Topics for He but not for She: Quantifying and Classifying Gender Bias in the Media

Author

Tyler J. Lanni, The Graduate Center, City University of New YorkFollow

Date of Degree

6-2023

Document Type

Thesis

Degree Name

M.A.

Program

Linguistics

Advisor

Sarah Ita Levitan

Subject Categories

Computational Linguistics

Keywords

Language bias, Gender stereotypes, Computational linguistics, Political discourse, Gendered language Media representation, Topic modeling, Text classification, News media analysis

Abstract

In this study, we used computational techniques to analyze the language used in news articles to describe female and male politicians. Our corpus included 370 subtexts for male candidates and 374 subtexts for female candidates, gathered through the New York Times API. We conducted two experiments: an LDA topic analysis to explore the data, and a logistic regression to classify the subtexts as either male or female. Our analysis revealed some noteworthy findings that suggest the possibility of developing a gender bias classifier in the future. However, to create a more robust understanding of bias, additional research and data are needed, as well as clearer definitions of what constitutes bias.

Recommended Citation

Lanni, Tyler J., "Topics for He but not for She: Quantifying and Classifying Gender Bias in the Media" (2023). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/5390