Abstract
PurposeOne of the most prolific areas of misinformation research is examining corrective strategies in messaging. The main purposes of the current study are to examine the effects of (1) partisan media (2) credibility perceptions and emotional reactions and (3) theory driven corrective messages on people's misperceptions about COVID-19 mask wearing behaviors.Design/methodology/approachThe authors used a randomized experimental design to test the hypotheses. The data were collected via the survey firm Lucid. The number of participants was 485. The study was conducted using Qualtrics after the research project was exempt by the Institutional Research Board of a large University in the US. The authors conducted an online experiment with four conditions, narrative versus statistics and individual versus collective. The manipulation messages were constructed as screenshots from Facebook.FindingsThe findings of this study show that higher exposure to liberal media was associated with lower misperceptions, whereas higher credibility perceptions of and positive reactions toward the misinformation post and negative emotions toward the correction comment were associated with higher misperceptions. Moreover, the findings showed that participants in the narrative and collective-frame condition had the lowest misperceptions.Originality/valueThe authors tested theory driven misinformation corrective messages to understand the impact of these messages and multiple related variables on misperceptions about COVID-19 mask wearing. This study contributes to the existing misinformation correction literature by investigating the explanatory power of the two well-established media effects theories on misinformation correction messaging and by identifying essential individual characteristics that should be considered when evaluating how misperceptions about the COVID-19 crisis works and gets reduced.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0600
Subject
Library and Information Sciences,Computer Science Applications,Information Systems
Cited by
12 articles.
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