ArticleBMC public health2022
What predicts people's belief in COVID-19 misinformation? A retrospective study using a nationwide online survey among adults residing in the United States.
Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development of a Conceptual Framework of Health Misinformation During the COVID-19 Pandemic: Systematic Review of Reviews.JMIR public health and surveillance · 2025Pooled it
- Digital health literacy and infodemic awareness among preschool teachers: skills, behaviors, and determinants.Frontiers in public health · 2026Article
- Susceptibility to digital health misinformation: a multi-level narrative review.Frontiers in public health · 2026Review
- Low Risk, High Stakes: Factors Associated with Low COVID-19 Risk Perception Among Black Chicago Residents.Journal of racial and ethnic health disparities · 2025Article
- Contested science communication: Representations of scientists and their science in newspaper articles and the associated comment sections.Public understanding of science (Bristol, England) · 2025Article
- Trust in doctors, social support, and belief in COVID-19 misinformation.BMC public health · 2025Article
- Living With Long COVID: Everyday Experiences, Health Information Barriers and Patients' Quality of Life.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Media portrayals and social stigma surrounding COVID-19 survivors: a content analysis of Chinese media coverage.BMC public health · 2025Article
- A Pandemic of Misinformation: Understanding Influences on Beliefs in Health and Conspiracy Myths.Journal of general internal medicine · 2025Article
- Article
- Belief in misinformation and acceptance of COVID-19 vaccine boosters: A survey analysis.PEC innovation · 2024Article
- A Comprehensive Analysis of COVID-19 Misinformation, Public Health Impacts, and Communication Strategies: Scoping Review.Journal of medical Internet research · 2024Article
- Prevalence of Health Misinformation on Social Media-Challenges and Mitigation Before, During, and Beyond the COVID-19 Pandemic: Scoping Literature Review.Journal of medical Internet research · 2024Article
- Development of a User-friendly Health Promotion Website to Spread Evidence-based Information in Italy.Journal of preventive medicine and hygiene · 2024Article
- Survival analysis of the duration of rumors during the COVID-19 pandemic.BMC public health · 2024Article
- Vaccine Hesitancy and Susceptibility to SARS-CoV-2 Misinformation in Japanese Youth: The Contribution of Personality Traits and National Identity.International journal of environmental research and public health · 2023Article
- Information Consumption, Trust Dynamics and COVID-19 Vaccine Hesitancy among Older Adults: Implications for Health Messaging.Vaccines · 2023Article
- Patient-Provider Health Communication Strategies: Enhancing HPV Vaccine Uptake among Adolescents of Color.Healthcare (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTackling infodemics with flooding misinformation is key to managing the COVID-19 pandemic. Yet only a few studies have attempted to understand the characteristics of the people who believe in misinformation.
methodsData was used from an online survey that was administered in April 2020 to 6518 English-speaking adult participants in the United States. We created binary variables to represent four misinformation categories related to COVID-19: general COVID-19-related, vaccine/anti-vaccine, COVID-19 as an act of bioterrorism, and mode of transmission. Using binary logistic regression and the LASSO regularization, we then identified the important predictors of belief in each type of misinformation. Nested vector bootstrapping approach was used to estimate the standard error of the LASSO coefficients.
resultsAbout 30% of our sample reported believing in at least one type of COVID-19-related misinformation. Belief in one type of misinformation was not strongly associated with belief in other types. We also identified 58 demographic and socioeconomic factors that predicted people's susceptibility to at least one type of COVID-19 misinformation. Different groups, characterized by distinct sets of predictors, were susceptible to different types of misinformation. There were 25 predictors for general COVID-19 misinformation, 42 for COVID-19 vaccine, 36 for COVID-19 as an act of bioterrorism, and 27 for mode of COVID-transmission.
conclusionOur findings confirm the existence of groups with unique characteristics that believe in different types of COVID-19 misinformation. Findings are readily applicable by policymakers to inform careful targeting of misinformation mitigation strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.