I work on examining and developing theory-driven communication strategies that address persistent and emergent public health challenges (e.g., health disparities; health misinformation; controversial risk issues; etc.). I study health communication through the lens of individual level interventions, collective behavior analysis, and message evaluation in the information ecosystem, with the goal of promoting policy change and health equity.

profile picture taken by: Jingjin Li


Focused Areas

Effects of Strategic Messages on Promoting Health Behaviors and Policy Change

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. (revise and resubmit). Exemplification effects on policy support: Exemplar familiarity, narrative vividness, and perceptions of maternal health disparities.

Understanding and Evaluating Media Messaging about 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.

Advancing Health Communication Theories by Integrating Big, Observational Data

Xu, Y., & Margolin, D. (revise and resubmit). Collective information seeking during a health crisis: Predictors of Google Trends during COVID-19.

Methods, Data Analytics


e.g., Media effects and message effects; Selective exposure

Content Analysis

e.g., TV News video content in-depth coding

Machine Learning

e.g., Supervised machine learning for textual analysis

Big Data Analysis

e.g., Google Trends analysis; Large scale news transcription analysis

Advanced Statistical Methods

e.g., Multilevel modeling; time series analysis; SEM; etc.

Ongoing Projects

Collective information seeking during a health crisis: What predicts Google Trends during COVID-19?

How might selective exposure complicate inoculation effects for controversial social issues?

How are demographic characteristics associated with the volume of local news coverage of racial disparities during COVID-19 pandemic?

How do relational motivations influence or bias people's belief in scientific facts on social media?

Recent Publications