My research examines how social networks, political institutions, and information environments shape democratic behavior, public trust, and political accountability. I use social network analysis, field experiments, and computational methods to study how people encounter, interpret, and act on political information in real-world settings, especially in contexts where institutional trust and media systems are under strain.
A central theme in my research is that political behavior is not shaped only by individual attitudes or formal institutions, but also by the social relationships and information environments in which people are embedded. I study how information travels through communities, how misinformation and polarization affect political judgment, and how interventions can support more reflective and trustworthy forms of civic engagement.
More recently, my work has focused on responsible and human-centered AI for information integrity. This includes developing and evaluating AI-enabled systems that help users examine their own reasoning on difficult public issues, as well as translating research findings into toolkits, curricula, dashboards, and training materials for practitioners. Across these projects, I am interested in how data science and AI can support trustworthy information systems: not only by detecting misinformation, but by improving how people access, interpret, and use information.
Please describe one or two of your most interesting projects.
One of my favorite past projects, with Julien Labonne and Pablo Querubin, “Politician Family Networks and Electoral Outcomes” (https://www.aeaweb.org/articles?id=10.1257/aer.20150343), began as a data puzzle: can we reconstruct political networks that are otherwise invisible? The key turned out to be a nineteenth-century Spanish colonial decree that systematically assigned surnames across the Philippines, leaving historical traces of family networks that could still be measured more than a century later. We linked those naming patterns to contemporary electoral, administrative, and public finance data to study how kinship networks shape political behavior and accountability. The project shows how family networks shape politics by structuring political exchange, coordination, and the incentives elected officials face once in office
One current project studies community-based approaches to countering misinformation in the Philippines. In a randomized field experiment across 160 villages, my coauthor, Julien Labonne and I evaluated in-person workshops that taught participants to recognize common techniques used to spread disinformation online and to discuss these issues with others in their communities. We find that the intervention changed how people consumed political news, shifted discussion toward offline civic and religious networks, reduced perceived polarization, and generated spillover effects among untreated peers. The project also links the intervention to downstream electoral outcomes in the 2025 Philippine elections.
How did you end up where you are today? (Your research journey)
I am an Associate Professor of Political Science and Economics at the University of Michigan. My research began with a question from Philippine politics: how do family networks and local relationships shape democratic accountability? Answering that question pushed me toward unusual data sources, including historical naming records, social networks, administrative data, and election returns.
Over time, that agenda expanded from family networks and local politics to broader questions about misinformation, polarization, and information integrity. At UCLA, I developed more field experimental and survey-based work on political behavior; at Michigan, I have moved further into interdisciplinary collaborations on responsible AI and human-centered technology. The throughline is a focus on how people make political decisions in real-world social and informational environments, and how data science can help us understand those environments more clearly.
What are 1-3 interesting facts about yourself?
My interest in social networks started with a very different kind of network: my first network analysis class was in computer science, on graph theory and distributed computing. I still study networks, but now the nodes are usually people, politicians, and communities instead of computers.