Detecting Objectifying Language in Online Professor Reviews

TL;DR

This paper develops supervised classifiers to detect objectifying language in online professor reviews, analyzing trends over ten years and assessing the impact of website design and gender on such comments.

Contribution

It introduces two supervised classifiers and an ensemble approach for identifying objectifying comments in reviews, and applies them to analyze temporal and gender-based patterns.

Findings

Objectifying comments persist despite social awareness.

Website interface changes correlate with review content.

Gender influences the prevalence of objectifying language.

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.