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    <titleInfo>
        <title>Analysis of Annotator Demographics in Sexism Detection</title>
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    <name type="personal">
        <namePart type="given">Narjes</namePart>
        <namePart type="family">Tahaei</namePart>
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    <name type="personal">
        <namePart type="given">Sabine</namePart>
        <namePart type="family">Bergler</namePart>
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            <title>Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)</title>
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        <name type="personal">
            <namePart type="given">Agnieszka</namePart>
            <namePart type="family">Faleńska</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Christine</namePart>
            <namePart type="family">Basta</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Marta</namePart>
            <namePart type="family">Costa-jussà</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Debora</namePart>
            <namePart type="family">Nozza</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>This study explores the effect of annotators’ demographic features on labeling sexist content in social media datasets, specifically focusing on the EXIST dataset, which includes direct sexist messages, reports and descriptions of sexist experiences and stereotypes. We investigate how various demographic backgrounds influence annotation outcomes and examine methods to incorporate these features into BERT-based model training. Our experiments demonstrate that adding demographic information improves performance in detecting sexism and assessing intention of the author.</abstract>
    <identifier type="citekey">tahaei-bergler-2024-analysis</identifier>
    <identifier type="doi">10.18653/v1/2024.gebnlp-1.24</identifier>
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        <url>https://aclanthology.org/2024.gebnlp-1.24</url>
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        <date>2024-08</date>
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            <start>376</start>
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%0 Conference Proceedings
%T Analysis of Annotator Demographics in Sexism Detection
%A Tahaei, Narjes
%A Bergler, Sabine
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Goldfarb-Tarrant, Seraphina
%Y Nozza, Debora
%S Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F tahaei-bergler-2024-analysis
%X This study explores the effect of annotators’ demographic features on labeling sexist content in social media datasets, specifically focusing on the EXIST dataset, which includes direct sexist messages, reports and descriptions of sexist experiences and stereotypes. We investigate how various demographic backgrounds influence annotation outcomes and examine methods to incorporate these features into BERT-based model training. Our experiments demonstrate that adding demographic information improves performance in detecting sexism and assessing intention of the author.
%R 10.18653/v1/2024.gebnlp-1.24
%U https://aclanthology.org/2024.gebnlp-1.24
%U https://doi.org/10.18653/v1/2024.gebnlp-1.24
%P 376-383

Markdown (Informal)

[Analysis of Annotator Demographics in Sexism Detection](https://aclanthology.org/2024.gebnlp-1.24) (Tahaei & Bergler, GeBNLP-WS 2024)

• Analysis of Annotator Demographics in Sexism Detection (Tahaei & Bergler, GeBNLP-WS 2024)

ACL

• Narjes Tahaei and Sabine Bergler. 2024. Analysis of Annotator Demographics in Sexism Detection. In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 376–383, Bangkok, Thailand. Association for Computational Linguistics.