About

I am Yiwei Xu (Ph.D., 2023, Cornell University), Postdoctoral Scholar at the Center for an Informed Public (CIP) in the Information School at the University of Washington. I am also a Data Science Postdoctoral Fellow at the UW eScience Institute.

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 Health Communication, Political Communication, International Journal of Communication, Race and Social Problems, and ICWSM. I was awarded a Doctoral Dissertation Research Improvement Grant (award #2242458) in Decision, Risk & Management Sciences from the National Science Foundation (NSF) for my dissertation. I also received a Top Paper Award and Annie Lang Outstanding Dissertation Award from the International Communication Association (ICA) Information Systems Division.

In Fall 2025, I will be joining University of Maryland College of Information (UMD iSchool) as a tenure-track Assistant Professor. I enjoy conducting research with diverse groups of researchers and I am always on the lookout for collaborators and students to work with - please feel free to reach out.

Research

Focused Areas

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 (e.g., misinformation). I conduct experiments, surveys, and content analyses by incorporating computational methods.

The goal of my research is 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 as a source for health information seeking [collaborating with UW Communication colleagues], (2) understand the role of LLMs in amplifying disinformation operations [collaborating with UW iSchool colleagues], (3) evaluate potential bias in LLMs' outputs and improve LLMs in delivering health information [collaborating with UW Computer Science colleagues].

Methods and Data 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 scaled up by supervised machine learning

Big Data Analytics

e.g., Online longitudinal big data analysis (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

Social and Psychological Impacts of Generative AI

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.

Dash, S., Xu, Y., & Spiro, E. S. (in progress). Understanding the role of Large Language Models in amplifying heuristic persuasion in disinformation operations.

Liao, W., Xu, Y., Cai, L., & Lin, H. (in progress). Understanding marginalized populations’ emerging beliefs about AI as a source for seeking and processing health and scientific information.

Understanding Online Information Behaviors during Health Crises

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.

Evaluating Media Messaging about Health and Health Equity

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. (2022). Local TV news coverage of racial disparities in COVID-19 during the first wave of the pandemic, March-June 2020. Race and Social Problems.

Neumann, M., Moore, S., Baum, L. M., Oleinikov, P., Xu, Y., Niederdeppe, J., Gollust, S. E., & Fowler, E. F. (2023). 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., 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 Strategic Messages on Promoting Health Behaviors and Evidenced-Based Policies

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.

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

Selected Grants

National Science Foundation (NSF) Doctoral Dissertation Research Improvement Grant in Decision, Risk & Management Sciences.

“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 – September 2024