COVID-19 myth-busting: an experimental study

Abstract

Background: COVID-19 misinformation is a danger to public health. A range of formats are used by health campaigns to correct beliefs but data on their effectiveness is limited. We aimed to identify A) whether three commonly used myth-busting formats are effective for correcting COVID-19 myths, immediately and after a delay, and B) which is the most effective.

Methods: We tested whether three common correction formats could reduce beliefs in COVID-19 myths: (i) question-answer, ii) fact-only, (ii) fact-myth. n = 2215 participants (n = 1291 after attrition), UK representative of age and gender, were randomly assigned to one of the three formats. n = 11 myths were acquired from fact-checker websites and piloted to ensure believability. Participants rated myth belief at baseline, were shown correction images (the intervention), and then rated myth beliefs immediately post-intervention and after a delay of at least 6 days. A partial replication, n = 2084 UK representative, was also completed with immediate myth rating only. Analysis used mixed models with participants and myths as random effects.

Results: Myth agreement ratings were significantly lower than baseline for all correction formats, both immediately and after the delay; all β's > 0.30, p's < .001. Thus, all formats were effective at lowering beliefs in COVID-19 misinformation. Correction formats only differed where baseline myth agreement was high, with question-answer and fact-myth more effective than fact-only immediately; β = 0.040, p = .022 (replication set: β = 0.053, p = .0075) and β = - 0.051, p = .0059 (replication set: β = - 0.061, p < .001), respectively. After the delay however, question-answer was more effective than fact-myth, β = 0.040, p =. 031.

Conclusion: Our results imply that COVID-19 myths can be effectively corrected using materials and formats typical of health campaigns. Campaign designers can use our results to choose between correction formats. When myth belief was high, question-answer format was more effective than a fact-only format immediately post-intervention, and after delay, more effective than fact-myth format.

Keywords: COVID-19; Infodemic; Misinformation; Myth busting; Myth correction.

Fig. 1

Example correction graphics. There were three correction format conditions: A Question-answer B Fact-only C Fact-myth. Each graphic had two boxes. The first contained the intervention material, the second the supporting explanation statement (and the answer, i.e., yes/no, in the case of question-answer)

Fig. 2

Means of myth agreement ratings (1 denotes low agreement, 6 denotes high agreement) with by-participant standard errors and violin distributions. Ratings were reduced at both timepoints 1 and 2 for all correction formats (question-answer, qa, fact-only, fo, fact-myth, fm) relative to baseline. At timepoint 2, myth agreement was higher than at timepoint 1, but stayed below baseline for all formats

Fig. 3

Main data set. Means of myth agreement (post-intervention) as a function of baseline agreement (pre-intervention), correction format and timepoint e.g., responses at timepoint 1 in the question-answer condition that were 2 at baseline (pre-intervention) had an average of 1.5 post-intervention. N’s indicate the number of responses in each data point e.g., there were 3505 responses that had baseline 2. No N’s are included for timepoint 2 because the same number of responses were used for timepoint 1 and timepoint 2. Dashed line shows equivalence between baseline and myth agreement (post-intervention) so that data below the line indicates correction. In both timepoints there was a strong positive correlation between baseline agreement and post-intervention agreement (post-intervention agreement was high when baseline agreement was high). Differences between correction formats were more apparent at higher levels of baseline agreement than at lower levels, hence interactions between baseline and correction format. At timepoint 1, no differences between correction formats were visible when baseline was low, but at higher levels fact-only was less effective at lowering agreement than question-answer or fact-myth (p = .022). At timepoint 2, again no differences were visible at low baselines, but fact-myth was less effective than question-answer when baseline was very high (p = .031)

Fig. 4

Replication data set. Means of myth agreement (post-intervention) as a function of baseline agreement (pre-intervention) and correction format. Data from replication set. N’s indicate the number of responses in each data point. Dashed line shows equivalence between baseline and myth agreement (post-intervention) so that data below the line indicates correction. Data pattern replicates main data set in that fact-only is less effective than other correction formats at higher baselines

Fig. 5

Means of myth agreement (post-intervention) as a function of baseline agreement (pre-intervention), correction format and timepoint. Data combined from complete main and replication data set. Dashed line shows equivalence between myth agreement (post-intervention) and baseline. There are interactions of correction format by baseline such that fact-only is less effective than other formats at higher baselines