@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://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.codi-1.12/",
    doi = "10.18653/v1/2025.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."
}