About


I research collective level phenomena in health communication (e.g., collective health information seeking, community level factors that shape media agenda). I work on examining and developing theory-driven communication strategies that address persistent and emergent public health challenges (e.g., racial disparities, vaccine hesitancy, misinformation, gun violence, etc), with the goal of promoting policy change and health equity. I conduct experiments, survey, and content analysis by incorporating computational pipelines (e.g., web experiment, supervised machine learning, and digital trace data) to study strategic health communication. My dissertation has been recommended for funding by National Science Foundation (NSF) Doctoral Dissertation Research Improvement Grant in Decision, Risk & Management Science.


profile picture taken by: Jingjin Li

Research

Methods and Data Analytics

Traditional and Computational Experiment

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

Content Analysis

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.

Focused Areas

Understanding Online Collective Behaviors during Health Crises

Xu, Y., & Margolin, D. (revise and resubmit). Predictors of collective information seeking during a health crisis: A time-series and cross-sectional analysis of Google Trends during COVID-19.

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.

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.

Ongoing Projects

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

Keywords: Online collective behavior, digital trace data, time series analysis, cross sectional analysis

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

Keywords: Computational experiment with self-programmed mock web, behavioral measures, selective exposure, inoculation, controversial issues

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

Keywords: Community structure model, agenda building, supervised machine learning, multilevel modeling

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

Keywords: Relational motivations, information processing, social media, science communication

Recent Publications