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        <title>A Survey on Combating Hate Speech through Detection and Prevention in English</title>
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        <namePart type="given">Prashant</namePart>
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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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            <namePart type="given">Karunesh</namePart>
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    <abstract>The rapid rise of social networks has brought with it an increase in hate speech, which poses a significant challenge to society, researchers, companies, and policymakers. Hate speech can take the form of text or multimodal content, such as memes, GIFs, audio, or videos, and the scientific study of hate speech from a computer science perspective has gained attention in recent years. The detection and combating of hate speech is mostly considered a supervised task, with annotated corpora and shared resources playing a crucial role. Social networks are using modern AI tools to combat hate speech, and this survey comprehensively examines the work done to combat hate in the English language. It delves into state-of-the-art methodologies for unimodal and multimodal hate identification, the role of explainable AI, prevention of hate speech through style transfer, and counternarrative generation, while also discussing the efficacy and limitations of these methods. Compared with earlier surveys, this paper offers a well-organized presentation of methods to combat hate.</abstract>
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%0 Conference Proceedings
%T A Survey on Combating Hate Speech through Detection and Prevention in English
%A Kapil, Prashant
%A Ekbal, Asif
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F kapil-ekbal-2024-survey
%X The rapid rise of social networks has brought with it an increase in hate speech, which poses a significant challenge to society, researchers, companies, and policymakers. Hate speech can take the form of text or multimodal content, such as memes, GIFs, audio, or videos, and the scientific study of hate speech from a computer science perspective has gained attention in recent years. The detection and combating of hate speech is mostly considered a supervised task, with annotated corpora and shared resources playing a crucial role. Social networks are using modern AI tools to combat hate speech, and this survey comprehensively examines the work done to combat hate in the English language. It delves into state-of-the-art methodologies for unimodal and multimodal hate identification, the role of explainable AI, prevention of hate speech through style transfer, and counternarrative generation, while also discussing the efficacy and limitations of these methods. Compared with earlier surveys, this paper offers a well-organized presentation of methods to combat hate.
%U https://aclanthology.org/2024.icon-1.57/
%P 485-501

Markdown (Informal)

[A Survey on Combating Hate Speech through Detection and Prevention in English](https://aclanthology.org/2024.icon-1.57/) (Kapil & Ekbal, ICON 2024)

• A Survey on Combating Hate Speech through Detection and Prevention in English (Kapil & Ekbal, ICON 2024)

ACL

• Prashant Kapil and Asif Ekbal. 2024. A Survey on Combating Hate Speech through Detection and Prevention in English. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 485–501, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).