HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911041671135232 |
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| author | Rizwan, Naquee Yimam, Seid Muhie Dementieva, Daryna Skupin, Florian Fischer, Tim Moskovskiy, Daniil Borkar, Aarushi Ajay Geislinger, Robert Saha, Punyajoy Roy, Sarthak Semmann, Martin Panchenko, Alexander Biemann, Chris Mukherjee, Animesh |
| author_facet | Rizwan, Naquee Yimam, Seid Muhie Dementieva, Daryna Skupin, Florian Fischer, Tim Moskovskiy, Daniil Borkar, Aarushi Ajay Geislinger, Robert Saha, Punyajoy Roy, Sarthak Semmann, Martin Panchenko, Alexander Biemann, Chris Mukherjee, Animesh |
| contents | Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hateful content persists as a significant challenge. Existing approaches primarily rely on reactive measures such as blocking or suspending offensive messages, with emerging strategies focusing on proactive measurements like detoxification and counterspeech. In our work, which we call HatePRISM, we conduct a comprehensive examination of hate speech regulations and strategies from three perspectives: country regulations, social platform policies, and NLP research datasets. Our findings reveal significant inconsistencies in hate speech definitions and moderation practices across jurisdictions and platforms, alongside a lack of alignment with research efforts. Based on these insights, we suggest ideas and research direction for further exploration of a unified framework for automated hate speech moderation incorporating diverse strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation Rizwan, Naquee Yimam, Seid Muhie Dementieva, Daryna Skupin, Florian Fischer, Tim Moskovskiy, Daniil Borkar, Aarushi Ajay Geislinger, Robert Saha, Punyajoy Roy, Sarthak Semmann, Martin Panchenko, Alexander Biemann, Chris Mukherjee, Animesh Computation and Language Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hateful content persists as a significant challenge. Existing approaches primarily rely on reactive measures such as blocking or suspending offensive messages, with emerging strategies focusing on proactive measurements like detoxification and counterspeech. In our work, which we call HatePRISM, we conduct a comprehensive examination of hate speech regulations and strategies from three perspectives: country regulations, social platform policies, and NLP research datasets. Our findings reveal significant inconsistencies in hate speech definitions and moderation practices across jurisdictions and platforms, alongside a lack of alignment with research efforts. Based on these insights, we suggest ideas and research direction for further exploration of a unified framework for automated hate speech moderation incorporating diverse strategies. |
| title | HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.04350 |