I studied the community's attempt to engage with the FDA on the social platform X. I wanted to understand whether the linguistic cues and rhetorical strategies used in the community's posts demonstrate characteristics of collective intelligence. To address this question, I used the linguistic model of stance and engagement and conducted six months of digital ethnography to deeply understand the community's communicative practices. I also examined established factors of collective intelligence to assess whether, and in what ways, the community could be considered an instance of collective intelligence. Together, these methods led to the development of a codebook that captures the community's rhetorical strategies when attempting to interact with institutions on a social platform.

In collaboration with BSMS student Carly Atwell on a study examining how the ALS community engages with FDA policy drafts through Regulations.gov. We analyzed public comments on an ALS-related policy, coded them thematically, and tracked which inputs influenced policy revisions. We show that patient communities have substantive, detailed input to offer across multiple stages of health regulatory processes. They provide specific technical recommendations about clinical trial design, regulatory timelines, and risk-benefit assessments. However, our findings also reveal a mismatch: while institutions want structured, evidence-based feedback on discrete policy elements, many patients struggled to provide this, instead offering urgent but vague demands, suggesting that current digital participation platforms fail to bridge the gap between institutional needs and patient capabilities.

I am currently studying the scientific visualizations that the ALS community shares on social media to support different arguments. The community frequently reuses scientific visualizations originally produced by institutions such as the FDA, treating them as evidence to support claims or challenge decisions. For this work, my focus goes beyond assessing whether people interpret the scientific visualizations "correctly." Instead, I aim to understand how individuals use these visual representations to communicate their needs, values, and concerns. The broader goal of the project is to explore whether scientific representations can be designed in ways that integrate both experiential knowledge from communities and institutional knowledge from experts.

I led a study, alongside undergraduate student Megan Genetti, examining knowledge-seeking practices during pregnancy. Through interviews with 14 participants, we identified various operations on knowledge that pregnant people perform when seeking, evaluating, and using information across different aspects of pregnancy. A key finding revealed that factual information itself is not the primary driver of information-seeking behavior, and people rely on emotional support and reassurance as well as entertainment and accessibility of information. We also show that people's embodied knowledge is highly valued by people, sometimes even more than the medical advice.