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Understanding Counterspeech for Online Harm Mitigation
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Counterspeech offers direct rebuttals to hateful speech by challenging perpetrators of hate and showing support to targets of abuse. It provides a promising alternative to more contentious measures, such as content moderation and deplatforming, by contributing a greater amount of positive online speech rather than attempting to mitigate harmful content through removal. Advances in the development of large language models mean that the process of producing counterspeech could be made more efficient by automating its generation, which would enable large-scale online campaigns. However, we currently lack a systematic understanding of several important factors relating to the efficacy of counterspeech for hate mitigation, such as which types of counterspeech are most effective, what are the optimal conditions for implementation, and which specific effects of hate it can best ameliorate. This paper aims to fill this gap by systematically reviewing counterspeech research in the social sciences and comparing methodologies and findings with computer science efforts in automatic counterspeech generation. By taking this multi-disciplinary view, we identify promising future directions in both fields.
Forward citations
Cited by 4 Pith papers
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Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate
Persona- and emotion-guided prompts make LLM counter-narratives more empathetic and readable, but generated responses remain verbose, college-level, and sometimes classified as hateful.
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Echoes of Discord: Forecasting Hater Reactions to Counterspeech
A three-way classifier trained on Reddit hate speech/counterspeech pairs predicts hater reentry and reentry type more accurately than a two-stage predictor, with linguistic features of counterspeech signaling differen...
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Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation
Contextualized counterspeech from LLaMA2-13B using conversation and user history is rated more adequate and persuasive than generic counterspeech, but algorithmic metrics rank configurations inconsistently with humans.
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PANDA -- Paired Anti-hate Narratives Dataset from Asia: Using an LLM-as-a-Judge to Create the First Chinese Counterspeech Dataset
The first Chinese-language counterspeech dataset of paired hate speech and counterspeech instances, created via an LLM-as-a-Judge pipeline with only partial human verification.
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