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    <titleInfo>
        <title>Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context</title>
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    <name type="personal">
        <namePart type="given">Mladen</namePart>
        <namePart type="family">Karan</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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        <name type="personal">
            <namePart type="given">Vinodkumar</namePart>
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    <abstract>We address the task of automatically detecting toxic content in user generated texts. We fo cus on exploring the potential for preemptive moderation, i.e., predicting whether a particular conversation thread will, in the future, incite a toxic comment. Moreover, we perform preliminary investigation of whether a model that jointly considers all comments in a conversation thread outperforms a model that considers only individual comments. Using an existing dataset of conversations among Wikipedia contributors as a starting point, we compile a new large-scale dataset for this task consisting of labeled comments and comments from their conversation threads.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3514</identifier>
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%0 Conference Proceedings
%T Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context
%A Karan, Mladen
%A Šnajder, Jan
%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 karan-snajder-2019-preemptive
%X We address the task of automatically detecting toxic content in user generated texts. We fo cus on exploring the potential for preemptive moderation, i.e., predicting whether a particular conversation thread will, in the future, incite a toxic comment. Moreover, we perform preliminary investigation of whether a model that jointly considers all comments in a conversation thread outperforms a model that considers only individual comments. Using an existing dataset of conversations among Wikipedia contributors as a starting point, we compile a new large-scale dataset for this task consisting of labeled comments and comments from their conversation threads.
%R 10.18653/v1/W19-3514
%U https://aclanthology.org/W19-3514
%U https://doi.org/10.18653/v1/W19-3514
%P 129-134

Markdown (Informal)

[Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context](https://aclanthology.org/W19-3514) (Karan & Šnajder, ALW 2019)

• Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context (Karan & Šnajder, ALW 2019)

ACL

• Mladen Karan and Jan Šnajder. 2019. Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context. In Proceedings of the Third Workshop on Abusive Language Online, pages 129–134, Florence, Italy. Association for Computational Linguistics.