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

Angelo Fasce*, Philipp Schmid, Dawn Holford, Luke Bates, Iryna Gurevych, Stephan Lewandowsky

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

42 Citations (Scopus)
304 Downloads (Pure)

Abstract

The proliferation of anti-vaccination arguments is a threat to the success of many immunisation 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. Through a preregistered 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 COVID-19 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.
Original languageEnglish
Pages (from-to)1462-1480
Number of pages19
JournalNature Human Behaviour
Volume7
Issue number9
DOIs
Publication statusPublished - 17 Jul 2023

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature Limited.

Research Groups and Themes

  • TeDCog
  • Cognitive Science

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