Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal
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Abstract (arXiv)
Widespread uptake of vaccines is necessary to achieve herd immunity. However, uptake rates have varied across U.S. states during the first six months of the COVID-19 vaccination program. Misbeliefs may play an important role in vaccine hesitancy, and there is a need to understand relationships between misinformation, beliefs, behaviors, and health outcomes. Here we investigate the extent to which COVID-19 vaccination rates and vaccine hesitancy are associated with levels of online misinformation about vaccines. We also look for evidence of directionality from online misinformation to vaccine hesitancy. We find a negative relationship between misinformation and vaccination uptake rates. Online misinformation is also correlated with vaccine hesitancy rates taken from survey data. Associations between vaccine outcomes and misinformation remain significant when accounting for political as well as demographic and socioeconomic factors. While vaccine hesitancy is strongly associated with Republican vote share, we observe that the effect of online misinformation on hesitancy is strongest across Democratic rather than Republican counties. Granger causality analysis shows evidence for a directional relationship from online misinformation to vaccine hesitancy. Our results support a need for interventions that address misbeliefs, allowing individuals to make better-informed health decisions.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2021.
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Citing papers per year: 2022: 2; 2023: 5; 2024: 4; 2025: 2.
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- 2023Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformationcited by 39
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- 2023Demystifying Misconceptions in Social Bots Researchcited by 7
- 2024Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Mediacited by 7
- 2022Multimodal and Explainable Internet Meme Classificationcited by 5
- 2023Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logscited by 3
- 2024SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Taskscited by 2
- 2024Unmasking Social Bots: How Confident Are We?cited by 2
- 2025The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussionscited by 2
- 2024A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Healthcited by 1
- 2025The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussions29 Oct 2025
- 2024A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health4 Nov 2024
- 2024Unmasking Social Bots: How Confident Are We?18 Jul 2024
- 2024SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Tasks17 May 2024
- 2024Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media6 Mar 2024
- 2023Factuality Challenges in the Era of Large Language Models8 Oct 2023
- 2023Fact-checking information from large language models can decrease headline discernment21 Aug 2023
- 2023Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs12 Jun 2023
- 2023Demystifying Misconceptions in Social Bots Research30 Mar 2023
- 2023Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation11 Mar 2023
- 2022Multimodal and Explainable Internet Meme Classification11 Dec 2022
- 2022Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls17 Oct 2022