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        <title>Enriching Abusive Language Detection with Community Context</title>
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        <namePart type="given">Haji</namePart>
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            <title>Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)</title>
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            <namePart type="given">Kanika</namePart>
            <namePart type="family">Narang</namePart>
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        <name type="personal">
            <namePart type="given">Aida</namePart>
            <namePart type="family">Mostafazadeh Davani</namePart>
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    <abstract>Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups. One way to engage with non-dominant perspectives is to add context around conversations. Previous research has leveraged user- and thread-level features, but it often neglects the spaces within which productive conversations take place. Our paper highlights how community context can improve classification outcomes in abusive language detection. We make two main contributions to this end. First, we demonstrate that online communities cluster by the nature of their support towards victims of abuse. Second, we establish how community context improves accuracy and reduces the false positive rates of state-of-the-art abusive language classifiers. These findings suggest a promising direction for context-aware models in abusive language research.</abstract>
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    <identifier type="doi">10.18653/v1/2022.woah-1.13</identifier>
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        <date>2022-07</date>
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            <start>131</start>
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%0 Conference Proceedings
%T Enriching Abusive Language Detection with Community Context
%A Saleem, Haji Mohammad
%A Kurrek, Jana
%A Ruths, Derek
%Y Narang, Kanika
%Y Mostafazadeh Davani, Aida
%Y Mathias, Lambert
%Y Vidgen, Bertie
%Y Talat, Zeerak
%S Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington (Hybrid)
%F saleem-etal-2022-enriching
%X Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups. One way to engage with non-dominant perspectives is to add context around conversations. Previous research has leveraged user- and thread-level features, but it often neglects the spaces within which productive conversations take place. Our paper highlights how community context can improve classification outcomes in abusive language detection. We make two main contributions to this end. First, we demonstrate that online communities cluster by the nature of their support towards victims of abuse. Second, we establish how community context improves accuracy and reduces the false positive rates of state-of-the-art abusive language classifiers. These findings suggest a promising direction for context-aware models in abusive language research.
%R 10.18653/v1/2022.woah-1.13
%U https://aclanthology.org/2022.woah-1.13
%U https://doi.org/10.18653/v1/2022.woah-1.13
%P 131-142

Markdown (Informal)

[Enriching Abusive Language Detection with Community Context](https://aclanthology.org/2022.woah-1.13) (Saleem et al., WOAH 2022)

• Enriching Abusive Language Detection with Community Context (Saleem et al., WOAH 2022)

ACL

• Haji Mohammad Saleem, Jana Kurrek, and Derek Ruths. 2022. Enriching Abusive Language Detection with Community Context. In Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), pages 131–142, Seattle, Washington (Hybrid). Association for Computational Linguistics.