Media Framing and the Rise of Anti-Transgender Legislation: A Computational Analysis

• In Event: Innovative Methodologies and New Perspectives in Queer Criminology Research

Abstract

In the first three months of 2025, nearly 800 anti-trans bills were introduced at the state and federal levels – an 830% increase compared to 2020. Existing research highlights the media’s critical role in shaping public perceptions of crime and facilitating policy diffusion. This study uses computational text analysis to investigate the media's contribution to the complex social dynamics underlying this significant rise in anti-transgender legislation. Specifically, this research employs a machine learning method called topic modeling to examine the frequency and framing of coverage about transgender folks and issues within a dataset of U.S. news media articles collected through Media Cloud. Findings shed light on the media’s role in amplifying transphobic sentiment, provide a foundation for future research examining factors driving the proliferation of anti-transgender policies, and demonstrate how queer criminology can use computational social science methodologies.

Author

• Madison Gerdes, University of Montana