用多模态方法识别多人对话中的网络欺凌行为，提升平台安全。
Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning
- 融合文本与交互图谱，捕捉对话中的攻击性线索
- 多模态模型在辱骂检测上达到0.718的准确率
- 适合研究网络暴力、社交平台安全的学者与工程师
社交媒体上的反社会行为（如仇恨言论、骚扰和网络欺凌）对平台安全与社会福祉构成日益增长的威胁。以往研究主要集中在X和Reddit等平台，而多人对话场景因数据有限仍被忽视。为此，我们采用法语开源数据集CyberAgressionAdo-Large，模拟多人对话中的反社会行为，并评估三项任务：滥用检测、欺凌行为分析与欺凌群体识别。我们对比了六种基于文本和八种基于图的表征学习方法，分析词汇线索、互动动态及其多模态融合效果。结果表明，多模态模型优于单模态基线。晚期融合模型mBERT + WD-SGCN表现最佳，在滥用检测上达到0.718，群体识别为0.286，欺凌分析为0.606。错误分析显示其能有效处理隐含攻击、角色转换和情境依赖的敌意现象。
原文摘要 · Abstract (English)
Antisocial behavior (ASB) on social media -- including hate speech, harassment, and cyberbullying -- poses growing risks to platform safety and societal well-being. Prior research has focused largely on networks such as X and Reddit, while \textit{multi-party conversational settings} remain underexplored due to limited data. To address this gap, we use \textit{CyberAgressionAdo-Large}, a French open-access dataset simulating ASB in multi-party conversations, and evaluate three tasks: \textit{abuse detection}, \textit{bullying behavior analysis}, and \textit{bullying peer-group identification}. We benchmark six text-based and eight graph-based \textit{representation-learning methods}, analyzing lexical cues, interactional dynamics, and their multimodal fusion. Results show that multimodal models outperform unimodal baselines. The late fusion model \texttt{mBERT + WD-SGCN} achieves the best overall results, with top performance on abuse detection (0.718) and competitive scores on peer-group identification (0.286) and bullying analysis (0.606). Error analysis highlights its effectiveness in handling nuanced ASB phenomena such as implicit aggression, role transitions, and context-dependent hostility.
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