@inproceedings{fuchs-etal-2025-measuring,
title = "Measuring Sexism in {US} Elections: A Comparative Analysis of {X} Discourse from 2020 to 2024",
author = "Fuchs, Anna and
Noltenius, Elisa and
Weinzierl, Caroline and
Ma, Bolei and
Haensch, Anna-Carolina",
editor = "Strube, Michael and
Braud, Chloe and
Hardmeier, Christian and
Li, Junyi Jessy and
Loaiciga, Sharid and
Zeldes, Amir and
Li, Chuyuan",
booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.codi-1.12/",
pages = "130--147",
ISBN = "979-8-89176-343-2",
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."
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%0 Conference Proceedings
%T Measuring Sexism in US Elections: A Comparative Analysis of X Discourse from 2020 to 2024
%A Fuchs, Anna
%A Noltenius, Elisa
%A Weinzierl, Caroline
%A Ma, Bolei
%A Haensch, Anna-Carolina
%Y Strube, Michael
%Y Braud, Chloe
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%Y Li, Chuyuan
%S Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-343-2
%F fuchs-etal-2025-measuring
%X 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.
%U https://aclanthology.org/2025.codi-1.12/
%P 130-147
Markdown (Informal)
[Measuring Sexism in US Elections: A Comparative Analysis of X Discourse from 2020 to 2024](https://aclanthology.org/2025.codi-1.12/) (Fuchs et al., CODI 2025)
ACL