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Measuring Sexism in US Elections: A Comparative Analysis of X Discourse From 2020 to 2024

MCML Authors

Bolei Ma

→ Group Frauke Kreuter
Social Data Science and AI

Anna-Carolina Haensch

Dr.

→ Group Frauke Kreuter
Social Data Science and AI

Abstract

Sexism continues to influence political campaigns, affecting public perceptions of candidates in a variety of ways. This paper examines sexist content on the social media platform X during the 2020 and 2024 US election campaigns, focusing on both male and female candidates. Two approaches, single-step and two-step categorization, were employed to classify tweets into different sexism categories. By comparing these approaches against a human-annotated subsample, we found that the single-step approach outperformed the two-step approach. Our analysis further reveals that sexist content increased over time, particularly between the 2020 and 2024 elections, indicating that female candidates face a greater volume of sexist tweets compared to their male counterparts. Compared to human annotations, GPT-4 struggled with detecting sexism, reaching an accuracy of about 51%. Given both the low agreement among the human annotators and the obtained accuracy of the model, our study emphasizes the challenges in detecting complex social phenomena such as sexism.

inproceedings FNW+25

CODI @EMNLP 2025

6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences at the Conference on Empirical Methods in Natural Language Processing. Suzhou, China, Nov 04-09, 2025.