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    <titleInfo>
        <title>Detecting Objectifying Language in Online Professor Reviews</title>
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    <name type="personal">
        <namePart type="given">Angie</namePart>
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            <title>Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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            <namePart type="given">Alan</namePart>
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    <abstract>Student reviews often make reference to professors’ physical appearances. Until recently RateMyProfessors.com, the website of this study’s focus, used a design feature to encourage a “hot or not” rating of college professors. In the wake of recent #MeToo and #TimesUp movements, social awareness of the inappropriateness of these reviews has grown; however, objectifying comments remain and continue to be posted in this online context. We describe two supervised text classifiers for detecting objectifying commentary in professor reviews. We then ensemble these classifiers and use the resulting model to track objectifying commentary at scale. We measure correlations between objectifying commentary, changes to the review website interface, and teacher gender across a ten-year period.</abstract>
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    <identifier type="doi">10.18653/v1/2020.wnut-1.23</identifier>
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        <date>2020-11</date>
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            <start>171</start>
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%0 Conference Proceedings
%T Detecting Objectifying Language in Online Professor Reviews
%A Waller, Angie
%A Gorman, Kyle
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F waller-gorman-2020-detecting
%X Student reviews often make reference to professors’ physical appearances. Until recently RateMyProfessors.com, the website of this study’s focus, used a design feature to encourage a “hot or not” rating of college professors. In the wake of recent #MeToo and #TimesUp movements, social awareness of the inappropriateness of these reviews has grown; however, objectifying comments remain and continue to be posted in this online context. We describe two supervised text classifiers for detecting objectifying commentary in professor reviews. We then ensemble these classifiers and use the resulting model to track objectifying commentary at scale. We measure correlations between objectifying commentary, changes to the review website interface, and teacher gender across a ten-year period.
%R 10.18653/v1/2020.wnut-1.23
%U https://aclanthology.org/2020.wnut-1.23
%U https://doi.org/10.18653/v1/2020.wnut-1.23
%P 171-180

Markdown (Informal)

[Detecting Objectifying Language in Online Professor Reviews](https://aclanthology.org/2020.wnut-1.23) (Waller & Gorman, WNUT 2020)

• Detecting Objectifying Language in Online Professor Reviews (Waller & Gorman, WNUT 2020)

ACL

• Angie Waller and Kyle Gorman. 2020. Detecting Objectifying Language in Online Professor Reviews. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 171–180, Online. Association for Computational Linguistics.