Improving Implicit Hate Speech Detection via a Community-Driven Multi-Agent Framework
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866917224557576192 |
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| author | Gajewska, Ewelina Budzynska, Katarzyna Chudziak, Jarosław A |
| author_facet | Gajewska, Ewelina Budzynska, Katarzyna Chudziak, Jarosław A |
| contents | This work proposes a contextualised detection framework for implicitly hateful speech, implemented as a multi-agent system comprising a central Moderator Agent and dynamically constructed Community Agents representing specific demographic groups. Our approach explicitly integrates socio-cultural context from publicly available knowledge sources, enabling identity-aware moderation that surpasses state-of-the-art prompting methods (zero-shot prompting, few-shot prompting, chain-of-thought prompting) and alternative approaches on a challenging ToxiGen dataset. We enhance the technical rigour of performance evaluation by incorporating balanced accuracy as a central metric of classification fairness that accounts for the trade-off between true positive and true negative rates. We demonstrate that our community-driven consultative framework significantly improves both classification accuracy and fairness across all target groups. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09342 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving Implicit Hate Speech Detection via a Community-Driven Multi-Agent Framework Gajewska, Ewelina Budzynska, Katarzyna Chudziak, Jarosław A Computation and Language Artificial Intelligence This work proposes a contextualised detection framework for implicitly hateful speech, implemented as a multi-agent system comprising a central Moderator Agent and dynamically constructed Community Agents representing specific demographic groups. Our approach explicitly integrates socio-cultural context from publicly available knowledge sources, enabling identity-aware moderation that surpasses state-of-the-art prompting methods (zero-shot prompting, few-shot prompting, chain-of-thought prompting) and alternative approaches on a challenging ToxiGen dataset. We enhance the technical rigour of performance evaluation by incorporating balanced accuracy as a central metric of classification fairness that accounts for the trade-off between true positive and true negative rates. We demonstrate that our community-driven consultative framework significantly improves both classification accuracy and fairness across all target groups. |
| title | Improving Implicit Hate Speech Detection via a Community-Driven Multi-Agent Framework |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.09342 |