Cited by 272Misinformation and Its ImpactsAcademic Publishing and Open AccessCommunication and COVID-19 Impact

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index; 5 of the 51 sources its claims cite were checked for support.

Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation

Open access
Pennycook, Gordon, Rand, David G.·2022·Nature Communications

Abstract

Interventions that shift users attention toward the concept of accuracy represent a promising approach for reducing misinformation sharing online. We assess the replicability and generalizability of this accuracy prompt effect by meta-analyzing 20 experiments (with a total N = 26,863) completed by our group between 2017 and 2020. This internal meta-analysis includes all relevant studies regardless of outcome and uses identical analyses across all studies. Overall, accuracy prompts increased the quality of news that people share (sharing discernment) relative to control, primarily by reducing sharing intentions for false headlines by 10% relative to control in these studies. The magnitude of the effect did not significantly differ by content of headlines (politics compared with COVID-19 related news) and did not significantly decay over successive trials. The effect was not robustly moderated by gender, race, political ideology, education, or value explicitly placed on accuracy, but was significantly larger for older, more reflective, and more attentive participants. This internal meta-analysis demonstrates the replicability and generalizability of the accuracy prompt effect on sharing discernment.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This internal meta-analysis of 20 experiments (total N = 26,863) finds that accuracy prompts reliably increase sharing discernment, mainly by reducing intentions to share false news by about 10% relative to control. The authors conclude the effect is replicable and generalizable across prompt types, topics, and samples, though effects are modest and mostly tested in U.S. online survey experiments by one research group.

Key findings

  • Across all 20 experiments, accuracy prompts significantly increased sharing discernment (interaction between headline veracity and treatment: b = 0.038, z = 7.102, p < 0.001), a 71.7% increase over baseline control discernment. This was driven by a significant decrease in sharing intentions for false news (b = −0.034, z = 7.851, p < 0.001), a 10% decrease relative to control, with no significant effect on true news (b = 0.006, z = 1.44, p = 0.150).
  • The most-tested Evaluation prompt (k = 14) significantly increased discernment (b = 0.034, p < 0.001), a 59.6% increase over baseline, and reduced false-news sharing by 8.2%. Combined Evaluation-plus-additional treatments were roughly twice as effective as Evaluation alone (discernment b = 0.054, p = 0.006; false-news sharing decrease 16.5%), and non-Evaluation treatments also increased discernment (b = 0.039, p < 0.001; false-news sharing decrease 11.0%).
  • Effects did not significantly differ for political versus COVID-19 headlines (b = −0.017, p = 0.244) and did not significantly decay over successive trials (three-way interaction with trial number: b = −0.001, p = 0.062; excluding first four trials: b = −0.000, p = 0.770).
  • Abstract reports no robust moderation by gender, race, political ideology, education, or value explicitly placed on accuracy, but effects were significantly larger for older, more reflective, and more attentive participants. At the item level, the treatment effect was strongly correlated with a headline’s out-of-sample perceived accuracy (meta-analytic r = 0.773, z = 19.417, p < 0.001; pooled r(355) = 0.663, p < 0.001).

Methods

  • Design: internal meta-analysis of 20 experiments conducted by the authors’ group between 2017 and 2020, including all qualifying studies regardless of outcome and using identical analyses across studies.
  • Data/sample: U.S. online survey participants (N = 26,863) recruited from Amazon Mechanical Turk, Lucid, or YouGov; participants indicated sharing intentions for true and false headlines about politics and/or COVID-19 after randomization to control or an accuracy prompt.
  • Analysis: rating-level linear regressions with robust standard errors clustered on participant and headline, with sharing intentions rescaled to [0,1]; study-level coefficients were combined using random effects meta-analysis. Main specification included headline veracity, treatment condition, and their interaction.

Limitations

  • Stated: the internal meta-analysis does not take external studies into account; only one external replication meeting inclusion criteria was included, and future work should examine studies from a wider range of research groups, across cultures, and in more field experiments. The authors also note the effects are modest in size, attention was measured post-treatment in the attentiveness analyses, and power for heterogeneity analyses across studies was comparatively low.
  • Stated: MTurk samples are not nationally representative and can produce misleading individual-difference results, particularly related to politics; the authors privilege Lucid/YouGov results for individual-level moderation.
  • Evident from the text: all included experiments came from the authors’ own group and used U.S. samples; the meta-analysis itself was not pre-registered and no protocol was prepared; Table 3 notes no adjustments were made for multiple comparisons.

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Integrity signals

Registry

Crossref · 10.1038/s41467-022-30073-5

Cites

78 of 78 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Accuracy prompts significantly increase sharing discernment. Meta-analytic estimate (via random effects meta-analysis) of the effect of accuracy prompts on sharing discernment across the 20 experiments analyzed in this paper. The coefficient on the interaction between condition and headline veracity and 95% confidence interval are shown for each study, and the meta-analytic estimate is shown with the red dotted line and blue diamond (positive values indicate that the treatment increased sharing discernment). We find significant heterogeneity in effect size across studies, Cochran’s Q test, Q(19) = 88.53, p < 0.001, I2 = 78.5% (k = 20 independent studies).
Fig. 1 Accuracy prompts significantly increase sharing discernment. Meta-analytic estimate (via random effects meta-analysis) of the effect of accuracy prompts on sharing discernment across the 20 experiments analyzed in this paper. The coefficient on the interaction between condition and headline veracity and 95% confidence interval are shown for each study, and the meta-analytic estimate is shown with the red dotted line and blue diamond (positive values indicate that the treatment increased sharing discernment). We find significant heterogeneity in effect size across studies, Cochran’s Q test, Q(19) = 88.53, p < 0.001, I2 = 78.5% (k = 20 independent studies).CC BY · PubMed Central
Fig. 2 Accuracy prompts significantly decrease sharing intentions for false news. Meta-analytic estimate (via random effects meta-analysis) of the effect of accuracy prompts on sharing of false news across the 20 experiments analyzed in this paper. The coefficient on the condition dummy (which captures the effect of the treatment on sharing of false headlines) and 95% confidence interval are shown for each study, and the meta-analytic estimate is shown with the red dotted line and blue diamond. We find no significant heterogeneity in effect size across studies, Cochran’s Q test, Q(19) = 23.33, p = 0.223, I2 = 18.5% (k = 20 independent studies).
Fig. 2 Accuracy prompts significantly decrease sharing intentions for false news. Meta-analytic estimate (via random effects meta-analysis) of the effect of accuracy prompts on sharing of false news across the 20 experiments analyzed in this paper. The coefficient on the condition dummy (which captures the effect of the treatment on sharing of false headlines) and 95% confidence interval are shown for each study, and the meta-analytic estimate is shown with the red dotted line and blue diamond. We find no significant heterogeneity in effect size across studies, Cochran’s Q test, Q(19) = 23.33, p = 0.223, I2 = 18.5% (k = 20 independent studies).CC BY · PubMed Central
Fig. 3 Accuracy prompts do not significantly affect sharing intentions for true news. Meta-analytic estimate (via random effects meta-analysis) of the effect of accuracy prompts on sharing of true news across the 20 experiments analyzed in this paper. The coefficient on the condition dummy when analyzing true headlines and 95% confidence interval are shown for each study, and the meta-analytic estimate is shown with the red dotted line and blue diamond. We find no significant heterogeneity in effect size across studies, Cochran’s Q test, Q(19) = 22.42, p = 0.264, I2 = 15.3% (k = 20 independent studies).
Fig. 3 Accuracy prompts do not significantly affect sharing intentions for true news. Meta-analytic estimate (via random effects meta-analysis) of the effect of accuracy prompts on sharing of true news across the 20 experiments analyzed in this paper. The coefficient on the condition dummy when analyzing true headlines and 95% confidence interval are shown for each study, and the meta-analytic estimate is shown with the red dotted line and blue diamond. We find no significant heterogeneity in effect size across studies, Cochran’s Q test, Q(19) = 22.42, p = 0.264, I2 = 15.3% (k = 20 independent studies).CC BY · PubMed Central
Fig. 4 The accuracy prompt effect on sharing of a given headline is strongly correlated with that headline’s perceived accuracy. Meta-analytic estimate (via random effects meta-analysis) of the item-level correlation between the accuracy prompt effect on sharing and the headline’s out-of-sample perceived accuracy rating. The correlation coefficient and 95% confidence interval are shown for each study, and the meta-analytic estimate is shown with the red dotted line and blue diamond. We find no significant heterogeneity in effect size across studies, Cochran’s Q test, Q(14) = 18.99, p = 0.165 (k = 15 independent studies).
Fig. 4 The accuracy prompt effect on sharing of a given headline is strongly correlated with that headline’s perceived accuracy. Meta-analytic estimate (via random effects meta-analysis) of the item-level correlation between the accuracy prompt effect on sharing and the headline’s out-of-sample perceived accuracy rating. The correlation coefficient and 95% confidence interval are shown for each study, and the meta-analytic estimate is shown with the red dotted line and blue diamond. We find no significant heterogeneity in effect size across studies, Cochran’s Q test, Q(14) = 18.99, p = 0.165 (k = 15 independent studies).CC BY · PubMed Central
Fig. 5 Accuracy prompts reduce sharing to the extent that headlines are perceived as inaccurate. For each of the 357 headlines in the 15 experiments where out-of-sample accuracy ratings were available, the accuracy prompt effect (sharing when treated minus sharing in control) is plotted against the headline’s perceived accuracy. False headlines are shown in green, true headlines in orange. Dot sizes are proportional to sample size. Best-fit line and 95% confidence interval are shown.
Fig. 5 Accuracy prompts reduce sharing to the extent that headlines are perceived as inaccurate. For each of the 357 headlines in the 15 experiments where out-of-sample accuracy ratings were available, the accuracy prompt effect (sharing when treated minus sharing in control) is plotted against the headline’s perceived accuracy. False headlines are shown in green, true headlines in orange. Dot sizes are proportional to sample size. Best-fit line and 95% confidence interval are shown.CC BY · PubMed Central
Fig. 6 Accuracy prompts increase sharing discernment across the ideological spectrum. Shown is sharing discernment in the control (red triangles) versus treatment (blue circles) as a function of liberal versus conservative ideology. The model fits for discernment in control and treatment, based on meta-analytic estimates of model coefficients, are shown with solid lines. The meta-analytic estimate of discernment in control and treatment at each level of conservatism (rounded to the nearest 0.25) are shown with dots. More representative samples from Lucid and YouGov are shown in the left panel; convenience samples from Amazon Mechanical Turk are shown in the right panel.
Fig. 6 Accuracy prompts increase sharing discernment across the ideological spectrum. Shown is sharing discernment in the control (red triangles) versus treatment (blue circles) as a function of liberal versus conservative ideology. The model fits for discernment in control and treatment, based on meta-analytic estimates of model coefficients, are shown with solid lines. The meta-analytic estimate of discernment in control and treatment at each level of conservatism (rounded to the nearest 0.25) are shown with dots. More representative samples from Lucid and YouGov are shown in the left panel; convenience samples from Amazon Mechanical Turk are shown in the right panel.CC BY · PubMed Central
Fig. 7 Flow diagram for study selection. Demonstration of study selection for our internal meta-analysis.
Fig. 7 Flow diagram for study selection. Demonstration of study selection for our internal meta-analysis.CC BY · PubMed Central

Verification

Claims the paper makes

73 Own result · 27 Not checked yet · 17 Own method · 14 Own interpretation · 3 No basis given · 1 Supported so far · 1 Partly supported · accuracy not yet measured

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Show all 92 claims

  • It may state two separate claims as one. 29
  • Probably not a substantial claim. 21
44 of 44
  • The paper relies on this without support.

    The massive networked character of social media platforms means that when people choose to share misinformation online, it has the potential to reach and influence a large number of others.

    “The massive networked character of social media platforms means that when people choose to share misinformation online, it has the potential to reach (and influence) a large number of others.”
  • The paper relies on this without support.

    Out-of-sample average accuracy ratings of headlines are valid measures of participants’ perceived accuracy.

    “for 15 experiments we have out-of-sample ratings of the average accuracy of each headline (e.g., from pre-tests, or from other experiments or conditions where participants made accuracy ratings rather than indicating sharing intentions).”
  • The paper relies on this without support.

    Participants' self-reported likelihood of sharing headlines is a valid proxy for their actual sharing behavior on social media.

    “participants were asked to indicate their likelihood of sharing a set of true and false headlines”
    “sharing decisions (the dependent variable) are rescaled such that the minimum possible value is 0 (indicating a very low likelihood of sharing) and the maximum possible value is 1 (indicating a very high likelihood of sharing)”
  • The paper relies on this without support.

    The professional fact-checker ratings of headline veracity are accurate.

    “the veracity of which were rated by professional fact-checkers”
  • The paper relies on this without support.

    The accuracy prompts used in the experiments are representative of accuracy prompts that could be deployed on social media platforms.

    “a variety of accuracy prompts”
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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Consistentz = 7.102reported p < .001 · recomputed p < .001

    “z = 7.102, p < .001”

  • Consistentz = 6.636reported p < .001 · recomputed p < .001

    “z = 6.636, p < .001”

  • Consistentz = 7.851reported p < .001 · recomputed p < .001

    “z = 7.851, p < .001”

  • Consistentz = 15.695reported p < .001 · recomputed p < .001

    “z = 15.695, p < .001”

  • Consistentz = 1.44reported p = .15 · recomputed p = .150

    “z = 1.44, p = .150”

  • ConsistentQ(19) = 88.53reported p < .001 · recomputed p < .001

    “Q(19) = 88.53, p < .001”

  • ConsistentQ(19) = 23.33reported p = .223 · recomputed p = .223

    “Q(19) = 23.33, p = .223”

  • ConsistentQ(19) = 22.42reported p = .264 · recomputed p = .264

    “Q(19) = 22.42, p = .264”

  • Consistentz = 7.823reported p < .001 · recomputed p < .001

    “z = 7.823, p < .001”

  • Consistentz = 7.2reported p < .001 · recomputed p < .001

    “z = 7.2, p < .001”

  • Consistentz = -5.548reported p < .001 · recomputed p < .001

    “z = −5.548, p < .001”

  • Consistentz = 14.1reported p < .001 · recomputed p < .001

    “z = 14.1, p < .001”

  • Consistentz = 1.89reported p = .059 · recomputed p = .059

    “z = 1.89, p = .059”

  • Consistentz = 2.765reported p = .006 · recomputed p = .006

    “z = 2.765, p = .006”

  • Consistentz = 2.92reported p = .003 · recomputed p = .004

    “z = 2.92, p = .003”

  • Consistentz = -2.99reported p = .003 · recomputed p = .003

    “z = −2.990, p = .003”

  • Consistentz = 8.7reported p < .001 · recomputed p < .001

    “z = 8.7, p < .001”

  • Consistentz = 0.775reported p = .438 · recomputed p = .438

    “z = 0.775, p = .438”

  • Consistentz = 4.974reported p < .001 · recomputed p < .001

    “z = 4.974, p < .001”

  • Consistentz = 7.1reported p < .001 · recomputed p < .001

    “z = 7.1, p < .001”

  • Consistentz = -5.161reported p < .001 · recomputed p < .001

    “z = −5.161, p < .001”

  • Consistentz = 16.6reported p < .001 · recomputed p < .001

    “z = 16.6, p < .001”

  • Consistentz = 0.338reported p = .735 · recomputed p = .735

    “z = 0.338, p = .735”

  • Consistentz = 19.417reported p < .001 · recomputed p < .001

    “z = 19.417, p < .001”

  • ConsistentQ(14) = 18.99reported p = .165 · recomputed p = .165

    “Q(14) = 18.99, p = .165”

  • Consistentr(355) = 0.663reported p < .001 · recomputed p < .001

    “r(355) = 0.663, p < .001”

  • Consistentz = 3.124reported p = .002 · recomputed p = .002

    “z = 3.124, p = .002”

  • Consistentz = 11.276reported p < .001 · recomputed p < .001

    “z = 11.276, p < .001”

  • Consistentz = 1.085reported p = .278 · recomputed p = .278

    “z = 1.085, p = .278”

  • Consistentz = -1.869reported p = .062 · recomputed p = .062

    “z = −1.869, p = .062”

  • Consistentz = -0.292reported p = .77 · recomputed p = .770

    “z = −0.292, p = .770”

  • Consistentz = 4.188reported p < .001 · recomputed p < .001

    “z = 4.188, p < .001”

  • Consistentz = 4.804reported p < .001 · recomputed p < .001

    “z = 4.804, p < .001”

  • Consistentz = 5.707reported p < .001 · recomputed p < .001

    “z = 5.707, p < .001”

  • Consistentz = 4.94reported p < .001 · recomputed p < .001

    “z = 4.940, p < .001”

  • Consistentz = 3.404reported p = .001 · recomputed p < .001

    “z = 3.404, p = .001”

  • Consistentz = 2.988reported p = .003 · recomputed p = .003

    “z = 2.988, p = .003”

  • Consistentz = 2.79reported p = .005 · recomputed p = .005

    “z = 2.790, p = .005”

  • Consistentz = 2.608reported p = .009 · recomputed p = .009

    “z = 2.608, p = .009”

  • Consistentz = 2.784reported p = .005 · recomputed p = .005

    “z = 2.784, p = .005”

  • Consistentz = 2.119reported p = .034 · recomputed p = .034

    “z = 2.119, p = .034”

  • Consistentz = 3.581reported p < .001 · recomputed p < .001

    “z = 3.581, p < .001”

  • Consistentz = 0.726reported p = .468 · recomputed p = .468

    “z = 0.726, p = .468”

  • Consistentz = 4.44reported p < .001 · recomputed p < .001

    “z = 4.440, p < .001”

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Citations

Cites

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Research works printed but not linked: 1

  1. Rivers, D. & Bailey, D. Inference from matched samples in the 2008 U.S. national elections. Proc. Jt. Stat. Meet. 627–639 (2009).

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Metadata

Authors
Pennycook, G., & Rand, D. G.
Affiliations
  • University of Regina (CA)
  • Massachusetts Institute of Technology (US)
Corresponding author
  • Pennycook, Gordon
  • Rand, David G.
Venue
Nature Communications
Published
2022-04-28
Volume
13
Issue
1
Pages
2333-2333
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4225127529
Cited by
270
References
74
Citations by year
2026: 66 · 2025: 59 · 2024: 76 · 2023: 41 · 2022: 23
FWCI
8.44
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
  • PsyArXiv (OSF Preprints) (submittedVersion)
Funders
  • Gouvernement du Canada | Canadian Institutes of Health Research
  • Gouvernement du Canada | Social Sciences and Humanities Research Council of Canada
  • William and Flora Hewlett Foundation
  • Google
  • National Science Foundation
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
  • Social Sciences › Communication › Communication and COVID-19 Impact
Keywords
misinformation, news sharing, replicability, generalizability, political ideology, COVID-19 news, political news
MeSH terms
COVID-19, Communication, Humans, Politics, Intention

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