Betsy Pleasants, a graduate student at UC Berkeley School of Public Health, shifted her dissertation focus in spring 2022 when a leaked Supreme Court draft opinion indicated that Roe v. Wade would be overturned. Rather than continuing to study pandemic-related disruptions to abortion access, she and her thesis committee adapted the project to examine real-time conversations about emerging barriers following the June decision that ended nearly 50 years of constitutional abortion protections.
Pleasants and her colleagues analyzed more than 7,000 posts from the Reddit forum r/abortion using natural language processing, a computational method that identifies patterns in human language. They combined this automated analysis with close reading of selected posts, developing a framework for using artificial intelligence tools while maintaining attention to the human context behind the data. The study identified 55 distinct topics across posts from the months surrounding the Supreme Court’s draft opinion leak and final decision.
The research confirmed well-established access barriers including high costs and limited appointment availability. It also uncovered lesser-known challenges not previously documented at scale, such as delays in receiving abortion medications by mail, credibility concerns about online ordering platforms, and legal anxieties associated with seeking abortion care. Pleasants noted that Reddit’s relative anonymity enabled people to share personal experiences they might not disclose through conventional research channels, and the platform captured perspectives from individuals who had not engaged with the formal health care system.
Now a postdoctoral research associate at the University of North Carolina’s Center for Women’s Health Research, Pleasants was named a STAT Wunderkind for her work. Her research demonstrates how computational methods can provide comprehensive views of population-level communication patterns and health-seeking behavior at scales difficult to achieve through traditional sampling approaches, offering potential insights for future reproductive health policy development.
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