Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention

2026-05-26

AI Summary

This work addresses the limitations of existing cyberbullying mitigation approaches, which predominantly focus on static content detection while overlooking behavioral dynamics, propagation mechanisms, and proactive intervention. The paper proposes the first unified, full-lifecycle framework that integrates content identification, user behavior modeling, diffusion-aware risk forecasting, and intervention strategy design, thereby shifting the paradigm from reactive detection to active, systematic governance. By synergistically combining multimodal data processing, generative AI–based risk assessment, and diffusion dynamics analysis, the study systematically examines emerging challenges such as model interpretability and algorithmic fairness, reviews current methodologies and benchmark datasets, establishes standardized evaluation criteria, and outlines a clear research roadmap toward building safe, resilient digital ecosystems.

Abstract

The proliferation of social media platforms and online communities has inadvertently catalyzed the spread of cyberbullying, hate speech, and other forms of online toxicity, making the effective governance of such harm a critical societal and computational challenge. While significant strides have been made in automating content moderation, existing research predominantly treats cyberbullying governance as passive, isolated detection at the post level. This reductionist view overlooks the continuous behavioral dynamics of users, the structural diffusion of toxic events, and the critical need for proactive mitigation. To bridge these gaps, this paper proposes a unified full-lifecycle governance framework that shifts the paradigm of cyberbullying governance from isolated static detection toward integrated, continuous, and proactive moderation. Drawing on cyberbullying research and adjacent fields, we systematically synthesize the state-of-the-art literature across four interconnected stages: (1) Content Identification, (2) User and Behavior Modeling, (3) Diffusion Dynamics and Early Warning, and (4) Intervention and Governance. Furthermore, we review available datasets and evaluation practices, and discuss emerging challenges including multimodality, explainability, algorithmic fairness, and the dual-use risks of generative AI, providing a roadmap for future research toward a safer and more resilient digital ecosystem.

Problem

• cyberbullying governance
• content identification
• behavioral dynamics
• diffusion dynamics
• proactive intervention

Innovation

• unified governance framework
• full-lifecycle moderation
• proactive intervention
• diffusion dynamics
• behavioral modeling