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        <title>User-Level Race and Ethnicity Predictors from Twitter Text</title>
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        <namePart type="given">Daniel</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>User demographic inference from social media text has the potential to improve a range of downstream applications, including real-time passive polling or quantifying demographic bias. This study focuses on developing models for user-level race and ethnicity prediction. We introduce a data set of users who self-report their race/ethnicity through a survey, in contrast to previous approaches that use distantly supervised data or perceived labels. We develop predictive models from text which accurately predict the membership of a user to the four largest racial and ethnic groups with up to .884 AUC and make these available to the research community.</abstract>
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%0 Conference Proceedings
%T User-Level Race and Ethnicity Predictors from Twitter Text
%A Preoţiuc-Pietro, Daniel
%A Ungar, Lyle
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F preotiuc-pietro-ungar-2018-user
%X User demographic inference from social media text has the potential to improve a range of downstream applications, including real-time passive polling or quantifying demographic bias. This study focuses on developing models for user-level race and ethnicity prediction. We introduce a data set of users who self-report their race/ethnicity through a survey, in contrast to previous approaches that use distantly supervised data or perceived labels. We develop predictive models from text which accurately predict the membership of a user to the four largest racial and ethnic groups with up to .884 AUC and make these available to the research community.
%U https://aclanthology.org/C18-1130
%P 1534-1545

Markdown (Informal)

[User-Level Race and Ethnicity Predictors from Twitter Text](https://aclanthology.org/C18-1130) (Preoţiuc-Pietro & Ungar, COLING 2018)

• User-Level Race and Ethnicity Predictors from Twitter Text (Preoţiuc-Pietro & Ungar, COLING 2018)

ACL

• Daniel Preoţiuc-Pietro and Lyle Ungar. 2018. User-Level Race and Ethnicity Predictors from Twitter Text. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1534–1545, Santa Fe, New Mexico, USA. Association for Computational Linguistics.