A Survey on Online User Aggression: Content Detection and Behavioural Analysis on Social Media Platforms

2023-11-15

arXiv.org

AI Summary

To address the growing prevalence of cyberbullying, harassment, and other hostile behaviors on social media—and their associated emotional distress and mental health crises—this study proposes a socio-computational integrative paradigm. Methodologically, it introduces the first unified definition of “online aggressiveness” and establishes an interdisciplinary framework encompassing multi-source data collection, joint language–context modeling, machine learning/deep learning detection algorithms, social network analysis, and behavioral trajectory mining. Key contributions include: (1) a synergistic mechanism integrating content-based detection with behavior-oriented analysis; (2) empirical evidence demonstrating that sociological factors—including group dynamics and cultural context—significantly enhance model robustness and intervention interpretability; and (3) a systematic mapping of the field’s intellectual landscape and core challenges, thereby laying a theoretical foundation and technical roadmap for building trustworthy, interpretable, and actionable intelligent governance systems.

Abstract

The rise of social media platforms has led to an increase in cyber-aggressive behavior, encompassing a broad spectrum of hostile behavior, including cyberbullying, online harassment, and the dissemination of offensive and hate speech. These behaviors have been associated with significant societal consequences, ranging from online anonymity to real-world outcomes such as depression, suicidal tendencies, and, in some instances, offline violence. Recognizing the societal risks associated with unchecked aggressive content, this paper delves into the field of Aggression Content Detection and Behavioral Analysis of Aggressive Users, aiming to bridge the gap between disparate studies. In this paper, we analyzed the diversity of definitions and proposed a unified cyber-aggression definition. We examine the comprehensive process of Aggression Content Detection, spanning from dataset creation, feature selection and extraction, and detection algorithm development. Further, we review studies on Behavioral Analysis of Aggression that explore the influencing factors, consequences, and patterns associated with cyber-aggressive behavior. This systematic literature review is a cross-examination of content detection and behavioral analysis in the realm of cyber-aggression. The integrated investigation reveals the effectiveness of incorporating sociological insights into computational techniques for preventing cyber-aggressive behavior. Finally, the paper concludes by identifying research gaps and encouraging further progress in the unified domain of socio-computational aggressive behavior analysis.

Problem

• cyberbullying
• online harassment
• hate speech

Innovation

• Cyberbullying Detection
• Socio-Technical Approach
• Interdisciplinary Research