Mode and Frequency of Covid-19 Information Updates, Political Values, and Future Covid-19 Vaccine Attitudes
Open accessAbstract
The effect of social media consumption on perceptions of the seriousness of the Covid-19 pandemic, attitudes to public health requirements, and intentions towards a future Covid-19 vaccine are of live public health interest. There are also public health and security concerns that the pandemic has been accompanied and arguably further amplified by an ‘infodemic’ spreading misinformation. Tests of the effect of social media consumption on future Covid-19 vaccine intentions using population samples have been relatively few to date. This study contributes to the evidence base by examining social media consumption and vaccine intentions using British and US population samples.Methods: Data were gathered on 1,663 GB adults and 1,198 US adults from an online panel on attitudes towards a future vaccine alongside self-reported social and legacy broadcast and print media consumption. Ordered and binomial logit models were used to assess reported intentions regarding a future Covid-19 vaccine, testing the effects of media consumption type. Respondents were categorised in terms of their media consumption using a fourfold typology, as less frequent social, less frequent legacy media consumers (low-low); high social, low legacy media consumers (high-low); low social, high legacy (low-high); and high social, high legacy (high-high).Results: In the British sample, regression results indicate that those who receive Covid-19 updates more frequently via legacy media (low-high), and those being updated more than daily via both online and legacy media consumers, tend to provide significantly less Covid-19 vaccine-hesitant responses than low-low consumers. There is no significant difference between high social, low legacy media consumers and low-low consumers. In the US sample, membership of the low-high group is associated with lower Covid-19 vaccine hesitancy compared with low-low consumers. However, respondents consuming both social and legacy media several times daily exhibit similar vaccine intentions on average to those consuming social media daily and legacy media less often, providing a contrast with the UK sample. We also identify differences in Covid-19 vaccine intentions relating to demographics and political values.Conclusions: Differences in vaccine attentions are associated with the extent and balance of consumption of news relating to Covid-19 and its source. Political values and ethnic identity also appear to structure attitudes to a future Covid-19 vaccine.
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Estimates of acceptance of a COVID-19 vaccine in June 2020 suggest that 38% of the public surveyed in the UK and 34.2% of the public in the USA would accept a COVID-19 vaccine.
“Estimates of acceptance of a COVID-19 vaccine in June 2020 suggest that 38% of the public surveyed in the UK and 34.2% of the public in the USA would accept a COVID-19 vaccine (a further 31% and 25% were, respectively, unsure that they would accept vaccination against COVID-19) 25 .”
From Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA (2021) · cites this paper as [25]
Table 1 (What this study adds) reports that 38.0% of UK respondents and 34.2% of US respondents said they would definitely get vaccinated. But the further 31.0% and 25.0% said they probably would get vaccinated, not that they were unsure; the Methods section reports 69% and 59%, respectively, as probably or definitely willing. (LLM, conf=0.90)
Experimental — accuracy not yet measuredHigher intent to accept a COVID-19 vaccine in the UK than the USA has been reported recently.
“Higher intent to accept a COVID-19 vaccine in the UK than the USA has been reported recently 25 .”
From Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA (2021) · cites this paper as [25]
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Cites
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587 · see citing works
The Theory of Planned Behavior
Ajzen, I. (2012) · SAGE Publications Ltd eBooks · doi:10.4135/9781446249215.n22 · Cited by 5,514 · see citing works
High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2
Sanche, S., Lin, Y. T., Xu, C., Romero-Severson, E., Hengartner, N., & Ke, R. (2020) · Emerging Infectious Diseases · doi:10.3201/eid2607.200282 · Cited by 1,619 · see citing works
Schmid, P., Rauber, D., Betsch, C., Lidolt, G., & Denker, M.-L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170550 · Cited by 1,233 · see citing works
Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate
Broniatowski, D. A., Jamison, A. M., Qi, S., AlKulaib, L., Chen, T., Benton, A., Quinn, S. C., & Dredze, M. (2018) · American Journal of Public Health · doi:10.2105/ajph.2018.304567 · Cited by 1,149 · see citing works
Kata, A. (2012) · Vaccine · doi:10.1016/j.vaccine.2011.11.112 · Cited by 1,094 · see citing works
Allington, D., Duffy, B., Wessely, S., Dhavan, N., & Rubin, J. (2021) · Psychological Medicine · doi:10.1017/s003329172000224x · Cited by 904 · see citing works
Self-citationDetoc, M., Bruel, S., Frappé, P., Tardy, B., Botelho-Nevers, É., & Gagneux‐Brunon, A. (2020) · Vaccine · doi:10.1016/j.vaccine.2020.09.041 · Cited by 655 · see citing works
Planning for a COVID-19 Vaccination Program
DeRoo, S. S., Pudalov, N., & Fu, L. Y. (2020) · JAMA · doi:10.1001/jama.2020.8711 · Cited by 602 · see citing works
Tuskegee and the Health of Black Men*
Alsan, M., & Wanamaker, M. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx029 · Cited by 464 · see citing works
Populist politics and vaccine hesitancy in Western Europe: an analysis of national-level data
Kennedy, J. (2019) · European Journal of Public Health · doi:10.1093/eurpub/ckz004 · Cited by 334 · see citing works
Assessing COVID-19 vaccine literacy: a preliminary online survey
Biasio, L. R., Bonaccorsi, G., Lorini, C., & Pecorelli, S. L. (2020) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2020.1829315 · Cited by 281 · see citing works
Hornsey, M. J., Finlayson, M., Chatwood, G., & Begeny, C. T. (2020) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2019.103947 · Cited by 273 · see citing works
Dunn, A. G., Surian, D., Leask, J., Dey, A., Mandl, K. D., & Coiera, E. W. (2017) · Vaccine · doi:10.1016/j.vaccine.2017.04.060 · Cited by 264 · see citing works
Dunn, A., Leask, J., Zhou, X.-J., Mandl, K., & Coiera, E. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4343 · Cited by 248 · see citing works
COVID-19 pandemic, infodemic and the role of eHealth literacy
Chong, Y. Y., Cheng, H. Y., Chan, H. Y. L., Chien, W. T., & Wong, S. Y. S. (2020) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2020.103644 · Cited by 203 · see citing works
Flattening the Curve of COVID-19 Vaccine Rejection—A Global Overview
Feleszko, W., Lewulis, P., Czarnecki, A., & Waszkiewicz, P. (2020) · Vaccines · doi:10.3390/vaccines9010044 · Cited by 170 · see citing works
Garrett, R. K., Weeks, B. E., & Neo, R. L. (2016) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12164 · Cited by 167 · see citing works
Addressing parents’ vaccine concerns: a randomized trial of a social media intervention
Daley, M. F., Narwaney, K. J., Shoup, J. A., Wagner, N. M., & Glanz, J. M. (2018) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2018.04.010 · Cited by 164 · see citing works
Web-based Social Media Intervention to Increase Vaccine Acceptance: A Randomized Controlled Trial
Glanz, J. M., Wagner, N. M., Narwaney, K. J., Kraus, C. R., Shoup, J. A., Xu, S., O’Leary, S. T., Omer, S. B., Gleason, K. S., & Daley, M. F. (2017) · PEDIATRICS · doi:10.1542/peds.2017-1117 · Cited by 161 · see citing works
Jylhä, K. M., & Hellmer, K. (2020) · Analyses of Social Issues and Public Policy · doi:10.1111/asap.12203 · Cited by 124 · see citing works
Parent psychology and the decision to delay childhood vaccination.
Callaghan, T., Motta, M. P., Sylvester, S. M., Trujillo, K. L., & Blackburn, C. C. (2019) · Social Science & Medicine (1967) · doi:10.1016/j.socscimed.2019.112407 · Cited by 119 · see citing works
Officials gird for a war on vaccine misinformation
Cornwall, W. (2020) · Science · doi:10.1126/science.369.6499.14 · Cited by 119 · see citing works
Social media use and influenza vaccine uptake among White and African American adults
Ahmed, N., Quinn, S. C., Hancock, G. R., Freimuth, V. S., & Jamison, A. (2018) · Vaccine · doi:10.1016/j.vaccine.2018.10.049 · Cited by 109 · see citing works
Perez, S., Tatar, O., Shapiro, G., Dubé, È., Ogilvie, G., Guichon, J., Gilca, V., & Rosberger, Z. (2016) · BMC Public Health · doi:10.1186/s12889-016-3828-9 · Cited by 40 · see citing works
Lewandowsky, S., Woike, J. K., & Oberauer, K. (2020) · Journal of Cognition · doi:10.5334/joc.99 · Cited by 36 · see citing works
Assessing COVID-19 Vaccine Literacy: A Preliminary Online SurveyPreprint
Biasio, L. R., Bonaccorsi, G., Lorini, C., et al. (2020) · Preprints.org · doi:10.20944/preprints202007.0295.v1 · Cited by 18 · see citing works
Murphy, J., Vallières, F., Bentall, R. P., Shevlin, M. E., McBride, O., Hartman, T. K., McKay, R., Bennett, K. M., Mason, L., Miller, J. G., et al. (2020) · doi:10.31234/osf.io/pev2b · Cited by 16 · see citing works
Tuskegee and the Health of Black Men
Alsan, M. M., & Wanamaker, M. (2016) · National Bureau of Economic Research · doi:10.3386/w22323 · Cited by 15 · see citing works
The Effects of an Informational Intervention on Attention to Anti-Vaccination Content on YouTube
Kim, S., Yalcin, O. F., Bestvater, S. E., Munger, K., Monroe, B. L., & Desmarais, B. A. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7364 · Cited by 14 · see citing works
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Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01056-1 · Cited by 1,800 · see citing works
- Authors
- McAndrew, S., & Allington, D.
- Affiliations
- University of Bristol (GB)
- University College London (GB)
- Publisher
- Center for Open Science
- Published
- 2020-08-14
- OpenAlex
- W3160450913
- Cited by
- 19
- References
- 30
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 12 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Topics
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- COVID-19 vaccine hesitancy, vaccination intention, social media use, traditional media, infodemic, misinformation, political values, ethnic identity, public health attitudes
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