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        <title>Leveraging World Knowledge in Implicit Hate Speech Detection</title>
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        <namePart type="given">Jessica</namePart>
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            <title>Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)</title>
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        <name type="personal">
            <namePart type="given">Laura</namePart>
            <namePart type="family">Biester</namePart>
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        <name type="personal">
            <namePart type="given">Lu</namePart>
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    <abstract>While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.</abstract>
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    <identifier type="doi">10.18653/v1/2022.nlp4pi-1.4</identifier>
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%0 Conference Proceedings
%T Leveraging World Knowledge in Implicit Hate Speech Detection
%A Lin, Jessica
%Y Biester, Laura
%Y Demszky, Dorottya
%Y Jin, Zhijing
%Y Sachan, Mrinmaya
%Y Tetreault, Joel
%Y Wilson, Steven
%Y Xiao, Lu
%Y Zhao, Jieyu
%S Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F lin-2022-leveraging
%X While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.
%R 10.18653/v1/2022.nlp4pi-1.4
%U https://aclanthology.org/2022.nlp4pi-1.4
%U https://doi.org/10.18653/v1/2022.nlp4pi-1.4
%P 31-39

Markdown (Informal)

[Leveraging World Knowledge in Implicit Hate Speech Detection](https://aclanthology.org/2022.nlp4pi-1.4) (Lin, NLP4PI 2022)

• Leveraging World Knowledge in Implicit Hate Speech Detection (Lin, NLP4PI 2022)

ACL

• Jessica Lin. 2022. Leveraging World Knowledge in Implicit Hate Speech Detection. In Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI), pages 31–39, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.