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A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling

Nature Human Behaviour volume 7, pages 1462–1480 (2023)Cite this article

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Abstract

The proliferation of anti-vaccination arguments is a threat to the success of many immunization programmes. Effective rebuttal of contrarian arguments requires an approach that goes beyond addressing flaws in the arguments, by also considering the attitude roots—that is, the underlying psychological attributes driving a person’s belief—of opposition to vaccines. Here, through a pre-registered systematic literature review of 152 scientific articles and thematic analysis of anti-vaccination arguments, we developed a hierarchical taxonomy that relates common arguments and themes to 11 attitude roots that explain why an individual might express opposition to vaccination. We further validated our taxonomy on coronavirus disease 2019 anti-vaccination misinformation, through a combination of human coding and machine learning using natural language processing algorithms. Overall, the taxonomy serves as a theoretical framework to link expressed opposition of vaccines to their underlying psychological processes. This enables future work to develop targeted rebuttals and other interventions that address the underlying motives of anti-vaccination arguments.

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Fig. 1: PRISMA flow diagram and precedents.
Fig. 2: Frequencies of the attitude roots.
Fig. 3: Visualizations of attitude roots in Study 1.
Fig. 4: Visualizations of attitude roots in Study 2.
Fig. 5: Text classification experimental set-up.

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Data availability

All datasets used in this article are publicly available at https://osf.io/e4yp6/. The dataset used in Study 1 is accessible at https://osf.io/xu2cz (retrieved from Scopus, Web of Science, PubMed, PsycInfo, PsyArXiv, ArXiv and MedrXiv). The dataset used in Study 2 is accessible at https://osf.io/d839n (retrieved from https://covid19misinfo.org portal)

Code availability

All source code used in this article is publicly available at https://osf.io/e4yp6/.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 964728 (JITSUVAX). L.B. and I.G. were funded by the German Federal Ministry of Education and Research and by the Hessian Ministry of Science and the Arts (HMWK) within the projects ‘The Third Wave of Artificial Intelligence – 3AI’, hessian.AI, and within their joint support of the National Research Center for Applied Cybersecurity ATHENE. S.L. also acknowledges support from the Humboldt Foundation through a research award. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Faculty of Medicine, University of Coimbra, Coimbra, Portugal

    Angelo Fasce

  2. Institute for Planetary Health Behaviour, University of Erfurt, Erfurt, Germany

    Philipp Schmid

  3. Department of Implementation Research, Bernhard-Nocht-Institute for Tropical Medicine, Hamburg, Germany

    Philipp Schmid

  4. School of Psychological Science, University of Bristol, Bristol, UK

    Dawn L. Holford & Stephan Lewandowsky

  5. Department of Psychology, University of Essex, Colchester, UK

    Dawn L. Holford

  6. Ubiquitous Knowledge Processing Lab/Department of Computer Science and Hessian Center for AI (hessian.AI), Technical University of Darmstadt, Darmstadt, Germany

    Luke Bates & Iryna Gurevych

  7. School of Psychological Science, University of Western Australia, Perth, Western Australia, Australia

    Stephan Lewandowsky

  8. Department of Psychology, University of Potsdam, Potsdam, Germany

    Stephan Lewandowsky

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Contributions

A.F.: conceptualization, data curation, formal analysis, investigation, methodology, supervision, writing—original draft, and writing—review and editing. P.S.: conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing—original draft, and writing—review and editing. D.L.H.: conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing—original draft, and writing—review and editing. L.B.: conceptualization, formal analysis, investigation, methodology, visualization, writing—original draft, and writing—review and editing. I.G.: funding acquisition, supervision, and writing—review and editing. S.L.: conceptualization, formal analysis, funding acquisition, investigation, methodology, supervision, visualization, and writing—review and editing.

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Correspondence to Angelo Fasce.

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Supplementary Information

Supplementary Table 1: Classification of articles. Supplementary Table 2: Krippendorff ’s alphas. Supplementary Figs. 1–3: Word clouds. Supplementary Figs. 4 and 5: Frequency of attitude roots. Supplementary Table 3: Summary statistics of both datasets. Supplementary Figs. 6 and 7 and Supplementary Table 4: Confirmatory text modelling using LDA. Supplementary Table 5: Text classification on Study 2 fact checks. Supplementary Figs. 8 and 9, and links to the 3D gifs: Visualization of attitude roots.

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Fasce, A., Schmid, P., Holford, D.L. et al. A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling. Nat Hum Behav 7, 1462–1480 (2023). https://doi.org/10.1038/s41562-023-01644-3

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