Fact-based Counter Narrative Generation to Combat Hate Speech
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3696410.3714718.pdf
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3.85 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Wilk, Brian
Shomee, Homaira Huda
Maity, Suman Kalyan
Medya, Sourav
Date Issued
April 22, 2025
Publisher
ACM|Proceedings of the ACM Web Conference 2025
Citation
Brian Wilk, Homaira Huda Shomee, Suman Kalyan Maity, and Sourav Medya. 2025. Fact-based Counter Narrative Generation to Combat Hate Speech. In Proceedings of the ACM on Web Conference 2025 (WWW '25). Association for Computing Machinery, New York, NY, USA, 3354–3365.
Version
Final published version
Abstract
Online hatred has become an increasingly pervasive issue, affecting individuals and communities across various digital platforms. To combat hate speech in such platforms, counter narratives (CNs) are regarded as an effective method. In recent years, there has been growing interest in using generative AI tools to construct CNs. However, most of the generative models produce generic responses to hate speech and can hallucinate, reducing their effectiveness. To address the above limitations, we propose a counter narrative generation method that enhances CNs by providing non-aggressive, fact-based narratives with relevant background knowledge from two distinct sources, including a web search module. Furthermore, we conduct a comprehensive evaluation using multiple metrics, including LLM-based measures for persuasion, factuality, and informativeness, along with human and traditional NLP evaluations. Our method significantly outperforms baselines, achieving an average factuality score of 0.915, compared to 0.741, 0.701, and 0.69 for competitive baselines, and performs well in human evaluations.
Description
WWW ’25, April 28-May 2, 2025, Sydney, NSW, Australia
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3696410.3714718
