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Don’t Read the Comments: Examining Social Media Discourse on Trans Athletes

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  • Zein Murib

    (Department of Political Science, Fordham University, New York, NY 10023, USA)

Abstract

How are transgender athletes understood in popular discourse? This paper adapts and merges Glaser and Strauss’ 1967 Grounded Theory Method with computerized Automated Text Analysis to provide clarity on large-n datasets comprised of social media posts made about transgender athletes. After outlining the procedures of this new approach to social media data, I present findings from a study conducted on comments made in response to YouTube videos reporting transgender athletes. A total of 60,000 comments made on three YouTube videos were scraped for the analysis, which proceeded in two steps. The first was an iterative, grounded analysis of the top 500 “liked” comments to gain insight into the trends that emerged. Automated Text Analysis was then used to explore latent connections amongst the 60,000 comments. This descriptive analysis of thousands of datapoints revealed three dominant ways that people talk about transgender athletes: an attachment to biology as determinative of athletic abilities, a racialized understanding of who constitutes a proper “girl”, and perceptions of sex-segregated sports as the sole way to ensure fairness in athletic opportunities. The paper concludes by drawing out the implications of this research for how scholars understand the obstacles facing transgender political mobilizations, presents strategies for addressing these roadblocks, and underscores the importance of descriptive studies of discourse in political science research concerned with marginalization and inequality.

Suggested Citation

  • Zein Murib, 2022. "Don’t Read the Comments: Examining Social Media Discourse on Trans Athletes," Laws, MDPI, vol. 11(4), pages 1-19, July.
    • Handle: RePEc:gam:jlawss:v:11:y:2022:i:4:p:53-:d:854142
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
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