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    <titleInfo>
        <title>The Effects of User Features on Twitter Hate Speech Detection</title>
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    <name type="personal">
        <namePart type="given">Elise</namePart>
        <namePart type="family">Fehn Unsvåg</namePart>
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        <namePart type="given">Björn</namePart>
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            <title>Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)</title>
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        <name type="personal">
            <namePart type="given">Darja</namePart>
            <namePart type="family">Fišer</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ruihong</namePart>
            <namePart type="family">Huang</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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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rob</namePart>
            <namePart type="family">Voigt</namePart>
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        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
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        <name type="personal">
            <namePart type="given">Jacqueline</namePart>
            <namePart type="family">Wernimont</namePart>
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    <abstract>The paper investigates the potential effects user features have on hate speech classification. A quantitative analysis of Twitter data was conducted to better understand user characteristics, but no correlations were found between hateful text and the characteristics of the users who had posted it. However, experiments with a hate speech classifier based on datasets from three different languages showed that combining certain user features with textual features gave slight improvements of classification performance. While the incorporation of user features resulted in varying impact on performance for the different datasets used, user network-related features provided the most consistent improvements.</abstract>
    <identifier type="citekey">fehn-unsvag-gamback-2018-effects</identifier>
    <identifier type="doi">10.18653/v1/W18-5110</identifier>
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        <date>2018-10</date>
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%0 Conference Proceedings
%T The Effects of User Features on Twitter Hate Speech Detection
%A Fehn Unsvåg, Elise
%A Gambäck, Björn
%Y Fišer, Darja
%Y Huang, Ruihong
%Y Prabhakaran, Vinodkumar
%Y Voigt, Rob
%Y Waseem, Zeerak
%Y Wernimont, Jacqueline
%S Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F fehn-unsvag-gamback-2018-effects
%X The paper investigates the potential effects user features have on hate speech classification. A quantitative analysis of Twitter data was conducted to better understand user characteristics, but no correlations were found between hateful text and the characteristics of the users who had posted it. However, experiments with a hate speech classifier based on datasets from three different languages showed that combining certain user features with textual features gave slight improvements of classification performance. While the incorporation of user features resulted in varying impact on performance for the different datasets used, user network-related features provided the most consistent improvements.
%R 10.18653/v1/W18-5110
%U https://aclanthology.org/W18-5110
%U https://doi.org/10.18653/v1/W18-5110
%P 75-85

Markdown (Informal)

[The Effects of User Features on Twitter Hate Speech Detection](https://aclanthology.org/W18-5110) (Fehn Unsvåg & Gambäck, ALW 2018)

• The Effects of User Features on Twitter Hate Speech Detection (Fehn Unsvåg & Gambäck, ALW 2018)

ACL

• Elise Fehn Unsvåg and Björn Gambäck. 2018. The Effects of User Features on Twitter Hate Speech Detection. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 75–85, Brussels, Belgium. Association for Computational Linguistics.