31

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

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
Main:9 Pages
8 Figures
Bibliography:5 Pages
1 Tables
Appendix:1 Pages
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.

View on arXiv
Comments on this paper