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        <title>Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition</title>
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    <name type="personal">
        <namePart type="given">Xiaolei</namePart>
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    <name type="personal">
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        <namePart type="family">Dernoncourt</namePart>
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    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="given">J</namePart>
        <namePart type="family">Paul</namePart>
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            <title>Proceedings of the Twelfth Language Resources and Evaluation Conference</title>
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    <abstract>Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this work, we assemble and publish a multilingual Twitter corpus for the task of hate speech detection with inferred four author demographic factors: age, country, gender and race/ethnicity. The corpus covers five languages: English, Italian, Polish, Portuguese and Spanish. We evaluate the inferred demographic labels with a crowdsourcing platform, Figure Eight. To examine factors that can cause biases, we take an empirical analysis of demographic predictability on the English corpus. We measure the performance of four popular document classifiers and evaluate the fairness and bias of the baseline classifiers on the author-level demographic attributes.</abstract>
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        <url>https://aclanthology.org/2020.lrec-1.180</url>
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%0 Conference Proceedings
%T Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
%A Huang, Xiaolei
%A Xing, Linzi
%A Dernoncourt, Franck
%A Paul, Michael J.
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F huang-etal-2020-multilingual
%X Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this work, we assemble and publish a multilingual Twitter corpus for the task of hate speech detection with inferred four author demographic factors: age, country, gender and race/ethnicity. The corpus covers five languages: English, Italian, Polish, Portuguese and Spanish. We evaluate the inferred demographic labels with a crowdsourcing platform, Figure Eight. To examine factors that can cause biases, we take an empirical analysis of demographic predictability on the English corpus. We measure the performance of four popular document classifiers and evaluate the fairness and bias of the baseline classifiers on the author-level demographic attributes.
%U https://aclanthology.org/2020.lrec-1.180
%P 1440-1448

Markdown (Informal)

[Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition](https://aclanthology.org/2020.lrec-1.180) (Huang et al., LREC 2020)

• Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (Huang et al., LREC 2020)

ACL

• Xiaolei Huang, Linzi Xing, Franck Dernoncourt, and Michael J. Paul. 2020. Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1440–1448, Marseille, France. European Language Resources Association.