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

TL;DR

HatePRISM offers a comprehensive analysis of hate speech regulation policies, platform practices, and NLP research, highlighting inconsistencies and proposing a unified framework for proactive hate speech mitigation.

Contribution

It provides an integrated examination of hate speech policies, platform moderation, and research datasets, and suggests directions for developing a unified automated moderation framework.

Findings

Significant inconsistencies in hate speech definitions across jurisdictions.

Lack of alignment between platform policies and NLP research datasets.

Need for a unified framework for automated hate speech moderation.

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.