Paper Overview
- Field: AI
- Authors: Yiting Huang, Wenting Zhu, Zekun Wang, et al.
- Published: 2026-05-28
- arXiv: 2605.27584
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, it systematically synthesizes recent literature across four interconnected stages:
1. Content identification 2. User and behavior modeling 3. Diffusion dynamics and early warning 4. Intervention and governance
The paper further reviews available datasets and evaluation practices, and discusses emerging challenges including multimodality, explainability, algorithmic fairness, and the dual-use risks of generative AI.
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*Auto-collected on 2026-05-29.*