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

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.

References 43

1. 1.

   “WHO Coronavirus (COVID-19) Dashboard,” 2021. https://covid19.who.int. Accessed Apr. 19, 2021.

   Not in our library

2. 2.

   Orenstein WA, Ahmed R. Simply put: Vaccination saves lives. Proc. Natl. Acad. Sci. 2017;114(16):4031-4033.

   Not in our library10.1073/pnas.1704507114

3. 3.

   Kim JH, Marks F, Clemens JD. Looking beyond COVID-19 vaccine phase 3 trials. Nat. Med. 2021.

   Not in our library10.1038/s41591-021-01230-y

4. 4.

   “KFF COVID-19 Vaccine Monitor Dashboard,” KFF, Feb. 26, 2021. https://www.kff.org/coronavirus-covid-19/dashboard/kff-covid-19-vaccine-monitor-dashboard/. Accessed Apr. 07, 2021.

   Not in our library

5. 5.

   C. Funk and A. Tyson, “Growing Share of Americans Say They Plan To Get a COVID-19 Vaccine – or Already Have,” Pew Research Center Science & Society, Mar. 05, 2021. https://www.pewresearch.org/science/2021/03/05/growing-share-of-americans-say-they-plan-to-get-a-covid-19-vaccine-or-already-have/. Accessed Apr. 14, 2021.

   Not in our library

6. 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.

   Not in our library

7. 7.
   Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold · 2022

   No notice
8. 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.

   Not in our library

9. 9.

   Centers for Disease Control and Prevention, “COVID Data Tracker,” Mar. 28, 2020. https://covid.cdc.gov/covid-data-tracker. Accessed Apr. 14, 2021.

   Not in our library

10. 10.

    Salathé M, Bonhoeffer S. The effect of opinion clustering on disease outbreaks. J. R. Soc. Interface. 2008;5(29):1505-1508.

    Not in our library10.1098/rsif.2008.0271

11. 11.

    MacDonald NE. Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015;33(34):4161-4164.

    Not in our library10.1016/j.vaccine.2015.04.036

12. 12.

    Callaghan T, et al. Correlates and disparities of intention to vaccinate against COVID-19. Soc. Sci. Med. 1982;272:113638.

    Not in our library10.1016/j.socscimed.2020.113638

13. 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.

    Not in our library10.1007/s10900-020-00958-x

14. 14.

    Broniatowski DA, et al. Weaponized health communication: Twitter bots and russian trolls amplify the vaccine debate. Am. J. Public Health. 2018;108(10):1378-1384.

    Not in our library10.2105/ajph.2018.304567

15. 15.

    Burki T. Vaccine misinformation and social media. Lancet Digit. Health. 2019;1(6):e258-e259.

    Not in our library10.1016/s2589-7500(19)30136-0

16. 16.

    Wilson SL, Wiysonge C. Social media and vaccine hesitancy. BMJ Glob. Health. 2020;5(10):e004206.

    Not in our library10.1136/bmjgh-2020-004206

17. 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).

    Not in our library

18. 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.

    Not in our library10.1016/j.eclinm.2021.100780

19. 19.

    Gallotti R, Valle F, Castaldo N, Sacco P, De Domenico M. Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics. Nat. Hum. Behav. 2020.

    Not in our library10.1038/s41562-020-00994-6

20. 20.

    Loomba S, de Figueiredo A, Piatek SJ, de Graaf K, Larson HJ. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat. Hum. Behav. 2020.

    Not in our library10.1038/s41562-021-01056-1

21. 21.

    U.S. Food & Drug Administration, “Pfizer-BioNTech COVID-19 Vaccine,” FDA, Apr. 2021, Accessed: Apr. 12, 2021. [Online]. https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/pfizer-biontech-covid-19-vaccine

    Not in our library

22. 22.

    C. Crist, “States Begin Opening COVID-19 Vaccines to All Adults,” WebMD, 2021. https://www.webmd.com/vaccines/covid-19-vaccine/news/20210324/states-begin-opening-covid-19-vaccines-to-all-adults. Accessed Apr. 12, 2021.

    Not in our library

23. 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.

    Not in our library

24. 24.

    D. C. Farrow, L. C. Brooks, A. Rumack, R. J. Tibshirani, and R. Rosenfeld, “Delphi Epidata API,” 2015. https://github.com/cmu-delphi/delphi-epidata. Accessed Apr. 12, 2021.

    Not in our library

25. 25.

    Lazer DMJ, et al. The science of fake news. Science. 2018;359(6380):1094-1096.

    Not in our library10.1126/science.aao2998

26. 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.

    Not in our library10.1126/science.aau2706

27. 27.
    The spread of low-credibility content by social bots · 2018

    No notice
28. 28.
    Influence of fake news in Twitter during the 2016 US presidential election · 2019

    No notice
29. 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).

    Not in our library

30. 30.

    Hamilton JD. Time series analysis. 1994.

    Not in our library

31. 31.

    Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica. 1969;37(3):424-438.

    Not in our library10.2307/1912791

32. 32.

    D. Nikolov, A. Flammini, and F. Menczer, “Right and left, partisanship predicts (asymmetric) vulnerability to misinformation,” Harv. Kennedy Sch. Misinformation Rev., vol. 1(7), Feb. 2021, Accessed: Apr. 06, 2021. [Online]. https://misinforeview.hks.harvard.edu/article/right-and-left-partisanship-predicts-asymmetric-vulnerability-to-misinformation/

    Not in our library

33. 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.

    Not in our library10.1073/pnas.1712674115

34. 34.

    M. R. Smith, “How a Kennedy built an anti-vaccine juggernaut amid COVID-19 | AP News,” 2021. https://apnews.com/article/how-rfk-jr-built-anti-vaccine-juggernaut-amid-covid-4997be1bcf591fe8b7f1f90d16c9321e. Accessed Feb. 14, 2022.

    Not in our library

35. 35.

    Center for Countering Digital Hate, “The Anti-Vaxx Playbook | Center for Countering Digital Hate,” CCDH, 2020. https://www.counterhate.com/playbook (accessed Feb. 14, 2022).

    Not in our library

36. 36.

    Loney T, Nagelkerke NJ. The individualistic fallacy, ecological studies and instrumental variables: a causal interpretation. Emerg. Themes Epidemiol. 2014;11(1):18.

    Not in our library10.1186/1742-7622-11-18

37. 37.

    Brunson EK. The impact of social networks on parents’ vaccination decisions. Pediatrics. 2013;131(5):e1397-e1404.

    Not in our library10.1542/peds.2012-2452

38. 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.

    Not in our library10.1098/rsif.2010.0142

39. 39.

    Broniatowski DA, et al. Twitter and Facebook posts about COVID-19 are less likely to spread misinformation compared to other health topics. PLoS ONE. 2022;17(1):e0261768.

    Not in our library10.1371/journal.pone.0261768

40. 40.

    Shao C, et al. Anatomy of an online misinformation network. PLoS ONE. 2018;13(4):e0196087.

    Not in our library10.1371/journal.pone.0196087

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.