In spring 2022, Betsy Pleasants was studying how the Covid-19 pandemic affected abortion services when a leaked draft opinion signaled that Roe v. Wade would fall. With her thesis committee, the UC Berkeley graduate student pivoted the project to analyze thousands of posts from the Reddit forum r/abortion, using natural language processing alongside a qualitative close reading of a subset of posts. The dual approach was designed to keep the context and humanity of the data in view while still handling conversations at scale.
The findings reaffirmed well-established access barriers such as high costs and limited appointment availability, but also brought lesser-known challenges into focus: delays in receiving abortion medications by mail, low credibility of online ordering platforms, and fears about legal risks tied to seeking abortion or related care. Pleasants, now a postdoctoral researcher at the University of North Carolina, sees the work as a form of documentation that can support evidence-based policy change.
Her mentor Ushma Upadhyay of UCSF said she knew of no other studies at the time applying NLP to online abortion discourse, and that the method gave a more complete picture than a random sample would have. Danny Valdez of Indiana University, who has used NLP to study vaccine misinformation and addiction, described computational research as a way to see how people communicate when they need help and where policy may be headed. For Pleasants, the project also offers a framework for using AI tools responsibly in sensitive health research.