See That Post, Get That Shot: Content and Time-Series Analyses of Health Belief Model Constructs on CDC’s Instagram

COVID-19 has posed unprecedented challenges to our public health system but also offered opportunities for us to examine how the Centers for Disease Control and Prevention (CDC), the flagship health authority in the United States which was considered the gold standard globally before the pandemic, designs persuasive vaccine-related messages over time. Grounded in the health belief model (HBM), this study employed quantitative content analysis of 858 vaccine-related Instagram posts from the CDC's @cdcgov account between 2021 and 2023. Then, a time-series analysis was conducted to consider the effect of CDC messaging trends on real-world vaccine behaviors. We found that the CDC posted more frequently between June 2021 and January 2022, and most posts utilized at least one HBM construct. Among six HBM constructs, benefit, self-efficacy, and cues to action were three dominant constructs that appeared on Instagram posts throughout the pandemic. Time-series analysis and Granger causality tests indicate significant causal links between all HBM constructs and newly administered vaccines, except for severity. Interestingly, acknowledging barriers was the only construct that immediately produced a positive effect on newly administered vaccines. In contrast, benefit, susceptibility, and self-efficacy messages exerted short-term negative impacts, yet showed positive influence in the following days. Theoretical contributions and implications are discussed.

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Publication Details

Journal
Health Communication
Published
2026-04-25
DOI
https://doi.org/10.1080/10410236.2026.2662333
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.00
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article

See That Post, Get That Shot: Content and Time-Series Analyses of Health Belief Model Constructs on CDC’s Instagram

Chelsea N. Hampton, Taylor Vasquez, Xiaobei Chen, Benjamin K. Johnson et al.
Health Communication
Data-Driven Disease Surveillance
article

See That Post, Get That Shot: Content and Time-Series Analyses of Health Belief Model Constructs on CDC’s Instagram

Chelsea N. Hampton, Taylor Vasquez, Xiaobei Chen, Benjamin K. Johnson, Dasha Rodimushkin, Samantha Benavides, Lauren Michaels, Chloe Quintero
article en

Abstract

COVID-19 has posed unprecedented challenges to our public health system but also offered opportunities for us to examine how the Centers for Disease Control and Prevention (CDC), the flagship health authority in the United States which was considered the gold standard globally before the pandemic, designs persuasive vaccine-related messages over time. Grounded in the health belief model (HBM), this study employed quantitative content analysis of 858 vaccine-related Instagram posts from the CDC's @cdcgov account between 2021 and 2023. Then, a time-series analysis was conducted to consider the effect of CDC messaging trends on real-world vaccine behaviors. We found that the CDC posted more frequently between June 2021 and January 2022, and most posts utilized at least one HBM construct. Among six HBM constructs, benefit, self-efficacy, and cues to action were three dominant constructs that appeared on Instagram posts throughout the pandemic. Time-series analysis and Granger causality tests indicate significant causal links between all HBM constructs and newly administered vaccines, except for severity. Interestingly, acknowledging barriers was the only construct that immediately produced a positive effect on newly administered vaccines. In contrast, benefit, susceptibility, and self-efficacy messages exerted short-term negative impacts, yet showed positive influence in the following days. Theoretical contributions and implications are discussed.

Health Communication
University of Florida (US)
Gender equality
Openalex Percentile: Top 6%
Data-Driven Disease Surveillance
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See That Post, Get That Shot: Content and Time-Series Analyses of Health Belief Model Constructs on CDC’s Instagram — Chelsea N. Hampton, Taylor Vasquez, et al. · Health Communication (2026) | TGRS Research Map | TGRS