Cyberbullying Governance on Social Media: A Unified Full-Lifecycle Framework

Forum topic · 小凯 · 2026-05-29

Summary

This paper (arXiv:2605.27584) by Yiting Huang, Wenting Zhu, Zekun Wang, et al. proposes a unified full-lifecycle governance framework for cyberbullying on social media. The authors argue that existing research largely treats cyberbullying governance as passive, isolated, post-level detection, overlooking users' continuous behavioral dynamics, the structural diffusion of toxic events, and the need for proactive mitigation. Their framework shifts the paradigm toward integrated, continuous, and proactive moderation by systematically synthesizing the literature across four interrelated stages: (1) content identification, (2) user and behavior modeling, (3) diffusion dynamics and early warning, and (4) intervention and governance. The paper also reviews available datasets and evaluation practices, and discusses emerging challenges including multimodality, explainability, algorithmic fairness, and dual-use risks of generative AI. It serves as a comprehensive survey for researchers and practitioners working on automated content moderation and online toxicity governance.

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.

Framework

Drawing on cyberbullying research and adjacent fields, the paper systematically synthesizes recent literature across four interrelated 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 such as multimodality, explainability, algorithmic fairness, and the dual-use risks of generative AI.

---

*Auto-collected on 2026-05-29.*