Betsy Pleasants wants to understand how people look for information about reproductive care. And since people often seek answers for their questions online, including on social media, that’s where she takes her research. In her Ph.D. thesis, for instance, she parsed Reddit communities and TikTok feeds to understand how people discussed abortion, and what information they shared about it. She conducted her analysis using a creative mix of methods: computational approaches and language models, alongside qualitative analysis and epidemiology.

It’s an innovative approach that allowed her “to get both the breadth and the depth of what we’re studying,” she said, and that her mentors have celebrated as “pioneering” and “cutting-edge.” But it ruffles its share of feathers, too. Historically, academia has been hesitant to use analysis of social media data for research, “particularly because it’s not necessarily yielding generalizable results even if they are quantitative findings. People have a lot of qualms about that,” she said.

Her counterargument, that “not all research has to be generalizable to be meaningful,” gets to the core of the profound scientific tension that she tries to overcome in her career, “the false dichotomy of qualitative and quantitative.”

Quantitative data gets more credibility, but Pleasants said she believes no research in a field as complex as medical decision-making can be completed without the nuances of qualitative analysis. Her research interests are taking her toward “reproductive health information ecosystems,” or “how things like social media, but also clinicians and peers and family and regular media, come together to create the context in which people are making their reproductive health decisions.”

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Betsy Pleasants