Detecting Objectifying Language in Online Professor Reviews

Angie Waller, Kyle Gorman


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.
Anthology ID:
2020.wnut-1.23
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
171–180
Language:
URL:
https://www.aclweb.org/anthology/2020.wnut-1.23
DOI:
10.18653/v1/2020.wnut-1.23
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.wnut-1.23.pdf
Optional supplementary material:
 2020.wnut-1.23.OptionalSupplementaryMaterial.pdf