CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges
arXiv:2606.20369v1 Announce Type: new Abstract: Online hate speech and misinformation frequently overlap, yet NLP research has mainly treated them in isolation. While LLMs represent a scalable solution for assisting humans in the generation of counterspeech for both threats, zero-shot models frequently generate repetitive and vague responses, underscoring the need for high-quality examples to steer model generation. However, existing counterspeech datasets against the overlap of hate and misinformation are scarce and limited to single-turn English dialogues, while real-life interactions span a
The proliferation of hate speech and misinformation online, coupled with the rapid advancement and adoption of LLMs, highlights an urgent need for effective counterspeech generation methods.
This dataset addresses a critical gap in NLP research by providing high-quality, multi-turn, contextually annotated data for counterspeech against the overlap of hate and misinformation, which is essential for improving autonomous moderation and societal well-being.
The availability of this specialized dataset will enable more sophisticated and nuanced LLM training for combatting online harms, moving beyond single-turn and isolated issue approaches.
- · AI developers focused on content moderation
- · Social media platforms
- · Researchers in NLP and AI ethics
- · Organizations combating online hate and misinformation
- · Perpetrators of online hate and misinformation
- · LLMs without targeted fine-tuning data
- · Current zero-shot counterspeech models
Improved performance of LLMs in generating effective and contextually appropriate counterspeech against hate and misinformation.
Reduced prevalence and impact of hate speech and misinformation on social media platforms due to more effective automated and human-assisted moderation.
Potential for a more civil and informed online discourse, fostering healthier public deliberation and reducing societal polarization.
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