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        <title>“Condescending, Rude, Assholes”: Framing gender and hostility on Stack Overflow</title>
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    <name type="personal">
        <namePart type="given">Sian</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">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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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 disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3519</identifier>
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%0 Conference Proceedings
%T “Condescending, Rude, Assholes”: Framing gender and hostility on Stack Overflow
%A Brooke, Sian
%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 brooke-2019-condescending
%X The disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.
%R 10.18653/v1/W19-3519
%U https://aclanthology.org/W19-3519
%U https://doi.org/10.18653/v1/W19-3519
%P 172-180

Markdown (Informal)

[“Condescending, Rude, Assholes”: Framing gender and hostility on Stack Overflow](https://aclanthology.org/W19-3519) (Brooke, ALW 2019)

• “Condescending, Rude, Assholes”: Framing gender and hostility on Stack Overflow (Brooke, ALW 2019)

ACL

• Sian Brooke. 2019. “Condescending, Rude, Assholes”: Framing gender and hostility on Stack Overflow. In Proceedings of the Third Workshop on Abusive Language Online, pages 172–180, Florence, Italy. Association for Computational Linguistics.