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        <title>How Hate Speech Varies by Target Identity: A Computational Analysis</title>
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        <namePart type="given">Michael</namePart>
        <namePart type="family">Yoder</namePart>
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    <name type="personal">
        <namePart type="given">Lynnette</namePart>
        <namePart type="family">Ng</namePart>
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    <name type="personal">
        <namePart type="given">David</namePart>
        <namePart type="given">West</namePart>
        <namePart type="family">Brown</namePart>
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    <name type="personal">
        <namePart type="given">Kathleen</namePart>
        <namePart type="family">Carley</namePart>
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        <name type="personal">
            <namePart type="given">Vivek</namePart>
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    <abstract>This paper investigates how hate speech varies in systematic ways according to the identities it targets. Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities. This provides empirical evidence for differences in hate speech by target identity; we then investigate which patterns structure this variation. We find that the targeted demographic category (e.g. gender/sexuality or race/ethnicity) appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group. We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities. These experiments suggest the importance of considering targeted identity, as well as the social contexts associated with these identities, in automated hate speech classification</abstract>
    <identifier type="citekey">yoder-etal-2022-hate</identifier>
    <identifier type="doi">10.18653/v1/2022.conll-1.3</identifier>
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%0 Conference Proceedings
%T How Hate Speech Varies by Target Identity: A Computational Analysis
%A Yoder, Michael
%A Ng, Lynnette
%A Brown, David West
%A Carley, Kathleen
%Y Fokkens, Antske
%Y Srikumar, Vivek
%S Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F yoder-etal-2022-hate
%X This paper investigates how hate speech varies in systematic ways according to the identities it targets. Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities. This provides empirical evidence for differences in hate speech by target identity; we then investigate which patterns structure this variation. We find that the targeted demographic category (e.g. gender/sexuality or race/ethnicity) appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group. We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities. These experiments suggest the importance of considering targeted identity, as well as the social contexts associated with these identities, in automated hate speech classification
%R 10.18653/v1/2022.conll-1.3
%U https://aclanthology.org/2022.conll-1.3
%U https://doi.org/10.18653/v1/2022.conll-1.3
%P 27-39

Markdown (Informal)

[How Hate Speech Varies by Target Identity: A Computational Analysis](https://aclanthology.org/2022.conll-1.3) (Yoder et al., CoNLL 2022)

• How Hate Speech Varies by Target Identity: A Computational Analysis (Yoder et al., CoNLL 2022)

ACL

• Michael Yoder, Lynnette Ng, David West Brown, and Kathleen Carley. 2022. How Hate Speech Varies by Target Identity: A Computational Analysis. In Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL), pages 27–39, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.