A tri-partite survey finds that hate speech definitions and moderation practices in country laws, platform policies, and NLP datasets are largely misaligned, and calls for a unified proactive moderation framework.
Counterspeakers' Perspectives: Unveiling Barriers and AI Needs in the Fight against Online Hate
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abstract
Counterspeech, i.e., direct responses against hate speech, has become an important tool to address the increasing amount of hate online while avoiding censorship. Although AI has been proposed to help scale up counterspeech efforts, this raises questions of how exactly AI could assist in this process, since counterspeech is a deeply empathetic and agentic process for those involved. In this work, we aim to answer this question, by conducting in-depth interviews with 10 extensively experienced counterspeakers and a large scale public survey with 342 everyday social media users. In participant responses, we identified four main types of barriers and AI needs related to resources, training, impact, and personal harms. However, our results also revealed overarching concerns of authenticity, agency, and functionality in using AI tools for counterspeech. To conclude, we discuss considerations for designing AI assistants that lower counterspeaking barriers without jeopardizing its meaning and purpose.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation
A tri-partite survey finds that hate speech definitions and moderation practices in country laws, platform policies, and NLP datasets are largely misaligned, and calls for a unified proactive moderation framework.