Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning

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 textitmulti-party conversational settings remain underexplored due to limited data. To address this gap, we use textitCyberAgressionAdo-Large, a French open-access dataset simulating ASB in multi-party conversations, and evaluate three tasks: textitabuse detection, textitbullying behavior analysis, and textitbullying peer-group identification. We benchmark six text-based and eight graph-based textitrepresentation-learning methods, analyzing lexical cues, interactional dynamics, and their multimodal fusion. Results show that multimodal models outperform unimodal baselines. The late fusion model textttmBERT + 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.