The anti-vaccination infodemic on social media: A behavioral analysis
Open accessPublished version of a preprint
Abstract
Vaccinations are without doubt one of the greatest achievements of modern medicine, and there is hope that they can constitute a solution to halt the ongoing COVID-19 pandemic. However, the anti-vaccination movement is currently on the rise, spreading online misinformation about vaccine safety and causing a worrying reduction in vaccination rates worldwide. In this historical time, it is imperative to understand the reasons of vaccine hesitancy, and to find effective strategies to dismantle the rhetoric of anti-vaccination supporters. For this reason, we analyzed the behavior of anti-vaccination supporters on the platform Twitter. Here we identify that anti-vaccination supporters, in comparison with pro-vaccination supporters, share conspiracy theories and make use of emotional language. We demonstrate that anti-vaccination supporters are more engaged in discussions on Twitter and share their contents from a pull of strong influencers. We show that the movement’s success relies on a strong sense of community, based on the contents produced by a small fraction of profiles, with the community at large serving as a sounding board for anti-vaccination discourse to circulate online. Our data demonstrate that Donald Trump, before his profile was suspended, was the main driver of vaccine misinformation on Twitter. Based on these results, we welcome policies that aim at halting the circulation of false information about vaccines by targeting the anti-vaccination community on Twitter. Based on our data, we also propose solutions to improve the communication strategy of health organizations and build a community of engaged influencers that support the dissemination of scientific insights, including issues related to vaccines and their safety.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Global evidence suggests vaccine hesitancy is highest among people with lower formal education and lower incomes.
“Global evidence suggests that vaccine hesitancy is highest among people with lower levels of formal education and with lower incomes”
From The Lancet Commission on lessons for the future from the COVID-19 pandemic (2022) · cites this paper as [261]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767 · see citing works
Larson, H. J., Jarrett, C., Eckersberger, E., Smith, D. M. D., & Paterson, P. (2014) · Vaccine · doi:10.1016/j.vaccine.2014.01.081 · Cited by 2,254 · see citing works
A postmodern Pandora's box: anti-vaccination misinformation on the internet
Kata, A. (2010) · Vaccine · doi:10.1016/j.vaccine.2009.12.022 · Cited by 1,208 · see citing works
Kata, A. (2012) · Vaccine · doi:10.1016/j.vaccine.2011.11.112 · Cited by 1,094 · see citing works
Dead and Alive: Beliefs in Contradictory Conspiracy Theories
Wood, M. J., Douglas, K. M., & Sutton, R. M. (2012) · Social Psychological and Personality Science · doi:10.1177/1948550611434786 · Cited by 660 · see citing works
Political Extremism Predicts Belief in Conspiracy Theories
van Prooijen, J.-W., Krouwel, A. P. M., & Pollet, T. V. (2015) · Social Psychological and Personality Science · doi:10.1177/1948550614567356 · Cited by 626 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Lancet Commission on lessons for the future from the COVID-19 pandemic
Sachs, J. D., Karim, S. S. A., Aknin, L., Allen, J., Brosbøl, K., Colombo, F., Barron, G. C., Espinosa, M. F., Gaspar, V., Gaviria, A., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01585-9 · Cited by 842 · see citing works
A systematic review of worldwide causal and correlational evidence on digital media and democracy
Lorenz-Spreen, P., Oswald, L., Lewandowsky, S., & Hertwig, R. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01460-1 · Cited by 348 · see citing works
Zuboff, S. (2022) · Organization Theory · doi:10.1177/26317877221129290 · Cited by 209 · see citing works
- Authors
- Germani, F., & Biller-Andorno, N.
- Affiliations
- University of Zurich (CH)
- Corresponding author
- Germani, Federico
- ORCID
- Germani, Federico
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-03-03
- Volume
- 16
- Issue
- 3
- Pages
- e0247642
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7928468
- OpenAlex
- W3134443160
- Cited by
- 316
- References
- 7
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 22 · 2023: 75 · 2022: 124 · 2021: 63 · 2020: 1
- FWCI
- 98.40
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2022-12-22)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Swiss National Science Foundation (NCCR Molecular Systems Engineering)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- anti-vaccine movement, vaccine hesitancy, vaccine misinformation, infodemic, conspiracy theories, emotional expression, Twitter, online communities, influencers, Donald Trump, COVID-19 vaccination, health communication, misinformation countermeasures
- MeSH terms
- Behavior Rating Scale, Anti-Vaccination Movement, COVID-19, SARS-CoV-2, Communication, Humans, Public Health, Vaccination, Vaccines, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
