In spring 2022, as a graduate student at UC Berkeley School of Public Health, Betsy Pleasants was studying the impact of the Covid‑19 pandemic on abortion services when a leaked draft opinion signaled the U.S. Supreme Court would overturn Roe v. Wade. The Court finalized that decision in June 2022, ending the federal constitutional right to abortion and triggering rapid policy shifts and increased state restrictions.
Pleasants and her thesis committee agreed to shift the dissertation to capture how that seismic policy change was affecting people in real time. Now a postdoctoral research associate at the Center for Women’s Health Research at the University of North Carolina, Pleasants framed the revised project as an effort to document lived experience and to inform evidence‑based policy responses.
To observe conversations that might be missed by clinic‑based or survey research, Pleasants and collaborators turned to the Reddit community r/abortion. The forum’s relative anonymity offered space for candid discussion of reproductive experiences, including those from people who had not interacted with the formal health care system.
The team applied natural language processing (NLP) to more than 7,000 posts gathered from the period before, during, and after the leak of the Supreme Court’s draft opinion. Computational analysis identified 55 distinct topics across that corpus. Human oversight was central: researchers used the NLP outputs to guide a qualitative close reading of subsets of posts, validating algorithmic categories and adding contextual, narrative detail about how people were navigating barriers.
This mixed‑methods approach combined the scale and pattern recognition of computational tools with the interpretive depth of qualitative analysis, enabling both broad surveillance of discourse and deeper understanding of individual experiences.
The analysis both reaffirmed known obstacles and surfaced emerging challenges in post‑Roe contexts. Well‑established barriers identified in the Reddit corpus included state bans, high out‑of‑pocket costs, limited appointment availability, and long travel distances to clinics. Those findings align with prior evidence about structural access limits.
Emerging issues highlighted by users included delays in receiving abortion medications by mail, concern about the legitimacy and credibility of online ordering platforms for medications, and fears of legal risk when seeking abortion‑related care. Qualitative review also documented the personal impacts of these barriers: heightened anxiety, social isolation, and instances in which individuals considered or attempted self‑managed abortions.
The dataset thus provided insight into both systemic constraints and the psychosocial effects experienced by people seeking reproductive care in a rapidly changing legal landscape.
Pleasants emphasized the ethical complexity of studying sensitive reproductive health discussions on public platforms, noting that posts can describe stigmatized behavior or situations that could carry legal risk. To reduce the risk of reidentification and harm, the research team used a “heavy disguise” process for quotations: they created composite quotes that combined and rephrased similar posts.
These composite quotations were then tested using online search engines and plagiarism‑detection tools to help ensure they could not be traced back to original posts. Human supervision was applied throughout analytic stages to preserve context, prevent misclassification by algorithms, and guide responsible reporting of findings.
Colleagues and mentors praised the approach as forward‑thinking. Using NLP enabled Pleasants to obtain a broad, population‑level perspective on how people communicate when they need help, while qualitative methods maintained attention to nuance and lived experience. Experts argued that such mixed methods can offer a scalable way to monitor shifting needs and inform policy and practice.
Pleasants’ work established practices for combining computational analysis with rigorous qualitative oversight and concrete privacy protections, offering a replicable model for other researchers studying sensitive online discourse at scale.
The research has since expanded to include study of contraception and online misinformation, reflecting the team’s recognition that changing policies increase urgency for timely, ethically conducted research.
Pleasants grew up in North Carolina, experienced variable sex‑education environments, and pursued an undergraduate degree at the University of North Carolina before earning a master’s and doctorate at UC Berkeley. Initially interested in clinical work, she gravitated toward medical anthropology and research that interrogates how history, politics, and culture shape health care access.
Mentors described Pleasants’ decision to apply computational methods to Reddit discourse as visionary for capturing perspectives often missed by conventional methods. Her work has been framed as documentation intended to support evidence‑based policy change and to advocate for expanded access to reproductive health services as policy shifts create new constraints.
Throughout, Pleasants and collaborators emphasize that computational tools such as NLP and AI must be used with caution—paired with human judgment and robust privacy practices—when studying sensitive health topics.