Why Do People Believe in Vaccine Misinformation? The Roles of Perceived Familiarity and Evidence Type.

Health Commun

Hubbard School of Journalism and Mass Communication, University of Minnesota.

Published: November 2024


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Article Abstract

The proliferation of health misinformation poses a significant threat to public health, making it increasingly important to understand why misinformation is accepted. The illusory truth effect, which refers to the increased believability of a message due to repeated exposure, has been widely studied. However, there is limited research on this effect in the context of COVID-19 vaccine misinformation. This paper aims to examine the role of perceived familiarity with COVID-19 vaccine misinformation on various message perceptions, including perceived accuracy, agreement, perceived message effectiveness, and determinants of vaccination, including vaccine attitude and vaccination intention. Furthermore, it explores the impact of misinformation evidence (statistical vs. narrative) on the magnitude of the effects of perceived familiarity. To investigate these factors, a between-subjects experimental study was conducted, employing a 2 (Familiarity: strong vs. weak) × 3 (Evidence type: statistical, narrative, and both evidence) + 1 (Control: a message about drinking water) design. The results revealed that perceived familiarity with COVID-19 vaccine misinformation significantly predicted perceived accuracy, which was found to be negatively correlated with vaccine attitudes and vaccination intentions. Moreover, statistical evidence presented in misinformation was perceived as more persuasive in perceived message effectiveness, compared to narrative and mixed evidence. Interestingly, the effects of perceived familiarity were not contingent on the type of evidence used in COVID-19 vaccine misinformation. These findings emphasize the importance of avoiding the repetition of misinformation, reducing the processing fluency associated with misinformation correction, and educating individuals on how to critically evaluate statistical evidence when encountering (mis)information.

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http://dx.doi.org/10.1080/10410236.2024.2328455DOI Listing

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