Detecting Objectifying Language in Online Professor Reviews
This addresses the issue of objectifying comments in academic reviews, which is a domain-specific problem for online platforms and educators, but the approach is incremental as it builds on existing text classification methods.
The study tackled the problem of detecting objectifying language in online professor reviews by developing and ensembling two supervised text classifiers, and used the model to analyze correlations with website interface changes and teacher gender over ten years.
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.