Human-Centered AI & Health Informatics | Theory-Driven Social Data Science

I am Yiwei Xu (pronunciation: ee-way shoo), Assistant Professor in the College of Information at the University of Maryland, College Park (UMD iSchool). I am affiliated with the Institute for Trustworthy AI in Law & Society (TRAILS) and the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM).

I study how to leverage information and technology for social good, including (a) promoting health behaviors and improving health equity, (b) understanding selective exposure and reducing polarization, and (c) addressing problematic information. I conduct experiments, surveys, and content analyses by incorporating computational methods. My work appears in venues such as PNAS Nexus, Health Communication, Political Communication, International Journal of Communication, Milbank Quarterly, AAAI ICWSM, etc. I received a Top Paper Award and the Annie Lang Outstanding Dissertation Award from the International Communication Association (ICA) Information Systems Division. My research has received support through external grants from the National Science Foundation (NSF), and competitive internal grants from UW CIP, UW Population Health Initiative, Garvey Institute for Brain Health Solutions, etc.

I obtained my Ph.D. from the Department of Communication at Cornell University. During 2023 - 2025, I was a Postdoctoral Scholar at the Center for an Informed Public (CIP) in Information School at the University of Washington, Seattle (UW iSchool); I was also a Data Science Postdoctoral Fellow affiliated with the UW eScience Institute. I have been incredibly fortunate to receive exceptional mentorship from my academic advisors: Dr. Emma S. Spiro at UW, Dr. Jeff Niederdeppe at Cornell, Dr. Erin Ash at Clemson, and I enjoy paying it forward by mentoring my advisees and students.

Students who are interested in working with me can refer to this page: yiweixu.net/mentorship, where you will find information about PhD application for prospective students, my PhD advising philosophy, PhD committee requests, research opportunities for UMD Masters and undergraduate students, as well as recommendation letter requests.

Research

Focused Areas

The goals of my research are to (a) advance social scientific theory development, (b) to inform evidence-based policy, and (c) to provide implications for information and technology design.

My current research focus is on the social and psychological impacts of generative AI. There are three specific directions my collaborators and I are working on: (1) examine AI's role in health information seeking, (2) understand the role of LLMs in amplifying strategic information campaigns, (3) evaluate potential bias in LLMs' outputs and improve LLMs in delivering health information. My work is highly interdisciplinary, involving collaboration with outstanding researchers from Information Science, Communication, Computer Science, Political Science, Public Health, Psychology, etc.

Social Data Science Methods and Analytics

Traditional and Computational Experiments

e.g., Longitudinal panel experiment; Computational experiment with self-programmed mock webpage

Surveys

e.g., Survey experiment; omnibus survey

Content Analyses

e.g., TV News video content analysis with fine-grained human coding

Machine Learning

e.g., Large-scale content analysis with supervised machine learning

Big Data Analytics

e.g., Online longitudinal observational data (Google Trends) ; Text as data (Large scale news transcription extraction and analysis)

Advanced Statistical Methods

e.g., Multilevel modeling; Time series analysis; Structural equation modeling; etc.

Publications and Selected Ongoing Work

Social and Psychological Impacts of Generative AI

Dash, S., Xu, Y., Jalbert, M., & Spiro, E. S. (2025). The persuasive potential of AI-paraphrased information at scale. PNAS Nexus, 4(7), pgaf207.

Xian, L., Li, L., Xu, Y., Zhang, B. Z., & Hemphill, L. (2024). Landscape of large language models in global English news: Topics, sentiments, and spatiotemporal analysis. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 18(1), 1661-1673.

Liao, W., Cai, L., & Xu, Y. (in progress). Understanding people's emerging beliefs about AI as a source for seeking and processing health and scientific information.

Xu, Y., Dash, S., Kang, S., Liao, W., & Spiro, E. S. (in progress). AI summaries and attitude change.

Dash, S., Xu, Y., Cai, L., Liao, W., & Spiro, E. S. (in progress). AI summaries and selective exposure.

Understanding Users' Health Information Seeking and Processing Behaviors

Xu, Y., & Margolin, D. (2024). Predictors of collective information seeking during a health crisis: A time-series and cross-sectional analysis of Google Trends during COVID-19. Health Communication. 39(2), 388-402.

Liao, W., Cai, L., & Xu, Y. (in progress). Understanding people's emerging beliefs about AI as a source for seeking and processing health and scientific information.

Xu, Y., Dash, S., Kang, S., Liao, W., & Spiro, E. S. (in progress). AI summaries and attitude change.

Dash, S., Xu, Y., Cai, L., Liao, W., & Spiro, E. S. (in progress). AI summaries and selective exposure.

Evaluating Content of Health (Disparities) in the Information Ecosystem

Ash, E., Cox, E., Xu, Y., & Boatwright, B. (2025). Promoting teen pregnancy prevention: An analysis of social media content strategy over five years. Health Communication.

Neumann, M., Moore, S., Baum, L. M., Oleinikov, P., Xu, Y., Niederdeppe, J., Gollust, S. E., & Fowler, E. F. (2024). Politicizing masks? Examining the volume and content of local news coverage of face coverings in the U.S. through the COVID-19 pandemic. Political Communication.

Xu, Y., Farkouh, E. K., Dunetz, C. A., Varanasi, S. L., Mathews, S., Gollust, S. E., Fowler, E. F., Moore, S., Lewis, N. A., Niederdeppe, J. (2023). Local TV news coverage of racial disparities in COVID-19 during the first wave of the pandemic, March-June 2020. Race and Social Problems.

Xu, Y., Neumann, M., Fowler. E. F., Gollust, S. E., & Niederdeppe, J. (in progress). Community characteristics predict local news agenda building about racial health disparities.

Effects of Various Information Strategies on Promoting Health Behaviors and Evidenced-Based Policies

Liu, J., Xu, Y., & Niederdeppe, J. (2025) Inoculation theory and public policy. The Handbook of Inoculation Theory. John Wiley & Sons, Inc.

Ash, E., Xu, Y., Pool, R., Schulenberg, K., Mikkilineni, S. D., & Baraka, T. (2023). Exemplification effects on policy support: Exemplar familiarity, narrative vividness, and perceptions of maternal health disparities. Health Communication.

Xu, Y., Margolin, D., & Niederdeppe, J. (2021). Testing strategies to increase source credibility through strategic message design in the context of vaccination and vaccine hesitancy. Health Communication, 36(11), 1354-1367.

Xu, Y., Winett, L. B., Niederdeppe, J. (2021). Evidence of heterogeneity in the direction and magnitude of narrative effects on transportation and counterarguing across three independent samples. International Journal of Communication. 15. 5135–5157.

Niederdeppe, J., Winett, L.B., Xu, Y., Fowler, E. F, & Gollust, S. E. (2021). Evidence-based message strategies to increase public support for state investment in early childhood education: Results from a longitudinal panel experiment. The Milbank Quarterly. 1-44.

Winett, L. B., Niederdeppe, J., Xu, Y., Gollust, S. E., & Fowler, E. F. (2021). When “tried and true” advocacy strategies backfire: Narrative messages can undermine state legislator support for early childcare policies. The Journal of Public Interest Communications, 5(1), 45-45.

Xu, Y. (in progress). Communicating controversial risk issues: Effects of inoculation messages on selective exposure and subsequent persuasive outcomes.

Selected Grants

National Science Foundation (NSF) Division of Social and Economic Sciences (SBE/SES) Decision, Risk & Management Sciences (DRMS) Doctoral Dissertation Research Improvement Grant.

“Communicating Controversial Risk Issues - Effects of Inoculation Messages on Selective Exposure to Counterattitudinal Messages and Subsequent Persuasive Outcomes.” $30,168 Total. (Award #2242458)

role: co-PI; PI: Dr. Jeff Niederdeppe

February 2023 – December 2024

University of Washington Center for an Informed Public (CIP) Innovation Fund.

“Modeling the Role of Large Language Models in Amplifying Strategic (Dis)Information Campaigns and Examining its Persuasive Effects.” $23,465.35 Total.

role: PI; co-PI: Saloni Dash

March 2024 – June 2025