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Computer Science > Computation and Language

arXiv:2507.04350 (cs)
[Submitted on 6 Jul 2025]

Title:HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation

Authors:Naquee Rizwan, Seid Muhie Yimam, Daryna Dementieva, Florian Skupin, Tim Fischer, Daniil Moskovskiy, Aarushi Ajay Borkar, Robert Geislinger, Punyajoy Saha, Sarthak Roy, Martin Semmann, Alexander Panchenko, Chris Biemann, Animesh Mukherjee
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Abstract: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.
Comments: arXiv admin note: substantial text overlap with arXiv:2406.19543
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2507.04350 [cs.CL]
  (or arXiv:2507.04350v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2507.04350
arXiv-issued DOI via DataCite

Submission history

From: Naquee Rizwan [view email]
[v1] Sun, 6 Jul 2025 11:25:23 UTC (1,017 KB)
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