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Cited by 264Vaccine Coverage and HesitancyMisinformation and Its ImpactsSARS-CoV-2 and COVID-19 Research

Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal

Open access
Pierri, Francesco, Perry, Brea L., DeVerna, Matthew R., Yang, Kai-Cheng, Flammini, Alessandro, Menczer, Filippo, Bryden, John·2022·Scientific Reports
DOI 10.1038/s41598-022-10070-wPMC PMC9043199
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Abstract

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.

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  • Misinformation spread on Twitter is linked to lower county-level vaccine uptake.

    “Other research suggests that misinformation spread on Twitter is linked to lower county-level vaccine uptake (Pierri et al. 2022), and people who got their news primarily from”

    From Social Media and Morality. (2024) · cites this paper as Pierri et al. 2022

    The Results report that Twitter misinformation was significantly correlated with county-level vaccine hesitancy. The Methods distinguish that outcome from vaccination uptake, which the study measured only at the state level; the cited paper therefore does not establish lower county-level uptake. (LLM, conf=0.91)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 27

  1. [2]Orenstein WA Ahmed R Simply put: Vaccination saves lives Proc. Natl. Acad. Sci. 2017 114 16 4031 4033 10.1073/pnas.1704507114 28396427 PMC5402432 · DOI
  2. [3]Kim JH Marks F Clemens JD Looking beyond COVID-19 vaccine phase 3 trials Nat. Med. 2021 10.1038/s41591-021-01230-y 33469205 · DOI
  3. [6]R. Aguas, R. M. Corder, J. G. King, G. Gonçalves, M. U. Ferreira, and M. G. M. Gomes, “Herd immunity thresholds for SARS-CoV-2 estimated from unfolding epidemics,” medRxiv, p. 2020.07.23.20160762, Nov. 2020.
  4. [7]Gomes, M. G. M. et al., “Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold,” Journal of theoretical biology 540 (2022). PMC8855661 35189135
  5. [8]D. Lu, A. Aleta, M. Ajelli, R. Pastor-Satorras, A. Vespignani, and Y. Moreno, “Data-driven estimate of SARS-CoV-2 herd immunity threshold in populations with individual contact pattern variations,” medRxiv, p. 2021.03.19.21253974, Mar. 2021, doi: 10.1101/2021.03.19.21253974. · DOI
  6. [12]Callaghan T Correlates and disparities of intention to vaccinate against COVID-19 Soc. Sci. Med. 1982 272 113638 10.1016/j.socscimed.2020.113638 PMC7834845 33414032 · DOI
  7. [13]Khubchandani J Sharma S Price JH Wiblishauser MJ Sharma M Webb FJ COVID-19 vaccination hesitancy in the United States: A rapid national assessment J. Community Health 2021 46 2 270 277 10.1007/s10900-020-00958-x 33389421 PMC7778842 · DOI
  8. [14]Broniatowski DA Weaponized health communication: Twitter bots and russian trolls amplify the vaccine debate Am. J. Public Health 2018 108 10 1378 1384 10.2105/AJPH.2018.304567 30138075 PMC6137759 · DOI · Paper page
  9. [16]Wilson SL Wiysonge C Social media and vaccine hesitancy BMJ Glob. Health 2020 5 10 e004206 10.1136/bmjgh-2020-004206 33097547 PMC7590343 · DOI
  10. [17]M. R. DeVerna et al., "CoVaxxy: A Collection of English-Language Twitter Posts About COVID-19 Vaccines” Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 992–999. Data available at: https://github.com/osome-iu/CoVaxxy (Accessed 21 Apr 2021).
  11. [18]P. Hotez et al., “Correcting COVID-19 vaccine misinformation: Lancet Commission on COVID-19 Vaccines and Therapeutics Task Force Members,” EClinicalMedicine, vol. 33, Mar. 2021, 10.1016/j.eclinm.2021.100780. PMC7935671 33718854 · DOI
  12. [23]D. Lazer et al., The COVID States Project #43: COVID-19 vaccine rates and attitudes among Americans. OSF Preprints, 2021. 10.31219/osf.io/rnw8z. · DOI
  13. [25]Lazer DMJ The science of fake news Science 2018 359 6380 1094 1096 10.1126/science.aao2998 29590025 · DOI · Paper page
  14. [26]Grinberg N Joseph K Friedland L Swire-Thompson B Lazer D Fake news on Twitter during the 2016 U.S. presidential election Science 2019 363 6425 374 378 10.1126/science.aau2706 30679368 · DOI · Paper page
  15. [27]Shao C Ciampaglia GL Varol O Yang K-C Flammini A Menczer F The spread of low-credibility content by social bots Nat. Commun. 2018 9 1 4787 10.1038/s41467-018-06930-7 30459415 PMC6246561 · DOI · Paper page
  16. [28]Bovet A Makse HA Influence of fake news in Twitter during the 2016 US presidential election Nat. Commun. 2019 10 1 7 10.1038/s41467-018-07761-2 30602729 PMC6315042 · DOI · Paper page
  17. [29]Dredze, M. et al. “Carmen: A Twitter Geolocation System with Applications to Public Health”. In Workshops at the twenty-seventh AAAI conference on artificial intelligence (2013).
  18. [30]Hamilton JD Time series analysis 1994 1 Princeton University Press
  19. [31]Granger CWJ Investigating causal relations by econometric models and cross-spectral methods Econometrica 1969 37 3 424 438 10.2307/1912791 · DOI · Paper page
  20. [33]Gastner MT Seguy V More P Fast flow-based algorithm for creating density-equalizing map projections Proc. Natl. Acad. Sci. 2018 115 10 E2156 E2164 10.1073/pnas.1712674115 29463721 PMC5877977 · DOI
  21. [36]Loney T Nagelkerke NJ The individualistic fallacy, ecological studies and instrumental variables: a causal interpretation Emerg. Themes Epidemiol. 2014 11 1 18 10.1186/1742-7622-11-18 25745504 PMC4350299 · DOI
  22. [37]Brunson EK The impact of social networks on parents’ vaccination decisions Pediatrics 2013 131 5 e1397 e1404 10.1542/peds.2012-2452 23589813 · DOI
  23. [38]Funk S Salathé M Jansen VAA Modelling the influence of human behaviour on the spread of infectious diseases: a review J. R. Soc. Interface 2010 7 50 1247 1256 10.1098/rsif.2010.0142 20504800 PMC2894894 · DOI
  24. [40]Shao C Anatomy of an online misinformation network PLoS ONE 2018 13 4 e0196087 10.1371/journal.pone.0196087 29702657 PMC5922526 · DOI
  25. [41]Larson HJ Broniatowski DA Volatility of vaccine confidence Science 2021 371 6536 1289 1289 10.1126/science.abi6488 33766861 · DOI
  26. [42]Yang K-C The COVID-19 Infodemic: Twitter versus Facebook Big Data Soc. 2021 8 1 20539517211013860 10.1177/20539517211013861 · DOI
  27. [43]Pierri F. et al., Reproducibility code for ‘Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal, https://github.com/osome-iu/CoVaxxy-Misinfo. Accessed 20 Apr 2021. PMC9043199 35474313

Cited by

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Authors
Pierri, F., Perry, B. L., DeVerna, M. R., Yang, K.-C., Flammini, A., Menczer, F., & Bryden, J.
Affiliations
Indiana University Bloomington (US)
Corresponding author
Pierri, Francesco
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2022-04-26
Volume
12
Issue
1
Pages
5966-5966
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-022-10070-w
PMC
PMC9043199
OpenAlex
W4224979504
Cited by
264
References
6
Citations by year
2026: 39 · 2025: 66 · 2024: 69 · 2023: 66 · 2022: 24
FWCI
43.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • European Research Council (101016233)
  • National Science Foundation (ACI-1548562)
Topics
  • Social Sciences › Health › Vaccine Coverage and Hesitancy
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
Keywords
COVID-19 vaccination, vaccine hesitancy, vaccine refusal, online misinformation, vaccine uptake, partisanship, Republican vote share, Granger causality, health misinformation, herd immunity
MeSH terms
COVID-19, COVID-19 Vaccines, Vaccination Hesitancy, Communication, Humans, Patient Acceptance of Health Care, Vaccination, Vaccines

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