{"ID":2852638,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17289","arxiv_id":"2510.17289","title":"Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.17289","url_pdf":"https://arxiv.org/pdf/2510.17289v1","authors":"[\"Hajar Bakarou\",\"Mohamed Sinane El Messoussi\",\"Anaïs Ollagnier\"]","published":"2025-10-20T08:27:38Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}