Examining conspiracy theory spillover in the health communication arena: factors that impact COVID-19 conspiratorial beliefs and health-related behaviors

This study examined the strength of different groups of individual-level variables in predicting conspiracy beliefs about Coronavirus disease (COVID-19) and related health behaviors by conducting a survey on a national online sample of U.S. adults. The results indicated that, among a wide range of individual-level variables, including psychopathological variables, cognitive variables, trust perceptions, trait emotions, health-related variables, and demographics, general belief in conspiracy theories (CTs) best predicted belief in specific COVID-19 CTs. In addition, our results showed that a stronger belief in COVID-19 CTs served as a significant predictor of engaging in less avoidance behavior. Furthermore, our results indicated that belief in general CTs does not directly lead to a change in avoidance behavior; the relationship is instead mediated by belief in COVID-19 CTs. Perceived severity of COVID-19 was the best predictor for proactive health behavior, whereas actual vaccination behavior was best predicted by confidence in COVID-19 vaccines. These results were interpreted using a framework that combines health communication theories with the concepts of path dependency and spillover effects in conspiratorial thinking.

Authors

Institutions

Publication Details

Journal
Frontiers in Communication
Published
2025-10-31
DOI
https://doi.org/10.3389/fcomm.2025.1548575
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Examining conspiracy theory spillover in the health communication arena: factors that impact COVID-19 conspiratorial beliefs and health-related behaviors

Leslie Klein, Jin Yan, Xuerong Lu, Ivanka Pjesivac et al.
Frontiers in Communication
Misinformation and Its Impacts
article

Examining conspiracy theory spillover in the health communication arena: factors that impact COVID-19 conspiratorial beliefs and health-related behaviors

Leslie Klein, Jin Yan, Xuerong Lu, Ivanka Pjesivac, Wenqing Zhao
article en

Abstract

This study examined the strength of different groups of individual-level variables in predicting conspiracy beliefs about Coronavirus disease (COVID-19) and related health behaviors by conducting a survey on a national online sample of U.S. adults. The results indicated that, among a wide range of individual-level variables, including psychopathological variables, cognitive variables, trust perceptions, trait emotions, health-related variables, and demographics, general belief in conspiracy theories (CTs) best predicted belief in specific COVID-19 CTs. In addition, our results showed that a stronger belief in COVID-19 CTs served as a significant predictor of engaging in less avoidance behavior. Furthermore, our results indicated that belief in general CTs does not directly lead to a change in avoidance behavior; the relationship is instead mediated by belief in COVID-19 CTs. Perceived severity of COVID-19 was the best predictor for proactive health behavior, whereas actual vaccination behavior was best predicted by confidence in COVID-19 vaccines. These results were interpreted using a framework that combines health communication theories with the concepts of path dependency and spillover effects in conspiratorial thinking.

Frontiers in CommunicationVol. 10
University of Nebraska–Lincoln (US), Oregon State University (US), University of Georgia (US)
Openalex Percentile: Top 59%
Misinformation and Its Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.