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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2510.17289 |
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| _version_ | 1866909857611776000 |
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| author | Bakarou, Hajar Messoussi, Mohamed Sinane El Ollagnier, Anaïs |
| author_facet | Bakarou, Hajar Messoussi, Mohamed Sinane El Ollagnier, Anaïs |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17289 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning Bakarou, Hajar Messoussi, Mohamed Sinane El Ollagnier, Anaïs Computation and Language 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. |
| title | Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.17289 |