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    <titleInfo>
        <title>Subversive Toxicity Detection using Sentiment Information</title>
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        <namePart type="given">Eloi</namePart>
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            <title>Proceedings of the Third Workshop on Abusive Language Online</title>
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        <name type="personal">
            <namePart type="given">Sarah</namePart>
            <namePart type="given">T</namePart>
            <namePart type="family">Roberts</namePart>
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            <namePart type="given">Joel</namePart>
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        <name type="personal">
            <namePart type="given">Vinodkumar</namePart>
            <namePart type="family">Prabhakaran</namePart>
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            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
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    <abstract>The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3501</identifier>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T Subversive Toxicity Detection using Sentiment Information
%A Brassard-Gourdeau, Eloi
%A Khoury, Richard
%Y Roberts, Sarah T.
%Y Tetreault, Joel
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Third Workshop on Abusive Language Online
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F brassard-gourdeau-khoury-2019-subversive
%X The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection.
%R 10.18653/v1/W19-3501
%U https://aclanthology.org/W19-3501
%U https://doi.org/10.18653/v1/W19-3501
%P 1-10

Markdown (Informal)

[Subversive Toxicity Detection using Sentiment Information](https://aclanthology.org/W19-3501) (Brassard-Gourdeau & Khoury, ALW 2019)

• Subversive Toxicity Detection using Sentiment Information (Brassard-Gourdeau & Khoury, ALW 2019)

ACL

• Eloi Brassard-Gourdeau and Richard Khoury. 2019. Subversive Toxicity Detection using Sentiment Information. In Proceedings of the Third Workshop on Abusive Language Online, pages 1–10, Florence, Italy. Association for Computational Linguistics.