evolution in line with technological innovation and changing customer behaviors. The presence of online marketplaces not only revolutionized buying and selling process but also encouraged a global shift for a more interconnected and accessible trading.

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2.3 Sustainability and Circular Economy The popularity of second-hand marketplaces has significantly increased which driven by the growing environmental awareness. There is also a notable rise in interest in purchasing sustainable products due to the environmental impact. Trading secondhand products reduces waste and contributes to a circular economy, in which goods are reused and recycled. Thus, it can minimize the environmental footprint. Additionally, consumer habits are changing led by younger generations such as who seem to prioritize sustainability and cost-effectiveness. The drive behind this change comes from millennials and Gen Z who have become more sustainably conscious and concerned about the environmental impact of their purchasing decisions. As a result, thrifting and buying second-hand items have become popular practices among these demographics. It reflects their values and contributes to the growth of the market. This consumer trend is indicative of the larger role sustainability will continue to play in shopping habits and how they may contribute positively toward overall environmental health.

2.4 Discussion of Existing and Similar Websites in Malaysia • Mudah.my Mudah.my is one of the leading and well-known online marketplace platforms that will pop up in people’s minds when thinking of buying and selling second-hand products. Established as a powerful platform, Mudah.my connects millions of users all around Malaysia and allows customers to perform business transactions such as buying and selling pre-owned items [7]. The seller can start selling immediately after posting an advertisement on Mudah.my that is usually attached with relevant images and other detailed information of the selling products. As Mudah.my is a public platform that has high flexibility, it offers a wide range of products that can be found on the platform [8–11]. • Carousell Carousell is another large and popular marketplace that was founded by Quek Siu Rui in August 2012 [12]. It has expanded its business globally beyond Southeast Asia. By providing a platform for users to buy and sell new or pre-used items, it gained public popularity for its user-friendly interface from mobile applications and the diverse array of products available. Carousell believes in the power of unlimited possibilities and opportunity apart from transactional. Their mission is to encourage people to start selling and buying with the purpose of making more possible for one another whether it is decluttering or earning side income. With this ambitious belief, the organization successfully expanded its market globally beyond Singapore. Today, Carousell is accessible in multiple countries, and turning it into a versatile

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website or application to cater to its large number of users worldwide. To highlight, Carousell is known for its user-friendly interface that is applicable to both website and mobile applications. The firm emphasizes on the simplicity and functionality of Carousell so users can access it easily. For example, item listing, item browsing, transaction transferring, live chat, and so on. The live chat between seller and buyer enables direct communication and it indicates that users are able to negotiate prices, discuss details, and arrange meet-ups by using the platform only [13–15]. • Facebook Marketplace Facebook Marketplace is an online platform developed and launched by Facebook or Meta in 2016. It allows users to buy and sell a variety of goods and services within the local community. It became a popular option for individuals who investigate engagement in local transactions. Facebook Marketplace is integrated seamlessly into the platform of Facebook. This indicates that users can utilize and access Facebook Marketplace through the major Facebook applications or websites, and it is fair to say that it is relatively convenient for those users who are already active on social media platforms [16]. Focusing on accommodating local transactions is one of the unique features of Facebook Marketplace compared with other second-hand marketplace platforms. This means that users can list the selling items and set their location accordingly to attract potential buyers within the location by discovering the business and connecting easily. It facilitates businesses that are not limited to used items but also a platform for firms to showcase what products they are selling to attract potential customers [17]. As with most of the platforms, users can filter the searches based on their concerns such as location, category, or pricing to help them in searching what they want strategically. It also supports instant messaging features through Facebook Messenger. It leverages users’ existing Facebook profiles, and it is fair to say that it adds a transparent layer for users to enhance a sense of trust within the community. This is due to the reason that they can view each other’s Facebook profiles and mutual friends, and it prevents scams or fraud issues. Moreover, buyers and sellers can leave reviews or ratings and provide constructive feedback after completing a business [18, 19, 20] (Table 1).

3 Research Methodology System Development Life Cycle (SDLC) Agile Model will be used throughout this project. SDLC Agile Model methodology is an iterative and flexible software development approach that emphasizes on collaborative decision making between the stakeholders. Compare with the traditional model such as Waterfall model, Agile model focuses on collaboration, feedback input from customer, and most importantly the adaptability to changing requirements [21]. Generally, Agile model can be classified into six different phases that including planning, design, development, testing, deployment, and review. The project will be

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Table 1 Comparison table Criteria

Mudah.my

Carousell

Facebook marketplace

UCSI eMarket hub

User-friendly interface

✔

✔

✔

✔

User registration

✔

✔

✔

✔

Item categories

✔

✔

✔

✔

Geographical focus (Country & State)

✔

✔

✔

✘

Community focus

✘

✘

✘

✔

Onsite payment options

✘

✔

✘

✘

Price negotiation

✘

✔

✘

✘

Profile visiting

✔

✔

✔

✘

Messaging function

✘

✔

✔

✘

User feedback/ Ratings

✔

✔

✔

✘

Social integration

✘

✘

✔

✘

determined during the planning phase. Firstly, the project title shall be determined in this phase which is “UCSI eMarket Hub”. Also, the relevant and detailed information regarding to this project should be included in the project proposal. Second, design phase. The requirements or other relevant information that have been gathered and analysis in the previous stage should be able to transfer into design phase. After collecting and analyzing the users’ requirements, UML diagrams such as Use Case Diagram, Sequence Diagram, Activity Diagram can be developed in order to present the flow of the websites. These diagrams are very useful when entering development phases of the websites. Thirdly, development phase. This is the essential step that coding was implemented. The websites shall be developed based on design phase to address the requirements of the systems. Apache Netbeans will be utilized to develop the websites. Fourthly, the functionality of the websites shall be tested after it has been fully developed completely. It is important to test the system to make sure the websites are working and detect any bugs or errors that occur. After the bugs or errors have been addressed and fixed accordingly, the system will be tested once again to ensure it can work smoothly. After the websites have been checked and developed successfully, it comes to the fifth phase which is deployment stage. The websites will be launched and released to the public. Lastly, the documentation, user feedback collection and other references will be used as a reference for maintenance. Agile model has been selected because this hybrid model of iterative and incremental process model has high flexibility and ability to modify along with the rapid development of software products. This model has been widely utilized in the software industry to deliver high-quality products in an efficient and quick way. The

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task will be completed in an iterated cycle which also known as sprints regularly to achieve risk management [22].

3.1 Research Approaches By conducting research and reading relevant journal articles strategically, it helps in gaining better understanding and comprehensive views of the subject matter. Academic search engine such as Science Direct and Google Scholar assist in finding most specialized, accurate, and professional article in a more effective way.

3.2 Data Collection Method A questionnaire survey will be conducted and distributed to students and other stakeholders within UCSI Community. The purpose of distributing the questionnaire survey is to gather exact information and requirements from the stakeholders of UCSI community. Therefore, the questionnaire will be released to the public through social media platforms such as Course Networking (CN), Facebook, Instagram, and WhatsApp to approach the targeted respondent group. In addition, literature review of the reliable academic resources will also be a data collection method too.

4 Analysis and Design In order to gather information and capture requirement from potential or future users, a questionnaire survey has been designed and conducted through Google Forms. The questionnaire has released to UCSI community to reach the target respondents which are students and employees of UCSI University. 55 respondents have been successfully achieved within the timeframe. As shown in Fig. 1, there are a total of 81.80% respondents selected “Carousell” as the second-hand trading website they most frequently use it. Facebook Marketplace is the second most popular selection among respondents (61.80%). Mudah.my follows with 45.50%. Lastly, Secondhand.my did not receive any responses being a newly launched website. Figure 2 illustrates the factors influencing the decision to adopt second-hand trading websites. The results shows that 100% of the respondents agree that cost savings is a primary and dominant factor that influence their decision to adopt secondhand trading. Following this, 75.50% or 41 respondents cited the variety of items available as an important factor. Next, both environmental sustainability and convenience were each selected by 47.3% of respondents. In addition, 20 respondents indicated that their previous positive experience influenced their decision. Lastly,

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Fig. 1 Most used 2nd hand trading websites

29.10% of respondents mentioned that the trust in the platform is one of the key factor that influence their choice. Based on Fig. 3, 100% of the respondents believe that “search filters” are important for UCSI eMarket Hub’s features. Additionally, 90.90% or 50 respondents consider “listing category” is an essential feature. User registration or Login is deemed essential by 89.10% of the respondents. 43 respondents or 78.20% think that the ability to edit or manage item listings is crucial. Profile visiting is selected by 34.50% of respondents, and 38.20% believe that viewing order is another important feature. Figure 4, it shows that the majority of respondents (63.30%) prefer to trade exclusively within the UCSI community. A smaller portion of participants would

Fig. 2 Factors that influence decision of using 2nd hand trading websites

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Fig. 3 Desired features for UCSI eMarket hub

like to include users outside UCSI in UCSI eMarket Hub. Additionally, 25.20% of respondents have no preference regarding the inclusion of non-UCSI members. This results depicts a strong inclination towards trading within the UCSI community while acknowledging a minority who are open to trade with outsiders.

Fig. 4 Preference for trading within or outside UCSI community

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5 Implementation Figure 5. shows the home page of UCSI eMarket Hub. It allows existing users to sign in with their registered email address and password. For new users, they can click on “Register here” to access the registration page. For users who wish to start selling, they can click on “I am a Seller” which will direct to the login page as seller. When users log into their account, they will be directed to this page which displaying the product list as shown in Fig. 6. On the left side, there is a product category section that organizes listings into different groups. Additionally, the search bar allows users to search for what they want directly. Figure 7 shows the seller’s home page, which displays a product list of the items they have listed for sale.

Fig. 5 Home page

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Fig. 6 User home page

Fig. 7 Seller home page

6 Conclusion In a nutshell, the development of the UCSI eMarket Hub has reflected the project’s goal which is to create a specialized platform for the UCSI community to engage in second-hand trading efficiently. UCSI eMarket Hub aims to provide a centralized and user-friendly marketplace platform within the university where students and staff members can buy and sell items with ease and security, UCSI eMarket Hub as a comprehensive platform that facilitates the buying and selling activities between buyers and sellers has been successfully designed

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and implemented. The key features include simple and user-friendly interfaces for account registration and sign-in, intuitive search and listing functionalities, and so on. Beyond the operational success, UCSI eMarket Hub has addressed broader environmental and sustainability objectives. By facilitating the reuse of items such as textbooks, furniture, and electronics, the platform supports eco-friendly practices and contributes to reducing waste. This focus on sustainability not only benefits the environment but also promotes a culture of resource appreciation within the university community. Through working on UCSI eMarket Hub, I have gained immense educational experience. It has equipped me with valuable insights into the complexities of system development and project management. Although there were challenges encountered during the development of this project, the solutions developed throughout this project have significantly expanded my technical and problem-solving skills. This hands-on experience has not only deepened my understanding of digital marketplace solutions but also reinforced the importance of perseverance and creativity in tackling real-world problems. Lastly, this project highlights on the value of how digital solutions can improve campus life. This project has established a useful model for leveraging technology to solve real-world community problems with tech-based solutions and it demonstrated how innovative platforms can drive positive change. The UCSI eMarket Hub has achieved its goal of creating a practical, impactful solution for second-hand trading while aligning with broader sustainability goals. The platform’s implementation has set a benchmark for future developments and serves as an inspiring example of how educational institutions can harness technology to advance both community well-being and environmental stewardship. The project not only enhances campus resource management but also opens avenues for continued innovation and improvement in the realm of digital marketplace solutions.

References 1. Y. Hristova, “The Second-Hand Goods Market: Trends and Challenges,” Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, vol. 8, no. 3, pp. 62–71, 2019, https:// doi.org/10.36997/ijusv-ess/2019.8.3.62. 2. United Nations, “Sustainable Development Goals,” United Nations Sustainable Development. https://www.un.org/sustainabledevelopment/sustainable-development-goals/. 3. J. Z. Bratich, “The Flea-Market of History: Capital Remains,” Journalism & communication monographs, vol. 25, no. 3, pp. 282–289, Sep. 2023, https://doi.org/10.1177/152263792311 82954. 4. R. D. Buzzell, “Market Functions and Market Evolution,” Journal of Marketing, vol. 63, p. 61, 1999, https://doi.org/10.2307/1252101. 5. J. Lingel, “Notes from the Web that Was: The Platform Politics of Craigslist,” Surveillance & Society, vol. 17, no. 1/2, pp. 21–26, Mar. 2019, https://doi.org/10.24908/ss.v17i1/2.12939.

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6. M. D. Petrović, E. Ledesma, A. Morales, M. M. Radovanović, and S. Denda, “The Analysis of Local Marketplace Business on the Selected Urban Case—Problems and Perspectives,” Sustainability, vol. 13, no. 6, p. 3446, Mar. 2021, https://doi.org/10.3390/su1306 3446. 7. “About Mudah - Mudah.my,” Mudah.my - Malaysia’s largest marketplace. https://www.mudah. my/about/. 8. “Mudah.my | LinkedIn,” my.linkedin.com. https://www.linkedin.com/company/mudahmy (accessed Feb. 13, 2024). 9. “Malaysia’s Largest Marketplace - Buy & Sell Your New and Preloved Items Mudah.my,” Mudah.my - Malaysia’s largest marketplace. https://www.mudah.my (accessed Feb. 15, 2024). 10. “Furniture & Decoration Items For Sale In Kuala Lumpur,” https://www.mudah.my/kuala-lum pur/furniture-and-decoration-for-sale. 11. “sharp refrigerator,” https://www.mudah.my/sharp+refrigerator+-105649493.htm. 12. “Carousell Group | LinkedIn,” sg.linkedin.com. https://sg.linkedin.com/company/carousell group#:~:text=Carousell%20was%20founded%20by%20Siu (accessed Feb. 13, 2024). 13. A. Hussain, E. O. C. Mkpojiogu, N. B. Yahaya, and N. Z. B. A. Bakar, “A mobile usability assessment of Carousell mobile app,” AIP Conf, vol. 2016, 2018, https://doi.org/10.1063/1. 5055455. 14. “Carousell Malaysia - Online Marketplace to Buy and Sell Items for Free Anywhere in Malaysia,” Carousell. https://www.carousell.com.my. 15. “Sharp washing machine 10kg,” Carousell. https://www.carousell.com.my/p/sharp-washingmachine-10kg-1285160192/?t-id=10591829_1707959140842&t-referrer_browse_type=sea rch_results&t-referrer_page_type=search&t-referrer_request_id=UXEZHnYpNSDLio8s&treferrer_search_query=sharp%20washing%20machine%2010kg&t-referrer_search_query_ source=direct_search&t-referrer_sort_by=popular&t-tap_index=1. 16. “About Ads in Marketplace,” Facebook Business Help Center. https://www.facebook.com/bus iness/help/1648521258544455?id=150605362430228. 17. S. Lile, “15 Benefits of Facebook Marketplace for Business,” Small Business Trends, Jan. 04, 2023. https://smallbiztrends.com/2023/01/facebook-marketplace-for-business.html. 18. “What is Facebook Marketplace? - Definition from WhatIs.com,” WhatIs.com. https://www. techtarget.com/whatis/definition/Facebook-Marketplace. 19. “Marketplace,” www.facebook.com. https://www.facebook.com/marketplace. 20. “Dapur Gas/Stove,” www.facebook.com. https://www.facebook.com/marketplace/item / 404215768687966/?ref=browse_tab&referral_code=marketplace_top_picks&referral_story_ type=top_picks (accessed Feb. 15, 2024). 21. S. Das, “All about Agile SDLC (Software Development Life Cycle),” BrowserStack, Jul. 25, 2023. https://www.browserstack.com/guide/agile-sdlc. 22. “What Is the Agile SDLC and How Does It Work? | Synopsys,” www.synopsys.com. https:// www.synopsys.com/glossary/what-is-agile-sdlc.html.

Investigating LLMs Potential in Software Requirements Evaluation Najlaa Alsaedi, Ahlam Alsaedi, Amjad Almaghathawi, Mai Alshanqiti, and Abdul Ahad Siddiqi

Abstract LLMs have made a big splash on social media by changing how trends catch on and keeping users engaged. LLMs now have the application and understanding of software engineering activities, including requirement engineering, designing, coding, testing and software security. LLMs can help accelerate the process and save cost, time, and enhance security. This paper investigates software engineering activities using LLMs but focuses on requirements evaluation on the generated requirements from different LLMs. The three LLMs used in our work are GPT3.5, through ChatGPT bot; Palm 2, through Poe bot; and Claude 1, through Poe bot. We investigated if LLMs have the understanding and can evaluate the provided requirements according to software quality attributes. We consider the requirements evaluation a main process since all other processes depend on it. Keywords Requirements evaluation · Security · Social media · Quality criteria · LLM · GPT3.5 · Palm2 · Claude1

1 Introduction Humans are born with the natural ability to speak and understand language, which continues to grow throughout the years. Humans can communicate because of this ability. However, unless they are programmed with artificial intelligence (AI) methods and techniques, computers are incapable of reading, writing, or comprehending natural language. In theory, Language Models (LM) can give computers this capability. Social media connects people from all over the world. It has become vital for businesses to engage with their audience and create communities. LLM on social media refers to Large Language Models, which use advanced AI technology for N. Alsaedi · A. Alsaedi · A. Almaghathawi · M. Alshanqiti · A. A. Siddiqi (B) Taibah University, Madinah 41477, Saudi Arabia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_18

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Natural Language Processing (NLP). These models excel at language tasks like translation, sentence prediction, and generating text. Requirements engineering is considered as a pivotal point in the interaction between technology and ethics [1]. Beginning a product development life cycle with the requirements engineering process encourages better communication and cooperation among stakeholders, providing a forum for discussing ethical issues [2], such as those pertaining to AI’s reliability, and integrating them into the process in a tangible way. Based on the IEEE standards for requirements specification, a number of researchers and standards organizations have determined a set of quality attributes that are essential for requirements engineering [3]. The close relationship that exists between natural language and requirements encourages researchers to introduce Natural Language Processing (NLP) for requirements engineering [4]. In order to support human analysts in performing various tasks on textual requirements, such as identifying and resolving language issues [5], NLP for requirements engineering aims to apply NLP tools, techniques, and resources to the requirements engineering processes, which improves the requirements’ quality. However, in recent times, Large Language Models (LLM) have attracted a great deal of attention because of their noticeably enhanced performance in NLP tasks [6]. This chapter aims to investigate the potential of LLM to help in requirements evaluation process. We take the requirements produced by [7] and assess them using three LLMs. The evaluation outcomes from LLMs and human experts from [7] are compared. The remaining structure of this chapter is organized as follow: Sect. 2 provides background information about LLM and software engineering. In addition, we investigate the literature to summarize the work done on software engineering with respect to LLM. After that, our methodology is described in Sect. 3, and our experimentation and results are presented in Sect. 4. Finally, Sect. 5 concludes our chapter.

2 Background and Literature Review Humans are born with the natural ability to speak and understand language, which continues to grow throughout the years. Humans can communicate because of this ability. However, unless they are programmed with artificial intelligence (AI) methods and techniques, computers are incapable of reading, writing, or comprehending natural language. In theory, Language Models (LM) can give computers this capability. Language is a natural ability in human beings that develops from childhood and grows throughout the person’s lifetime. This ability enables humans to communicate. On the other hand, computers don’t have this ability by nature, unless it is equipped with AI methods and techniques to enable computers to read, write, and understand the natural language. Technically, this ability can be added to computers through Language Models (LM). The literature has given LM research a great deal of attention, which developed from Statistical Language Model (SLM) to Natural

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Language Models (NLM) ending with Large Language Models (LLM). The basic idea of SLM is to perform specific language tasks, such as predicting the next word. It depends on Markov’s assumption. After that, NLM exhibited several advancements that aimed to provide end-to-end solutions for different Natural Language Processing (NLP) tasks. These advancements are achieved through the use of neural networks, e.g., multi-layer perceptron (MLP) and recurrent neural network (RNN). Nowadays, LLMs are having a big influence on the NLP community by scaling NLM in terms of model size, e.g., increasing number of parameters, and data size. For example, GPT3 which is an LLM has been trained using 175B parameters. Another LLM, BERT also has been trained using 330 M parameters. They have also received training from an extensive amount of publicly available data on the internet. These models are therefore referred to as Large Language Models (LLMs) [8]. A. Large Language Model (LLM) LLMs are now revolutionizing social media interactions, providing a multitude of benefits across various domains. Businesses strategically influence LLMs to tap into public sentiment on social platforms and customer reviews, empowering market research and brand management. • Requirement elicitation, where the requirements are gathered from stakeholders. • Requirement specification, where the requirements are documented. • Requirement validation, where the requirements are checked against several defeats, such as incompleteness and inconsistency [2]. In this research, we investigate the potential of LLM to assist in requirements validation. • Requirements Engineering is an essential phase in software development. It concerns understanding of what the user needs. Good requirement engineering can reduce the amount of effort needed for rework later in the software development cycle. Requirement engineering can be conducted using the following steps. Requirements engineering is considered as a pivotal point in the interaction between technology and ethics [1]. Beginning a product development life cycle with the requirements engineering process encourages better communication and cooperation among stakeholders, providing a forum for discussing ethical issues [2], such as those pertaining to AI’s reliability, and integrating them into the process in a tangible way. Based on the IEEE standards for requirements specification, a number of researchers and standards organizations have determined a set of quality attributes that are essential for requirements engineering [3]. Social media connects people from all over the world. It has become vital for businesses to engage with their audience and create communities. LLM on social media refers to Large Language Models, which use advanced AI technology for Natural Language Processing (NLP). These models excel at language tasks like translation, sentence prediction, and generating text. B. Software Engineering Processes The close relationship that exists between natural language and requirements encourages researchers to introduce Natural Language Processing (NLP) for requirements

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engineering [4]. In order to support human analysts in performing various tasks on textual requirements, such as identifying and resolving language issues [5], NLP for requirements engineering aims to apply NLP tools, techniques, and resources to the requirements engineering processes, which improves the requirements’ quality. However, in recent times, Large Language Models (LLM) have attracted a great deal of attention because of their noticeably enhanced performance in NLP tasks [6]. The field of software engineering examines methodical and economical approaches to software development. These methods support software engineering approaches in software development [1]. The main phases included in software engineering can be listed as follow: There are many concepts behind our topic as it combines two disciplinaries software engineering and LLM. This section provides a small background about software engineering and LLM. In addition, it investigates the literature and provides a literature review for the work conducted in each phase of software engineering in response to LLM. This paper aims to investigate the potential of LLM to help in requirements evaluation process. We take the requirements produced by [7] and assess them using three LLMs. The evaluation outcomes from LLMs and human experts from [7] are compared. In the second section, we provide background information about LLM and software engineering. In addition, we investigate the literature to summarize the work done on software engineering with respect to LLM. After that, our methodology is described in Sect. 3, and our experimentation and results are presented in Sect. 4. Finally, Sect. 6 concludes our paper. • Design is aimed to convert the requirements specified in requirements engineering phase into a structure that is suitable for implementation [2]. • Implementation is concerned with translating the design into source code and real program [2]. • Testing ensures that software is functioning correctly. It can be done at unit level during the implementation, and at system level during the integration of the system [2]. C. LLM for Software Engineering (LLM4SE) Traditional Language Models (LMs) have been fundamental building blocks in the field of language processing that have been established for text generation and understanding [9]. In the field of software engineering, many studies have been established to leverage NLP techniques in software engineering processes: requirements engineering, design, implementation, and testing [10–12]. With the development of computational power of computers, and the advancements in machine learning techniques, in addition to the ability to access very large amounts of data, all these factors have led to an important transformation to the emergence of Large Language Models (LLMs) [9]. Software Engineering is one of the areas that is highly affected by LLM due to the ability of LLM to deal with and generate large texts, such as requirements and codes [9].

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Fig. 1 Literature review for LLM in software engineering processes

In this section, we provide a review of the literature in the field of leveraging LM for each phase in software engineering, see Fig. 1. i. LLM for Requirements Engineering (LLM4RE) In this section, we would like to have a deeper look to the work and studies conducted in the field of requirements engineering particularly as our work targets this process specifically. Mainly, most of the work done for requirements engineering was using traditional NLP techniques to classify requirements into functional/non-functional requirements [13], or to detect anomalies in requirements such as incompleteness and ambiguity [5, 14–16]. On the other hand, little work has been done investigating LLM for requirements engineering as it is a new trending topic. Table 1 summarizes the related work of NLP and LLM for requirements engineering. Romani et al. [7] investigated the potential of using ChatGPT to assist in requirement elicitation processes. They formulated six questions to elicit requirements using ChatGPT. The same six questions were given to 5 experts in requirement engineering and collected 36 responses, 6 from ChatGPT + 30 from the experts. Those responses were evaluated over seven different requirements quality attributes by another five requirement engineering experts. They found that ChatGPT-generated requirements are highly Abstract, Atomic, Consistent, Correct, and Understandable. However, ChatGPT-generated requirements received lower scores in Unambiguity and Feasibility, which are the main causes for the problem of “Hallucination”. Pascal et al. [6] presented an algorithm based on language-transformers to automate the generation of main user requirements, using Amazon Echo reviews as a data source for their approach. Their methodology was to first capture the most critical requirements and employ the opportunity matrix to prioritize user needs. Then,

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Table 1 Literature review of LLM for requirements engineering Paper

Task

Technique used LLM

[7]

Requirements elicitation

✓

[6]

Requirements extraction from user reviews

✓

NLP

✓

[19]

✓

[20] [13]

Requirements classification

[17]

Anomaly detection in requirements

✓ ✓

[5]

✓

[14]

✓

[15]

✓ ✓

[16] [21]

✓

Requirements correlation analysis

✓

[4] [17]

Structured requirements generation

[22]

✓ ✓

utilizing the transformers to implement summary and sentiment analysis using ratings and users’ reviews. Resulting in a summary of key user needs, help requirement engineers in eliciting main user requirements. This paper [17], proposed by Ray et al., used LLM to convert the requirements from its human-readable format to a machine-readable format, so it can be used later for generating system design. The aim of this paper is to provide an approach for system design and implementation toward Model-Based Systems Engineering (MBSE). They utilized 3 LLM: aeroBERT-NER, aeroBERT-Classifier and flair/ chunk-english. Luitel et al. [18] used large language models (LLMs) to detect incompleteness in natural language requirements. Their approach utilized BERT’s masked language model (MLM) to create contextualized predictions for filling masked slots in requirements. They used 40 requirements documents from the PURE dataset that cover 15 domains. Then, filter the predictions to find a balance between successfully recognizing requirements’ omissions and reducing noise. They concluded that BERTs can effectively predict the terminology missing from requirements. Also, their filter notably reduces noise in predictions, increasing BERT’s efficiency as a tool for requirement completeness evaluation. However, simulating incompleteness by withholding content from existing requirements may not accurately represent incomplete requirements experienced in real-world scenarios. In addition, using half of the PURE dataset may not reflect all possible scenarios for different industries and contexts. ii. LLM for Design

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When we talk about design in software engineering, we usually mean either System design or User Interface (UI) design. For system design, there was an absence of studies of system design using LLM, no middle stage (system design), most of the studies were concerned with text-to-code using LLM. There are different studies working to enhance the process like [23], which aims to support the development of AI-native services by AI chain engineering. For UI design, there was a variance of studies about UI in general, There were studies about understanding and interacting with UIs using LLM [24], and there were studies on how to generate GUI using LLM divided as follows: • Prototype: The main goal is to help in RE so a customer can imagine how the application looks. Generate editable design based on text input it is not easy to edit since there is a missing design platform feature [25], or generate UI images based on text it uses Diffusion models [26]. • Wireframe: A pre-step before designing UIs. This study [27] focuses on deep learning. It collects data from Android UI and makes general wireframes based on the many combined UI results. • GUI: This study [28] collects data from different websites based on HTML tags and labeled data. The user explains in text what exactly they want: button, text, and section with metadata (width, height, alignment, etc.…). Then it generates GUI with the provided description and provides the structure of which tags with their order. There is still a lot of work in this field, especially in UI responsiveness. Each study focuses on a specific device type which is understandable because of the lack of DB for GUIs and GUIs components. Also, there is a lack of IOS GUIs generated using LLM. iii. LLM for Implementation In the field of LLM for implementation, there were attempts to directly convert natural text into code [23], allowing the emergence of a natural language interface for coding instead of using programming languages. On the other hand, many studies have been conducted on using LLM to assess the efficiency and security of a written code [29–34]. Sakib et al. [29] examined the correctness and effectiveness of the code generated by ChatGPT, an OpenAI chatbot that uses NLP to comprehend conversations in a human-like manner. Their dataset consists of 128 programming problems ranging in complexity from Leetcode. Even though ChatGPT is excellent at solving well-specified, structured problems, it does not always produce runtime-efficient or memory-efficient solutions. Even with feedback provided by the Leetcode platform, there is a significant number of inaccurate solutions. A benchmarking framework, EvalPlus, is presented in this paper [30] to evaluate the functional correctness of code synthesized using LLMs. EvalPlus enhanced evaluation datasets with automatic test input generators that uncovered previously undetected incorrect code. According to a study, 19 popular LLMs have a pass rate that is reduced by 13.6–15.3% at a given threshold.

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A generative AI technique, ChatGPT, has served as a programming assistant within the software engineering field. This study [31] aims to evaluate ChatGPT’s performance in code generation, program repair, and code summarization tasks. Despite its effectiveness on common programming problems, ChatGPT has a limited attention span and requires prompt engineering to achieve optimal results. LLMs may have a significant influence on academic work efficiency, as discussed in this editorial [32]. It explores ethical issues of fair use and the bias present in LLM-based chatbots. The editorial examines the drawbacks and utility of LLMs in academic writing, instruction, and programming, and it comes to a conclusion by highlighting the necessity for effective use, assessing bias, exercising caution with regard to correctness, and acknowledging the LLMs’ promise as academic tools in the future. This extensive literature review [33] examines the implementation of NLP techniques, particularly transformer-based large language models (LLMs), in programming tasks supported by artificial intelligence. Code creation, completion, translation, refinement, summarization, defect detection, and clone detection have all benefited from the use of LLMs. Incorporating NLP approaches with software naturalness offers a number of challenges and opportunities, which are explored in this study along with the potential for enhanced coding assistance in software development, including the creation of mobile apps using Apple’s Xcode. In another recent study, Khoury et al. [34] illustrate a comprehensive analysis of ChatGPT security in the generated code. The study is based on 21 problems in five different programming languages (C++, C, Java, Python, and HTML). Each program highlights specific vulnerability risks (e.g. An SQL injection can occur when a program interacts with a database). The used dataset is available on the author’s GitHub repository. ChatGPT generated the correct code only for five problems and after giving input that triggers the vulnerability and asking to enhance the program, it generated the correct code for an additional seven problems. However, ChatGPT generates vulnerable code but doesn’t generate an attack code. Users who ask about security issues specifically get an answer, so it’s better to use it as a pedagogical tool. iv LM for Testing In this paper [35], Shengcheng Yu et al. explore the use of large language models (LLMs) in the generation and migration of test scripts for mobile applications. By exploring the potential of LLMs as a versatile tool for test automation, they aim to address the limitations of existing test script generation approaches. The investigation of scenario-based test generation, cross-platform test migration, and cross-app test migration was conducted using ChatGPT. They evaluate LLM’s adaptability to a variety of devices, systems, user interfaces, app architectures, and interaction patterns. It involves creating and migrating test scripts based on given scenarios, assessing LLM’s ability to handle operating system variations, and dealing with differences among apps sharing the same functionality. LLM capabilities in test script generation and migration are promising. Based on the state of the application under test (AUT), LLMs can adapt their generation and

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migration processes. Due to this adaptability, LLMs are able to create test scripts that accurately reflect the interactions and behaviors of apps. Moreover, LLMs have a number of drawbacks, such as the need for human effort, context memory, and random API usage. Although LLMs have limitations, the investigation shows that they have a high potential for enhancing mobile app testing methodologies. As the topic of LLM is a recent trending topic, there is still much work to be done in the field of LLM for software engineering to support various phases of software engineering. However, to the best of our knowledge, there is no study that aims to use LLM for requirements evaluation. Therefore, in this study, we investigate the potential LLM can help in the requirement evaluation process.

3 Research Methodology Our research is based on the results from [7]. That research was concerned with the potential of LLM to assist in requirements elicitation. Their method was to use the ChatGPT to generate a list of requirements. Then, these LLM-generated requirements were evaluated by experts in software engineering. However, our work is to investigate the potential of LLM to help in requirements evaluation. Therefore, we take the requirements generated by [7] and use three LLMs to evaluate these requirements. After that, we compare the evaluation results from LLMs with those from human experts [7]. Figure 2 illustrates the steps of our work. A. Requirements Quality Criteria

Fig. 2 Steps of our research

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Due to the time limitation and challenges encountered in finding an expert to help us at this time, we examined and concentrated on the seven quality criteria used by [7], which are Abstraction, Atomicity, consistency, correctness, unambiguity, understandability, and feasibility. As a result, using the same quality criteria assists us in dedicating and constructing higher levels of confidence. Moreover, here is a definition and clarification of each of these quality criteria for better understanding. • Abstraction: the requirements state what the application must perform but do not specify how. • Atomicity: every requirement is clearly defined and distinguished from others without being combined with them. • Consistency: requirements must have no contradictions among them. • Correctness: all requirements shall be met by the final system to accomplish a certain purpose. • Unambiguity: The requirements should only have one possible interpretation. • Understandability: The requirements are perfectly comprehended without difficulty. • Feasibility: All requirements can be implemented with the available technology and human resources. B. Evaluating the quality of requirements using various LLMs The three LLMs used in our work are GPT3.5, through ChatGPT bot; Palm 2, through Poe bot; and Claude 1, through Poe bot. Description of these LLMs is provided in the coming sections. a. GPT3.5 GPT3.5 is a LLM developed by OpenAI. It was trained using data that was licensed from outside sources and publicly accessible data, such as data from the internet. Next, Reinforcement Learning from Human Feedback (RLHF) was used to fine-tune the model. This fine-tuning technique depends on human feedback on the output of the model and rewarding the generator model. The architecture of GPT3.5 is a transformer architecture with 175B parameters [36]. Our experiment on GPT3.5 takes place on the official OpenAI ChatGPT chatting website. Generally, GPT3.5 scores each criterion in addition to clarification for each score. The clarification in most criteria is described with general words. However, GPT3.5 exhibits good results on clarifying the “Unambiguity” criteria as it detects and specifies the ambiguous words precisely. For example, this is a response from GPT3.5 regarding one of the requirements: “The requirements are generally clear and straightforward, with minimal room for misinterpretation. However, terms like “diverse enough,” “correcting biases,” and “adjusting the model” could benefit from more precise definitions.” As we can see, it detects and specifies the words that could make the requirement ambiguous. b. Palm2

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Palm2 is a LLM developed by Google, where its architecture is based on the Transformer [37]. Our experiment on Palm2 takes place on Poe. Poe (Platform for Open Exploration, Quora) was used because it allows fluent testing of multiple bots at the same time [37]. Palm2 works in the same way as GPT3.5 in terms of scoring each requirement and clarifying each score. However, Palm2 provides more specific descriptions for “Abstraction”, “Unambiguity”, and “Feasibility” quality criteria. For example, here is a response from Palm2 regarding feasibility of one of the requirements: “The requirements are mostly feasible and can be implemented with the available technology and resources. However, some of the requirements, such as “Continuous improvement” and “Auditability and accountability” could require significant effort and resources to implement and may need to be prioritized or phased in over time.” It specifies the areas that could be difficult or require hard work and thus may affect the feasibility of a system. c. Claude 1 Claude 1 is a LLM developed by Anthropic. Our experiment on Claude 1 takes place on Poe. Claude 1 works in the same way as GPT3.5, and Palm2 in terms of scoring each requirement and clarifying each score. However, Claude 1 clarification is very general and doesn’t provide specific details. For example, this is a response from Claude 1: “The requirements seem achievable with current technology and resources.”

4 Experimentation and Results A. Experimental Settings There is a lot of textual and numerical data used for experimentation in this research. For simplicity, we annotate some of them with special characters p, q and a: • Prompt p: prompt used by [7] to ask LLM to generate requirements. • Answer a: answer from LLM regarding the requirement prompt p: this is taken from [7]. • Prompt q: prompt used in our work to ask LLM to evaluate the requirement. • Human average evaluation is the average evaluation of requirements a, numerical statistics taken from [7]. • LLM evaluations are numerical statistics generated by LLM as answers to prompt q. Figure 3 shows the prompt q that is used with LLM to evaluate provided requirements. There are six questions p that were prompted to LLM to get the requirements [7]. For our work, we evaluate all six generated requirements to get more insights about the behavior of each LLM. To avoid memory retention, each requirement is

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Fig. 3 The propmt q that is used to evaluate provided requirements

evaluated in a different chat. This ensures that the scores of one requirements is not affected by the previous one. Figure 4 illustrates the flow of chat with LLM that is used in our work. B. Results Evaluating our results in. Figures 5 and 6 show that models give high scores for quality attributes more than what expert humans give. Palm 2 is the closest one to the human expert result and ChatGPT 3.5 gives the faraway result compared to expert humans. Models give better results for the Abstraction quality attribute and give the worst result for the Atomicity quality attribute. Consider that requirements have been generated by ChatGPT 3.5 and it has given high scores that could mean it’s biased to its result. Detailed results for each quality attribute are as follows: Abstraction: Claude and Palm 2 have similar results and are close to expert humans. ChatGPT 3.5 result is closer to Claude than Palm 2 and expert humans. Atomicity: Palm 2 has better results followed by Claude and finally ChatGPT 3.5 results compared to expert humans. Consistency: Palm 2 has the closest results then ChatGPT 3.5 followed by Claude compared to expert humans.

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Fig. 4 The flow of chatting with LLM in our experiment

Correctness: Palm 2 has very close results followed by Claude and finally ChatGPT 3.5 results compared to expert humans. Unambiguity: Palm 2 has the closest results than ChatGPT 3.5 followed by Claude compared to expert humans. Understandability: Palm 2 has the closest results then ChatGPT 3.5 followed by Claude compared to expert humans. Feasibility: Palm 2 has better results followed by Claude and finally ChatGPT 3.5 results compared to expert humans.

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Fig. 5 Each quality attribute for models compared to the expert average humans result

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Fig. 6 The average of each quality attribute for models compared to the expert average humans result

5 Conclusion LLMs can process vast amounts of data from your social media platforms to identify trends, understand audience demographics, and gauge the effectiveness of your campaigns. This research aimed to investigate the possibility of LLM to help in requirements evaluation. The experiment was done using 3 LLMs GPT3.5, Palm2, and Claude1. The requirements provided to LLMs are evaluated against these quality criteria: Abstraction, Atomicity, Consistency, Correctness, Unambiguity, Understandability, and Feasibility. The evaluation results from LLMs are compared to human evaluation on the same requirements. Our result shows that there is still a lack in LLMs understanding of quality attributes and the correlation between them. A. Future Work While this study emphasizes the potential of LLM in requirements evaluation, future research should explore more software processes. For instance, this could involve having the LLM generate code, evaluating the generated code, and comparing it to human experts, which was not addressed in the current analysis. As advancements in LLM continue to progress, its interpretive capabilities are expected to improve.

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Advancements and Applications of Multimodal Large Language Models: Integration, Challenges, and Future Directions S. K. Ahammad Fahad, Daniel Wang Zhengkui, Ng Pai Chet, Nicholas Wong, Aik Beng Ng, and Simon See

Abstract Multimodal Large Language Models (LLMs) have emerged as a significant advancement in artificial intelligence, capable of integrating and processing diverse data types such as text, images, and audio. This paper comprehensively explores multimodal LLMs, detailing their evolution from early unimodal models to sophisticated architectures like BERT, GPT-4, CLIP, DALL-E, and Perceiver. We highlight key milestones, including introducing transformers, which have enabled these models to leverage multimodal capabilities effectively. Current trends in multimodal LLMs involve the integration of more extensive and diverse datasets, realtime processing capabilities, and enhanced model interpretability. These advancements have expanded their applications across various domains, from improving healthcare diagnostics to personalizing entertainment content and enhancing product recommendations in e-commerce. Despite significant progress, challenges must be addressed to fully realize the potential of multimodal LLMs. Data scarcity and quality, high computational requirements, interpretability and explainability, and biases in model outputs present obstacles to their widespread adoption. Future research should focus on developing efficient model architectures, advanced interpretability tools, S. K. A. Fahad (B) · D. W. Zhengkui · N. P. Chet · N. Wong Singapore Institute of Technology, Punggol, Singapore e-mail: [email protected] D. W. Zhengkui e-mail: [email protected] N. P. Chet e-mail: [email protected] N. Wong e-mail: [email protected] A. B. Ng · S. See NVIDIA, Suntec City, Singapore e-mail: [email protected] S. See e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025 W. M. S. Yafooz and Y. Al-Gumaei (eds.), AI-Driven: Social Media Analytics and Cybersecurity, Studies in Computational Intelligence 1180, https://doi.org/10.1007/978-3-031-80334-5_19

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bias detection and mitigation strategies, robust cross- modal generalization techniques, and promoting sustainable AI practices. By addressing these challenges, the AI community can harness the full potential of multimodal LLMs, leading to more powerful, versatile, and fair models that drive innovation and deliver significant benefits across various domains.

1 Introduction The rapid advancements in artificial intelligence (AI) have ushered in a new era of technological innovation, with Multimodal Large Language Models (LLMs) at the forefront of this transformation. Unlike unimodal models that process a single data type, multimodal LLMs are designed to integrate and analyze multiple data types simultaneously—such as text, images, and audio—mirroring the complexity of human communication, which inherently involves multiple sensory inputs [1–3]. This capability significantly enhances AI’s ability to understand and generate humanlike responses, making multi- modal LLMs indispensable in various applications [4–6]. The primary objective of this paper is to provide a comprehensive overview of multimodal LLMs, encompassing their evolution, current trends, technical foundations, industry applications, and the challenges they face. By examining these aspects, we aim to elucidate the transformative potential of multimodal LLMs across domains such as healthcare, entertainment, e-commerce, finance, education, and autonomous systems. Understanding the development and applications of these models is crucial for leveraging their full potential to drive innovation and solve complex problems [7–9]. This paper’s significance lies in its detailed analysis of multimodal LLMs, offering valuable insights into their development, applications, and future potential. We aim to pave the way for more robust, efficient, and fair AI systems by addressing the technical, computational, and ethical challenges associated with these models. This contribution enhances the academic understanding of multimodal LLMs and provides practical guidance for their deployment in various industry contexts [10–12]. The structure of this paper is as follows: A. Background and Literature Review Early advancements in AI predominantly focused on unimodal data, where models were designed to handle a single type of data, such as text or images. Recurrent Neural Networks (RNNs) were pioneers in natural language processing (NLP), adept at handling sequential data like text but struggled with long-term dependencies [13]. Convolutional Neural Net- works (CNNs) revolutionized image processing by effectively capturing spatial hierarchies in images [14]. The transition from unimodal to multimodal models marked a significant shift in AI research, driven by the realization that real-world applications often involve

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multiple data types. Integrating various data modalities promised richer, more context-aware models. Early attempts at multimodal Learning involved combining text and images for tasks such as image captioning and visual question answering (VQA), using simple concatenation techniques to merge features from different modalities [6]. A pivotal moment in the evolution of AI was the introduction of the Transformer architecture by Vaswani et al., which addressed the limitations of RNNs by using self-attention mechanisms. This allowed for parallel processing and better handling of long-range dependencies, becoming the backbone for many subsequent model advancements [15]. Transformers’ success in NLP was first demonstrated by models such as BERT (Bidirectional Encoder Representations from Transformers), which achieved stateof-the-art results on various NLP tasks [16]. The ability of transformers to capture complex dependencies in data paved the way for their application in multimodal contexts. Significant contributions from various groundbreaking models have driven the evolution of multimodal AI: • CLIP (Contrastive Language–Image Pre-training): Developed by Radford et al., CLIP leverages a large dataset of images and their associated text descriptions to learn a joint representation of images and text. Using a contrastive learning approach, CLIP can understand and generate photo captions and find images that match a given textual description [4]. • DALL-E: Also developed by OpenAI, DALL-E extends the capabilities of multimodal models by generating high- quality images from textual descriptions. This model uses a variant of the transformer architecture to decode textual inputs into images, showcasing the potential of transformers in creative applications [5]. • Perceiver: Introduced by Jaegle et al., Perceiver is de- signed to handle inputs from multiple modalities, including text, images, and audio. Its flexible architecture allows it to process and integrate various data types efficiently, demonstrating the adaptability of transformer- based models to different modalities [7]. These models have significantly advanced the field of multimodal AI by demonstrating the effectiveness of integrating different data types. They highlight the importance of multimodal Learning in creating more versatile and context-aware AI systems. B. Technical Overview of Multimodal LLMs Multimodal LLMs employ various architectures to process and integrate data from different modalities. The evolution of these architectures has been marked by significant advancements, from essential neural networks to sophisticated transformerbased models. • Early Models: The journey began with neural networks designed to handle unimodal data. CNNs were adept at processing image data, while RNNs were used for sequential data such as text and audio [13, 14].

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• Transition to Multimodal Models: The shift to multi-modal models was driven by the need to handle real- world applications involving multiple data types. Integrating various data modalities promised richer, more context-aware models [1, 2]. • Introduction of Transformers: The Transformer architecture addressed the limitations of RNNs, using self-attention mechanisms for parallel processing and better handling of long-range dependencies [15]. • Advancement of Specific Models: Groundbreaking models like CLIP [4], DALLE [5], and Perceiver [7] have driven the evolution of multimodal models. Data representation and Fusion are crucial for the effective functioning of multimodal models. Various techniques are employed. • Early Fusion: Combining data from different modalities at the input level before feeding it into the model [3]. • Late Fusion: Processing each modality separately and combining the results at the output stage [1]. • Hybrid Fusion: Integrating data at multiple stages within the model to capture interactions at various levels [2]. Training methodologies for multimodal LLMs include supervised, unsupervised, and semi-supervised Learning, each with its advantages and applications [3, 17, 18]. C. Model Specialization and Industry Applications Multimodal LLMs have diverse applications across various industries: • Healthcare: Integrating medical data to improve diagnostics and personalized treatments [19]. • Entertainment: Enhancing content creation and personalizing user experiences [20]. • E-commerce: Improving product recommendations and customer service by analyzing product descriptions, im- ages, and user reviews [21]. • Finance: Providing insights for trading strategies and risk management by integrating textual news and market data [22]. • Education: Developing personalized learning experiences by integrating text, video lectures, and interactive exercises [12]. • Autonomous Systems: Enhancing perception and decision-making capabilities by integrating visual, auditory, and sensor data [23]. The selection of the appropriate model architecture is crucial, as each model has strengths and weaknesses that must be considered to optimize performance in various industry scenarios. D. Challenges and Future Directions Despite significant advancements, several challenges must be addressed: • Data Scarcity and Quality: The success of multimodal LLMs relies heavily on the quality and quantity of training data [24].

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• Computational Requirements: Training and deploying these models require substantial computational resources [25]. • Interpretability and Explainability: Developing interpretable models are essential for building trust and ensuring transparency [11, 26]. • Bias and Fairness: Addressing bias to prevent unfair and discriminatory outcomes is crucial [10, 27]. • Generalization Across Modalities: Integrating diverse data types to provide coherent outputs remains a significant challenge [3]. Future research should focus on developing robust training techniques, scalable architectures, and effective evaluation metrics. The AI community can create more powerful and versatile models that drive innovation across various domains by addressing these challenges. In conclusion, while multimodal LLMs have made significant strides, addressing these challenges is essential for unlocking their full potential. Focusing on future research directions will enable the development of more powerful, versatile, and fair models that deliver substantial benefits across various domains.

2 Background and Literature Review Multimodal Large Language Models (LLMs) have emerged as a significant advancement in artificial intelligence (AI) and machine learning, enabling the simultaneous processing and integration of multiple data types. This capability mirrors the complexity of human communication and cognition, which inherently involve multiple sensory inputs [1, 2]. By integrating diverse modalities such as text, images, and audio, multimodal LLMs capture richer contextual information, leading to more accurate and effective outputs [4, 5]. A. Evolution of Multimodal LLMs (1) Early Models in AI: Initial advancements in AI pre- dominantly focused on unimodal data processing. Recurrent Neural Networks (RNNs) were pioneers in natural language processing (NLP), adept at handling sequential data like text but struggled with long-term dependencies [13]. Convolutional Neural Networks (CNNs) revolutionized image processing by effectively capturing spatial hierarchies in images [14]. However, these models were limited to single data modalities, restricting their applicability in scenarios requiring integrating multiple data types. (2) Transition to Multimodal Models: The shift from unimodal to multimodal models marked a significant evolution in AI research, driven by the realization that real-world applications often involve multiple data types [1, 2]. Early attempts at multimodal Learning involved combining text and images for tasks such as image captioning and visual question answering

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(VQA), using straightforward techniques to merge features from different modalities [6]. (3) Introduction of Transformers: A pivotal breakthrough was the introduction of the Transformer architecture by Vaswani et al. [15]. By employing self-attention mechanisms, Transformers allowed for parallel processing and better handling of long-range dependencies, addressing the limitations of RNNs. This architecture became the foundation for many subsequent unimodal and multimodal model advancements. Transformers’ success in NLP was exemplified by models like BERT (Bidirectional Encoder Representations from Transformers) [16], which achieved state-of-the-art results on various tasks. The effectiveness of Transformers in capturing complex dependencies paved the way for their application in multimodal contexts. (4) Advancements in Specific Models: Building upon the Transformer architecture, several groundbreaking models have been developed: CLIP (Contrastive Language–Image Pre-training): Developed by Radford et al., CLIP leverages a large dataset of images and their associated text descriptions to learn a joint representation of pictures and text [4]. Using a contrastive learning approach, CLIP can understand and generate photo captions and retrieve images that match a given textual description. (a) DALL-E: Also developed by OpenAI, DALL-E extends the capabilities of multimodal models by generating high-quality images from textual descriptions (5). Utilizing a variant of the Transformer architecture, DALL-E decodes textual inputs into images, showcasing the potential of Trans- formers in creative applications. (b) Perceiver: Introduced by Jaegle et al., Perceiver is designed to handle inputs from multiple modalities, including text, images, and audio [7]. Its flexible architecture efficiently processes and integrates various data types, demonstrating the adaptability of Transformer-based models to different modalities. These models have significantly advanced multimodal AI by effectively integrating different data types, highlighting the importance of multimodal Learning in creating versatile and context-aware AI systems. B. Current Research Trends and Applications Recent research has focused on enhancing Transformer architectures to handle multimodal data more effectively [8, 9]. Multimodal LLMs are now being applied across various do- mains, impacting industries such as healthcare, entertainment, and e-commerce. (1) Healthcare: In healthcare, multimodal models integrate patient data from medical records, imaging studies, and genomic information, leading to more accurate diagnoses and personalized treatment plans [19]. These models enhance clinicians’ ability to make informed decisions by synthesizing diverse data sources.

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(2) Entertainment: The entertainment industry leverages multimodal LLMs to create immersive experiences and innovative content-generation tools. Models like DALL-E enable the production of high-quality visual content from textual descriptions, opening new avenues for artists and designers [20]. (3) E-commerce: In e-commerce, multimodal LLMs im- prove product recommendations and customer service by integrating text, images, and user behavior data [21]. These models enhance the shopping experience by providing personalized and contextually relevant recommendations. C. Technical Foundations (1) Model Architectures: Multimodal LLMs employ various architectures to process and integrate data from different modalities. The Transformer architecture [15] has become the cornerstone due to its ability to handle sequential data efficiently through self-attention mechanisms. Variants like BERT [16] have been extended to handle multimodal inputs by incorporating additional encoders or decoders for different data types. Other architectures include CNNs for image processing and RNNs for sequential data, often integrated into multi- modal frameworks before combining them in a unified model [13, 14]. Models like CLIP and DALL-E exemplify the use of Transformer variants to merge text and image data effectively [4, 5]. (2) Data Representation and Fusion Techniques: Effective integration of different modalities relies on data representation and fusion techniques: (a) Early Fusion: Combines data from different modalities at the input level before feeding it into the model. While straightforward, it may not capture complex interactions between modalities [3]. (b) Late Fusion: Each modality is processed separately in late Fusion, and the results are combined at the decision level. This approach maintains modality-specific features but may miss out on capturing interactions between modalities during the early processing stages [13]. (c) Hybrid Fusion: Integrates data at multiple stages within the model, leveraging the advantages of both early and late Fusion to capture interactions at various levels [2]. (3) Training Methodologies: Training methodologies for multimodal LLMs include: (a) Supervised Learning: It involves training models on labeled datasets where each input is paired with the correct output. Effective but requires large amounts of annotated data [17]. (b) Unsupervised Learning: It uses unlabeled data to identify patterns and structures within the data. Valuable for discovering hidden representations and reducing dependency on labeled data [18]. (c) Semi-Supervised Learning: Combines labeled and un- labeled data to train models, balancing the strengths of both supervised and unsupervised learning [3]. D. Key Challenges and Ethical Considerations

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Despite significant advancements, multimodal LLMs face several challenges: (1) Data Scarcity and Quality: The availability and quality of multimodal datasets are critical. Obtaining large, high-quality datasets across different modalities is challenging [3]. Poor-quality data can impair model performance and lead to erroneous conclusions [28]. (2) Computational Requirements: Training and deploying multimodal Large Language Models (LLMs) demand substantial computational resources, often necessitating high-performance computing infrastructure [25]. Additionally, the environmental impact of training large-scale models raises concerns, underscoring the need for more efficient approaches [29]. (3) Interpretability and Explainability: Understanding the decision-making processes of complex multimodal LLMs is challenging [26]. Enhancing interpretability is essential for building trust and ensuring transparency, particularly in critical applications like healthcare. (4) Bias and Fairness: Bias in training data can lead to unfair outcomes [10]. Addressing bias requires identifying and mitigating biases in data and model outputs [27, 28]. (5) Privacy and Security: Integrating various data types raises privacy concerns, especially when processing sensitive personal information [30]. Techniques like differential privacy are being explored to enhance data protection while enabling the effective use of multimodal data [31]. The evolution of multimodal LLMs reflects a significant shift in AI, moving towards models that can process and integrate diverse data types. While these advancements have tremendous potential across various industries, addressing the technical challenges and ethical considerations is crucial for responsible development and deployment. Future research should focus on enhancing data quality, reducing computational demands, improving interpretability, ensuring fairness, and protecting privacy to realize multimodal LLMs’ capabilities fully.

3 Current Trends and Applications of Multimodal LLMS Building upon the foundational developments discussed in the previous sections, this section explores the contemporary trends shaping Multimodal Large Language Models (LLMs) and their transformative applications across various domains. Recent advancements in model architectures, data representation techniques, and training methodologies have empowered multimodal LLMs to tackle complex tasks that were previously unattainable. These models are now being applied in diverse fields such as healthcare, entertainment, and e-commerce, demonstrating their versatility and potential to revolutionize multiple industries [4, 5, 7]. A. Emerging Trends in Multimodal LLMs

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Advancements in multimodal LLMs have been driven by several key trends that enhance their capabilities and broaden their applications. (1) Integration of Large and Diverse Datasets: A significant trend is the utilization of more extensive and more diverse datasets that combine text, images, audio, and video. Access to extensive multimodal datasets enables models to learn richer and more contextually nuanced representations. For instance, CLIP leverages vast datasets of images and corresponding textual descriptions to create joint representations, improving performance on tasks like image captioning and visual question answering [4]. (2) Real-Time Processing and Efficiency: Advancements in computational power and optimization techniques have facilitated the development of models capable of real-time processing. Multimodal LLMs can now process and integrate data from multiple modalities with minimal latency, making them suitable for applications requiring immediate responses, such as interactive virtual assistants and real-time translation services. The Perceiver model exemplifies this trend by efficiently handling inputs from various modalities [7]. (3) Enhanced Model Interpretability: As multimodal LLMs become increasingly complex, ensuring their interpretability and explainability has become critical. Techniques such as attention mechanisms and explainable AI (XAI) methods are being integrated into these models to provide transparency in their decisionmaking processes. This is particularly important in sensitive domains like healthcare and autonomous systems, where understanding the rationale behind model outputs is essential [11, 26] (Table 1). These trends reflect ongoing efforts to improve multimodal LLMs regarding data utilization, computational efficiency, and transparency, expanding their applicability across various sectors. B. Applications Across Various Domains The advancements in multimodal LLMs have led to their adoption in multiple industries, where they enhance decision- making processes, personalize user experiences, and provide innovative solutions. Table 1 Emerging trends in multimodal LLMs Trend

Description

Integration of larger datasets

Leveraging extensive and diverse datasets to enhance model robustness and contextual understanding [4]

Real-time processing capabilities

Developing models that can process multimodal data in real-time, enabling applications such as interactive virtual assistants [7]

Advancements in model interpretability

Implementing techniques to make models’ decision-making processes more transparent and explainable [26]

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(1) Healthcare: In healthcare, multimodal LLMs integrate diverse medical data to improve diagnostics and personalized treatment plans. By combining electronic health records, medical imaging, genomic data, and patient histories, these models provide comprehensive insights to support clinical decisions [19]. Example: A multimodal model integrating MRI scans, pathology reports, and patient demographics can assist in a more accurate diagnosis of brain tumors. Analyzing combined data allows the model to identify patterns that are not apparent when considering each modality separately. Case Study: A study demonstrated improved accuracy in predicting cancer treatment outcomes by integrating genomic sequences, clinical notes, and imaging data using a multimodal LLM, outperforming models using unimodal data [32]. 2) Entertainment: Multimodal LLMs enhance content creation and user personalization in the entertainment industry. These models generate high-quality multimedia content and tailor recommendations based on user preferences. Content Creation: DALL-E generates detailed images from textual descriptions, enabling artists and designers to quickly bring their ideas to life [5]. Personalization: Streaming services utilize multimodal LLMs to analyze user behavior, preferences, and viewing his- tory, integrating text reviews, video metadata, and interactions to recommend personalized content [20]. (3) E-commerce: In e-commerce, multimodal LLMs integrate text, images, and behavioral data to enhance product recommendations, customer service, and user engagement. Product Recommendations: By analyzing product descriptions, images, and user reviews, multimodal models provide more accurate and personalized recommendations, improving the shopping experience [21]. Customer Service: Chatbots powered by multimodal LLMs understand and respond to customer queries using text and visual inputs, enhancing customer support. (4) Finance: In finance, multimodal LLMs integrate textual news, market data, and financial reports to provide insights for trading strategies and risk management [22]. (5) Education: Educational platforms employ multimodal LLMs to create personalized learning experiences, integrating text, video lectures, and interactive exercises tailored to individual learners [12]. (6) Autonomous Systems: Multimodal LLMs enhance autonomous systems’ perception and decision-making capabilities by integrating visual, auditory, and sensor data, improving navigation and safety in applications like self-driving cars and drones [23] (Table 2).

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Table 2 Applications of multimodal LLMs across various domains Domain

Application

Healthcare

Improved diagnostics and personalized treatments by integrating diverse medical data [19]

Entertainment

Content creation (e.g., DALL-E) and personalized recommendations [5, 20]

E-commerce

Enhanced product recommendations and customer service chatbots [21]

Finance

Insights for trading strategies and risk management [22]

Education

Personalized learning experiences through multimodal integration [12]

Autonomous systems Improved perception and decision-making by integrating sensor data [23]

These applications demonstrate the transformative impact of multimodal LLMs across various sectors, showcasing their potential to innovate and enhance efficiency. C. Challenges and Future Directions Despite significant advancements, several challenges must be addressed to realize the full potential of multimodal LLMs. (1) Data Scarcity and Quality: High-quality, diverse multimodal datasets are essential for training effective models. However, collecting and curating such datasets is challenging due to data scarcity and the complexity of aligning different modalities [1]. (2) Computational Demands: Training and deploying multimodal LLMs require substantial computational resources, raising concerns about scalability and environmental impact [33]. (3) Ethical Considerations: Issues related to bias, fairness, and privacy are critical. Addressing biases in training data and ensuring the ethical use of multimodal LLMs are essential for their responsible deployment [10, 28]. (4) Future Prospects: Future research should focus on developing scalable architectures, efficient training methods, and robust evaluation metrics. Emphasizing interpretability and fairness will be crucial in building trust and expanding the adoption of multimodal LLMs [11]. Potential future applications include enhanced multimodal interactions in virtual and augmented reality, improved data alignment techniques, and integration of additional modalities like haptic feedback and biometric signals. Multimodal large language models represent a significant advancement in AI, enabling the integration and interpretation of diverse data types. Current trends showcase data integration, computational efficiency, and model transparency improvements, leading to transformative applications across various domains. Addressing the challenges and focusing on ethical considerations will be vital for these powerful models’ continued evolution and responsible deployment.

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4 Technical Overview of Multimodal LLMS Building upon the foundational developments discussed in the previous sections, this section delves into the technical underpinnings of Multimodal Large Language Models (LLMs). We examine the model architectures, data representation and Fusion techniques, and training methodologies that enable these models to effectively process and integrate diverse data types. Understanding these technical aspects is crucial for devel- oping more robust and efficient models. By dissecting the architectures and methodologies, researchers and practitioners can identify areas for innovation, customization, and optimization, thereby enhancing the utility of multimodal LLMs across various domains such as healthcare, entertainment, and e-commerce [4, 15, 16]. Building upon the foundational developments discussed in the previous sections, this section delves into the technical underpinnings of Multimodal Large Language Models (LLMs). We examine the model architectures, data representation and Fusion techniques, and training methodologies that enable these models to effectively process and integrate diverse data types. Understanding these technical aspects is crucial for developing more robust and efficient models. By dissecting the architectures and methodologies, researchers and practitioners can identify areas for innovation, customization, and optimization, thereby enhancing the utility of multimodal LLMs across various domains such as healthcare, entertainment, and e-commerce [4, 15, 16] (Table 3). This section explores each component in detail, highlighting the innovations driving the field forward and discussing the challenges that must be addressed to realize the full potential of multimodal LLMs. A. Model Architectures Multimodal LLMs employ various architectures to process and integrate data from different modalities. The evolution of these architectures has been marked by significant advancements, from essential neural networks to sophisticated transformerbased models. Table 3 Key components of multimodal LLMs Component

Description

Model architectures

Structures that define how multimodal LLMs process and integrate different data types

Data representation

Techniques for representing data from various modalities in a unified framework

Fusion techniques

Methods for combining data from different modalities to enhance model performance

Training methodologies

Strategies for training multimodal LLMs, including supervised, unsupervised, and semi-supervised Learning

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(1) From Basic Neural Networks to Transformers: Early models in AI focused on unimodal data processing using essential neural networks. Convolutional Neural Networks (CNNs) were effective for image processing by capturing spatial hierarchies [14], while Recurrent Neural Networks (RNNs) handled sequential data such as text and audio [13]. However, RNNs struggled with long-term dependencies and parallel processing. The introduction of the Transformer architecture by Vaswani et al. [15] revolutionized the field by employing self-attention mechanisms, allowing for parallel processing and better handling of long-range dependencies. Transformers became the backbone for many subsequent unimodal and multimodal models. (2) Key Multimodal Architectures: Several key architectures have significantly contributed to the development of multi-modal LLMs: (a) BERT (Bidirectional Encoder Representations from Transformers): BERT, introduced by Devlin et al. [16], employed bidirectional training of Transformers to understand the context of words based on their surroundings, achieving state-of-the-art results in various NLP tasks. (b) CLIP (Contrastive Language–Image Pre-training): Developed by Radford et al. [4], CLIP leverages a large dataset of images and their corresponding text descriptions to learn a joint representation. Using contrastive Learning, CLIP matches images with textual descriptions, enabling tasks like image retrieval and captioning. (c) DALL-E: DALL-E, also by OpenAI, extends multimodal capabilities by generating high-quality images from textual descriptions [5]. It uses a variant of the Transformer architecture to decode text into images, showcasing the creative potential of multimodal models. (d) Perceiver: Introduced by Jaegle et al. [7], the Perceiver architecture is designed to handle inputs from multiple modalities, including text, images, and audio. Its flexible, iterative attention mechanism allows it to efficiently process and integrate various data types (Table 4). These models highlight the versatility and power of Transformer-based architectures in handling and integrating multimodal data, paving the way for sophisticated applications across various domains. Table 4 Comparison of multimodal model architectures Model

Architecture

Applications

BERT

Transformer-based, bidirectional

NLP tasks [16]

CLIP

Transformer + CNN, contrastive learning

Image and text matching [4]

DALL-E

Transformer-based, text-to-image generation

Image synthesis from text [5]

Perceiver

Flexible, iterative attention

Multimodal inputs processing [7]

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B. Data Representation and Fusion Techniques Effective data representation and Fusion are crucial for multimodal LLMs to integrate information from different modalities seamlessly. (1) Data Representation: Data from various modalities is typically represented using specialized encoders that transform raw data into feature vectors. For example, the text is represented using embeddings generated by models like BERT [16], and images are encoded using CNNs [14]. Audio can be described using spectrograms processed by suitable neural networks [2]. (2) Data Fusion Techniques: Multimodal data fusion com- bines information from different modalities to produce a coherent representation. The primary data fusion techniques are early, late, and hybrid. (a) Early Fusion: Early Fusion, or feature-level Fusion, combines data from different modalities at the input level before feeding it into the model. This method captures low- level interactions but may struggle with complex relationships. Example: In multimodal emotion recognition, facial expressions, and speech features are concatenated and fed into a network to predict emotions [2]. (b) Late Fusion: Late Fusion processes each modality separately and combines the results at the decision level. It maintains modality-specific features but may miss out on early interactions. Example: In multimedia retrieval systems, image queries are processed independently, and their results are merged to produce the final retrieval list [1]. (c) Hybrid Fusion: Hybrid Fusion integrates data at multiple stages within the model, capturing interactions at various levels. Example: The Multimodal Transformer model integrates text, audio, and visual data through cross-modal attention mechanisms at different layers [8] (Table 5). The choice of fusion technique depends on the application’s requirements, including the complexity of interactions between modalities and computational considerations. C. Training Methodologies Training multimodal LLMs involves various methodologies, each with its advantages and challenges. (1) Supervised Learning: Supervised learning trains models on labeled datasets where each input is paired with the correct output. It is effective but requires large amounts of annotated data. Benefits:

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Table 5 Comparison of data fusion techniques Fusion technique

Advantages and disadvantages

Early fusion

Advantages: Simple implementation; captures low-level interactions Disadvantages: May struggle with complex interactions; requires synchronized data

Late fusion

Advantages: Maintains modality-specific features; easier to implement with existing models Disadvantages: May miss early interactions; less effective at capturing deep interdependence

Hybrid fusion

Advantages: Captures interactions at multiple levels; flexible and powerful Disadvantages: More complex to implement and tune; higher computational cost

– High accuracy with sufficient labeled data. – Clear error signals Facilitate Learning. Challenges: – Requires extensive labeled datasets, which can be costly. – May not generalize well if training data is not diverse. Example: CLIP uses supervised Learning on a dataset of image-text pairs to learn joint representations [4]. (2) Unsupervised Learning: Unsupervised Learning uses unlabeled data to discover patterns and structures, reducing dependency on labeled data. Benefits: – No need for labeled data. – Useful for pretraining models. Challenges: – Less accurate without supervision. – Difficult to evaluate performance. Example: Autoencoders learn to represent data in a lower-dimensional space without labels [18]. (3) Semi-Supervised Learning: Semi-supervised Learning combines labeled and unlabeled data, leveraging the strengths of both approaches. Benefits: – Reduces the need for extensive labeled data. – Can improve performance by utilizing more data. Challenges: – More complex training process.

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Table 6 Comparison of training methodologies Methodology

Benefits

Challenges

Supervised learning

High accuracy; Clear error signals

Requires large labeled datasets; Potential over-fitting

Unsupervised learning

No need for labels; discovers hidden structures

Less accurate; Hard to evaluate

Semi-supervised learning

Efficient use of data; improved Complex training; risk of noise performance

– Requires careful handling of unlabeled data. Example: Models like GPT-4 use unsupervised pretraining on large text corpora, followed by supervised fine-tuning [25] (Table 6). The choice of training methodology depends on factors such as data availability, desired accuracy, and computational resources. The technical foundations of multimodal LLMs encompass advanced model architectures, sophisticated data representation and Fusion techniques, and varied training methodologies. These components collectively enable the effective integration and processing of diverse data types, driving innovations across multiple domains. Understanding these technical aspects is essential for further advancements and addressing the challenges of developing more robust, efficient, and fair multimodal AI systems.

5 Model Specialization and Industry Applications Building upon the technical foundations and current trends discussed in the previous sections, this section delves into the specialization of specific multimodal large language models (LLMs) and explores their diverse applications across various industries. By examining the technical specifications and real-world implementations of models such as BERT, GPT-4, CLIP, DALL-E, and Perceiver, we aim to understand how these advanced models drive innovation and enhance capabilities in sectors like healthcare, entertainment, e-commerce, finance, education, and autonomous systems. Understanding the specialization and applications of these models is crucial for selecting the most suitable architecture for specific needs, optimizing performance, and identifying best practices for deployment [4, 15, 16]. A. Detailed Analysis of Specific Models (1) BERT (Bidirectional Encoder Representations from Transformers): (a) Technical Specifications: Developed by Devlin et al., BERT is a transformer-based model designed to pre-train deep bidirectional representations by joint conditioning on both left and right

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contexts in all layers [16]. This bidirectional approach allows BERT to capture nuanced relationships between words, improving performance on various natural language processing (NLP) tasks. BERT’s architecture consists of multiple transformer layers, typically 12 in the base version and 24 in the large version, with hidden sizes of 768 and 1024, respectively. During pre- training, the model uses the Masked Language Model (MLM) and Next Sentence Prediction (NSP) objectives. (b) Key Features and Innovations: BERT introduced several key innovations: • Bidirectional Contextual Understanding: BERT reads the text in both directions simultaneously, enabling a deeper understanding of word contexts. • Masked Language Modeling: The model randomly masks tokens in the input and predicts them, allowing it to learn bidirectional representations. • Next Sentence Prediction: This objective helps BERT understand the relationship between sentences, which is crucial for answering questions and natural language inference. (c) Industry Applications: BERT’s versatility has led to widespread adoption: • Healthcare: Used to extract insights from clinical notes and electronic health records (EHRs), aiding in patient outcome predictions [34]. • Customer Service: Powers chatbots and virtual assistants that provide context-aware responses [35]. • Finance: Assists in analyzing financial texts like earnings reports and market news [22]. (2) GPT-4: (a) Technical Specifications: GPT-4, developed by OpenAI, is an advanced transformer-based model that extends the capabilities of its predecessors by integrating extensive datasets from various modalities, including text and images [36]. GPT-4’s architecture allows it to generate coherent and contextually relevant content across multiple data types. (b) Key Features and Innovations: Key innovations of GPT-4 include: • Multimodal Capabilities: Processes and generates text, images, and other data types, enhancing versatility. • Few-Shot Learning: Performs tasks with minimal task- specific training data. • Enhanced Scalability: Designed to handle complex tasks and large datasets efficiently.

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• Improved Contextual Understanding: Demonstrates superior coherence in generated outputs. (c) Industry Applications: GPT-4’s advanced capabilities have facilitated its deployment in: • Healthcare: Generates medical reports and summarizes clinical trials [19]. • Entertainment: Assists in content creation, such as scripts and music composition [5]. • Education: Provides personalized tutoring and generates educational content [12]. • E-commerce: Enhances product recommendations and automates customer service interactions [21]. (3) CLIP (Contrastive Language–Image Pre-Training): (a) Technical Specifications: CLIP, developed by Radford et al., learns visual concepts from natural language supervision by leveraging a large dataset of images and their textual descriptions [4]. It uses a contrastive learning approach to align image and text representations. (b) Key Features and Innovations: Innovations of CLIP include: • Contrastive Learning: Matches images with corresponding text descriptions. • Zero-Shot Capabilities: Performs tasks without task-specific training. • Versatile Representations: Applicable to various tasks like image captioning and visual question answering. (c) Industry Applications: CLIP is utilized in: • Content Moderation: Automatically detects inappropriate content on platforms. • E-commerce: Improves product search and recommendations [21]. • Healthcare: Assists in medical image analysis. (4) DALL-E: (a) Technical Specifications: DALL-E, also from OpenAI, generates highquality images from textual descriptions using a transformer-based architecture [5]. (b) Key Features and Innovations: DALL-E’s innovations include: • Text-to-Image Generation: Creates detailed images from text prompts. • Diverse Image Generation: Produces images of various styles and objects. • Fine-Grained Control: Allows users to specify detailed attributes in prompts.

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(c) Industry Applications: Applications of DALL-E encompass: • Marketing and Advertising: Generates visual content for campaigns. • Entertainment and Media: Assists in concept art and storyboarding. • E-commerce: Enhances product visualization [21]. (5) Perceiver: (a) Technical Specifications: Perceiver, developed by Jaegle et al., handles inputs from multiple modalities through a flexible architecture that processes and integrates various data types efficiently [7]. (b) Key Features and Innovations: Innovations include: • Latent Array: Uses a latent array interacting with input data via cross-attention. • Iterative Refinement: Refines understanding of data iteratively. • Scalability: Efficiently handles large datasets and complex inputs. (c) Industry Applications: Perceiver is applied in: • Autonomous Systems: Enhances perception in self-driving cars [23]. • Smart Cities: Processes data for urban planning. • Healthcare: Aids in diagnostics by combining diverse medical data. B. Comparison of Models See Table 7. C. Industry Applications Across Domains The versatility of multimodal LLMs enables applications across various industries: Table 7 Comparison of model architectures and capabilities Model

Architecture

Capabilities

Applications

BERT

Transformer-based, bidirectional

NLPtasks, contextual understanding

Text classification, sentiment analysis [16]

GPT-4

Transformer-based, multimodal integration

Text and multi-modal generation

Content creation, tutoring [36]

CLIP

Transformer + CNN, contrastive learning

Image-text alignment, zero- shot learning

Imagesearch, content moderation [4]

DALLE

Transformer-based, text-to-image generation

Imagesynthesis from text

Marketing, conceptart [5]

Perceiver

Flexible, iterative attention Multimodal input processing

Autonomous systems, smart cities [7]

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(1) Healthcare: Multimodal LLMs integrate diverse medical data to improve diagnostics and personalized treatments [19, 32]. (2) Entertainment: These models enhance content creation and personalize user experiences [5, 20]. (3) E-commerce: They improve product recommendations and customer service by analyzing product descriptions, images, and user reviews [21]. (4) Finance: In finance, models like FinBERT integrate textual news and market data to provide insights for trading strategies [22]. (5) Education: Educational platforms use multimodal LLMs to develop personalized learning experiences [12]. (6) Autonomous Systems: They enhance perception and decision-making by integrating visual, auditory, and sensor data [23]. Exploring model specialization and industry applications highlights the transformative potential of multimodal LLMs across various sectors. By understanding the unique architectures and capabilities of models like BERT, GPT-4, CLIP, DALL-E, and Perceiver, we can optimize their deployment to address complex challenges and drive innovation in artificial intelligence.

6 Challenges and Future Directions Multimodal Large Language Models (LLMs) have revolutionized artificial intelligence by enabling complex and versatile applications across various domains. However, to fully realize their potential, several challenges must be addressed. This section discusses key challenges facing multimodal LLMs, including data scarcity and quality, computational requirements, interpretability and explainability, bias and fairness, and generalization across modalities. We also propose future research directions to overcome these limitations, enhancing the performance, scalability, and ethical deployment of multi- modal LLMs [4, 15, 16]. A. Data Scarcity and Quality Multimodal LLMs’ effectiveness relies heavily on the availability of large, highquality datasets. Quality data enables models to learn accurate and contextually rich representations, while extensive datasets expose models to diverse scenarios, improving their generalization capabilities [4, 5]. (1) Importance of Data Quality and Quantity: High-quality and diverse data are essential for training effective multimodal LLMs. Reliable data reduces the likelihood of errors and biases in model outputs, and diverse datasets ensure that models can generalize well to unseen data, which is crucial for real-world applications [15, 16]. (2) Current Limitations: Despite advancements, limitations persist:

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• Data Scarcity: In specialized domains like healthcare and finance, there is a scarcity of high-quality, labeled data, hindering model learning [32]. • Data Quality: Noisy, biased, or incomplete datasets can degrade model performance. Ensuring data quality requires rigorous preprocessing, which can be resource-intensive [37]. • Data Diversity: Lack of diversity can lead to biased models that do not generalize across different populations or scenarios, impacting fairness and equity [24]. (3) Future Directions: To address these limitations: • Synthetic Data Generation: Use generative models to create synthetic data, augmenting existing datasets and enhancing model robustness [38]. • Improved Data Collection: Develop innovative techniques, such as crowdsourcing and leveraging IoT devices, to increase data availability and diversity. Partner- ships with industry and academia can provide access to specialized datasets [19]. • Automated Data Cleaning: Implement automated tools for data cleaning and preprocessing to improve data quality efficiently [37]. • Bias Mitigation in Data: Integrate techniques to detect and mitigate biases in datasets, enhancing fairness and performance across diverse populations [10]. B. Computational Requirements Training and deploying multimodal LLMs require substantial computational resources, posing challenges for scalability and accessibility. (1) Challenges in Training and Deployment: • High Dimensionality: Multimodal data includes high- resolution images and long text sequences, requiring significant computational power [5, 25]. • Large Model Sizes: Models like GPT-4 and CLIP have billions of parameters, demanding extensive memory and computational capacity [4]. • Extended Training Times: Training can take weeks on powerful hardware, consuming substantial energy [29]. (2) Advancements and Limitations: Advancements include: • Model Optimization: Techniques like pruning, quantization, and knowledge distillation reduce the model size and improve efficiency [39]. • Specialized Hardware: TPUs and GPUs accelerate training and deployment but are often limited to well-funded organizations [40]. • Distributed Training: Leveraging multiple nodes and GPUs reduces training time but requires complex orchestration and can be costly [41]. (3) Future Research Directions: To optimize computational efficiency: • Efficient Architectures: Develop models that maintain performance with reduced computational demands, such as using sparse attention mechanisms.

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• Advanced Hardware: Innovate AI-specific hardware like neuromorphic chips for more efficient computation. • Sustainable AI Practices: Promote energy-efficient training methods to reduce environmental impact [42]. • Federated Learning and Edge Computing: Distribute computational load and enhance privacy by processing data locally [27]. C. Interpretability and Explainability As multimodal LLMs become more complex, interpretability and explainability are crucial for building trust and ensuring ethical deployment. (1) Need for Interpretability: • Trust and Adoption: Users are more likely to trust AI systems that can explain their decisions, especially in critical domains like healthcare and finance [26]. • Regulatory Compliance: Regulations may require explainable AI to ensure transparency and accountability. • Debugging and Improvement: Interpretability aids in identifying biases and errors, facilitating model improvement [28]. (2) Current Methods and Limitations: Methods include: • Attention Mechanisms: Highlight input data the model focuses on but may not align with human interpretations [43]. • Feature Importance: Techniques like SHAP and LIME provide feature contribution scores but can be computationally expensive [44]. • Saliency Maps: Visual representations of influential input areas may not capture complex feature interactions [45]. • Counterfactual Explanations: Show how to input changes affect predictions, but generating meaningful counterfactuals can be challenging [46]. (3) Future Directions: To enhance interpretability: • Advanced Interpretability Tools: Develop scalable and efficient tools capable of handling multimodal data [11]. • Integrate Interpretability in Design: Design models with inherent interpretability features, such as modular architectures. • Human-Centric Explanations: Create explanations aligned with human reasoning, understandable by non-experts [47]. • Ethical AI Practices: Focus on fairness and bias mitigation to build trust [48]. D. Bias and Fairness Bias in multimodal LLMs can lead to unfair and discriminatory outcomes, necessitating strategies to ensure fairness. (1) Sources and Impact of Bias: Bias can stem from:

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• Training Data: Unbalanced or biased data can perpetuate societal biases [28]. • Model Architecture: Certain architectures may amplify biases or introduce new ones through modality interactions [27]. • Deployment Context: Real-world use can introduce bias if models rely on biased data or are applied inappropriately [49]. (2) Current Mitigation Strategies: Strategies include: • Bias Detection: Use fairness metrics and algorithms to identify biases [10]. • Data Augmentation: Ensure diverse training data through oversampling and synthetic data generation [50]. • Fairness-Aware Algorithms: Incorporate fairness constraints during training, such as adversarial debiasing [51]. • Regular Audits: Conduct audits to identify and rectify biases in deployed models [52]. (3) Future Research Directions: To create fairer models: • Diversify Training Data: Increase data from underrepresented groups and modalities. • Advanced Bias Detection: Develop algorithms to identify subtle and complex biases continuously [53]. • Enhance Transparency: Improve model explainability to understand decision processes and potential biases [11]. • Establish Fairness Benchmarks: Create standardized benchmarks for evaluating fairness in multimodal LLMs [48]. E. Generalization Across Modalities Achieving effective cross-modal generalization remains a significant challenge. (1) Challenges: • Heterogeneous Data Representations: Different modalities have unique characteristics, making integration complex. • Alignment and Fusion: Precise techniques are needed to align and fuse multimodal data effectively [8]. • Scale and Complexity: Handling large-scale datasets from multiple modalities increases computational complexity [7]. (2) Current State and Limitations: While models like CLIP and Perceiver have made progress, limitations include: • Computational Resources: Advanced techniques require extensive resources [4, 7]. • Loss of Modality-Specific Features: Joint representations may not preserve unique features of each modality [54].

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• Fine-Tuning Challenges: Transfer learning requires careful fine-tuning for optimal performance across modalities. (3) Future Directions: To improve cross-modal generalization: • Robust Training Techniques: Develop methods that leverage strengths of each modality while mitigating weaknesses [55]. • Enhanced Transfer Learning: Design flexible models that adapt pre-trained knowledge to new modalities with minimal fine-tuning [56]. • Scalable Architectures: Build models that efficiently integrate multiple modalities without excessive computational overhead. • Standardized Evaluation Metrics: Develop metrics to assess cross-modal generalization effectiveness [57]. Addressing the challenges identified is crucial for unlocking the full potential of multimodal LLMs. Overcoming data scarcity and quality issues, optimizing computational requirements, improving interpretability and explainability, mitigating bias, and enhancing cross-modal generalization will enable these models to achieve greater accuracy, efficiency, and fairness in their applications. Future research should focus on developing robust training techniques, scalable architectures, and effective evaluation metrics. The AI community can create more powerful and versatile models that drive innovation across various domains by addressing these challenges.

7 Conclusion Multimodal Large Language Models (LLMs) represent a significant advancement in artificial intelligence, enabling the integration and processing of diverse data types such as text, images, audio, and more. This paper has explored the evolution, current trends, technical foundations, model specializations, industry applications, and challenges associated with multimodal LLMs, highlighting their transformative potential across various domains. A. Summary of Key Findings We began by tracing the evolution of multimodal LLMs from early unimodal models to sophisticated architectures capable of handling multiple modalities. The introduction of the Transformer architecture by Vaswani et al. [15] marked a pivotal moment, leading to models like BERT [16], GPT- 4, CLIP [4], DALL-E [5], and Perceiver [7]. These models leverage advanced data representation, fusion, and training techniques to achieve state-of-the-art performance across various tasks. Current trends in multimodal LLMs include the integration of more extensive and more diverse datasets, real-time processing capabilities, and enhanced model interpretability. These advancements have expanded the applications of multimodal LLMs across industries such as healthcare [19], entertainment [5], e-commerce [21],

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finance [22], education [12], and autonomous systems [23]. In these domains, multimodal LLMs have demonstrated their ability to improve diagnostics, personalize user experiences, enhance decision-making processes, and drive innovation. B. Addressing Key Challenges Despite significant progress, several challenges must be addressed to realize the potential of multimodal LLMs fully. Data scarcity and quality issues remain critical, as high-quality, diverse datasets are essential for training robust and unbiased models [3]. Computational requirements pose another challenge, with large models demanding substantial resources and energy consumption [29]. Improving interpretability and explainability is crucial for building trust and ensuring ethical deployment, especially in sensitive applications [26]. Bias and fairness concerns must be tackled to prevent unfair or discriminatory outcomes (28). Finally, achieving effective generalization across modalities requires advanced data alignment and Fusion techniques. C. Future Research Directions To overcome these challenges and advance the field, we suggest several future research directions: • Enhancing Data Quality and Diversity: Developing innovative data collection methods, leveraging synthetic data generation, and implementing automated data cleaning processes can mitigate data scarcity and improve dataset quality [19]. • Optimizing Computational Efficiency: Research into efficient model architectures and advancements in AI-specific hardware can reduce computational demands and promote sustainability [25, 33]. • Improving Interpretability and Explainability: Integrating interpretability into model design and developing advanced, user-friendly tools can enhance transparency and user trust [11]. • Addressing Bias and Ensuring Fairness: Developing sophisticated bias detection algorithms and establishing standardized fairness benchmarks are essential for creating equitable models [10, 27, 48, 53]. • Advancing Cross-Modal Generalization: Enhancing cross-modal training techniques, transfer learning methods, and scalable architectures will improve the ability of multimodal LLMs to handle diverse data types effectively [3]. D. Concluding Remarks Multimodal LLMs hold immense promise for driving innovation and delivering substantial benefits across various domains. The AI community can develop more powerful, efficient, and fair models by addressing the outlined challenges and focusing on the proposed research directions. These advancements will not only enhance the capabilities of AI systems but also contribute to their responsible and ethical deployment, ultimately leading to a positive impact on society.

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