Skip to main content
Log in

Fact-checker warning labels are effective even for those who distrust fact-checkers

  • Article
  • Published:

From Nature Human Behaviour

View current issue Submit your manuscript

Abstract

Warning labels from professional fact-checkers are one of the most widely used interventions against online misinformation. But are fact-checker warning labels effective for those who distrust fact-checkers? Here, in a first correlational study (N = 1,000), we validate a measure of trust in fact-checkers. Next, we conduct meta-analyses across 21 experiments (total N = 14,133) in which participants evaluated true and false news posts and were randomized to either see no warning labels or to see warning labels on a high proportion of the false posts. Warning labels were on average effective at reducing belief in (27.6% reduction), and sharing of (24.7% reduction), false headlines. While warning effects were smaller for participants with less trust in fact-checkers, warning labels nonetheless significantly reduced belief in (12.9% reduction), and sharing of (16.7% reduction), false news even for those most distrusting of fact-checkers. These results suggest that fact-checker warning labels are a broadly effective tool for combatting misinformation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1: Experimental stimuli.
Fig. 2: Relationship between TFC, partisanship and individual differences.
Fig. 3: Warnings reduce perceived accuracy even for participants who strongly distrust fact-checkers.
Fig. 4: Warning label effects on accuracy by TFC decile.
Fig. 5: Meta-analyses of warning effect on sharing intentions by TFC.
Fig. 6: Warning label effects on sharing by TFC decile.

Similar content being viewed by others

Data availability

All data are available on our OSF page (https://osf.io/yux4d/).

Code availability

All analysis codes are available on our OSF page (https://osf.io/yux4d/).

References

  1. Lazer, D. M. J. et al. The science of fake news. Science 359, 1094–1096 (2018).

    CAS  PubMed  Google Scholar 

  2. Pennycook, G. & Rand, D. G. The psychology of fake news. Trends Cogn. Sci. 25, 388–402 (2021).

    PubMed  Google Scholar 

  3. Kozyreva, A. et al. Toolbox of individual-level interventions against online misinformation. Nat. Hum. Behav. 8, 1044–1052 (2024).

    PubMed  Google Scholar 

  4. Mosseri, A. Addressing hoaxes and fake news. Meta https://about.fb.com/news/2016/12/news-feed-fyi-addressing-hoaxes-and-fake-news/ (2016).

  5. Instagram. Combatting misinformation on Instagram. Instagram https://about.instagram.com/blog/announcements/combatting-misinformation-on-instagram (2019).

  6. Roth, Y. & Pickles, N. Updating our approach to misleading information. Twitter Blog https://blog.x.com/en_us/topics/product/2020/updating-our-approach-to-misleading-information (2020).

  7. Porter, E. & Wood, T. J. Political misinformation and factual corrections on the Facebook news feed: experimental evidence. J. Polit. 84, 1812–1817 (2022).

    Google Scholar 

  8. Mena, P. Cleaning up social media: the effect of warning labels on likelihood of sharing false news on Facebook. Policy Internet 12, 165–183 (2020).

    Google Scholar 

  9. Pennycook, G., Cannon, T. D. & Rand, D. G. Prior exposure increases perceived accuracy of fake news. J. Exp. Psychol. Gen. 147, 1865 (2018).

    PubMed  PubMed Central  Google Scholar 

  10. Pennycook, G., Bear, A., Collins, E. T. & Rand, D. G. The implied truth effect: attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings. Manag. Sci. 66, 4944–4957 (2020).

    Google Scholar 

  11. Clayton, K. et al. Real solutions for fake news? Measuring the effectiveness of general warnings and fact-check tags in reducing belief in false stories on social media. Polit. Behav. 42, 1073–1095 (2020).

    Google Scholar 

  12. Brashier, N. M., Pennycook, G., Berinsky, A. J. & Rand, D. G. Timing matters when correcting fake news. Proc. Natl Acad. Sci. USA 118, e2020043118 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Martel, C. & Rand, D. G. Misinformation warning labels are widely effective: a review of warning effects and their moderating features. Curr. Opin. Psychol. 54, 101710 (2023).

    PubMed  Google Scholar 

  14. Brashier, N. M. Fighting misinformation among the most vulnerable users. Curr. Opin. Psychol. 57, 101813 (2024).

    PubMed  Google Scholar 

  15. Guess, A., Nagler, J. & Tucker, J. Less than you think: prevalence and predictors of fake news dissemination on Facebook. Sci. Adv. 5, eaau4586 (2019).

    PubMed  PubMed Central  Google Scholar 

  16. Guess, A., Nyhan, B. & Reifler, J. Selective exposure to misinformation: evidence from the consumption of fake news during the 2016 US presidential campaign. European Research Council 9, 4 (2018).

    Google Scholar 

  17. Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B. & Lazer, D. Fake news on Twitter during the 2016 U.S. presidential election. Science 363, 374–378 (2019).

    CAS  PubMed  Google Scholar 

  18. Mosleh, M., Yang, Q., Zaman, T., Pennycook, G. & Rand, D. G. Unbiased misinformation policies sanction conservatives more than liberals. Preprint at https://osf.io/preprints/psyarxiv/ay9q5 (2024).

  19. González-Bailón, S. et al. Asymmetric ideological segregation in exposure to political news on Facebook. Science 381, 392–398 (2023).

    PubMed  Google Scholar 

  20. Walker, M. & Gottfried, J. Republicans far more likely than Democrats to say fact-checkers tend to favor one side. Pew Research Center https://www.pewresearch.org/short-reads/2019/06/27/republicans-far-more-likely-than-democrats-to-say-fact-checkers-tend-to-favor-one-side/ (2019).

  21. Nyhan, B. & Reifler, J. Estimating fact-checking’s effects. Arlingt. VA Am. Press Inst. (2015).

  22. Benegal, S. D. & Scruggs, L. A. Correcting misinformation about climate change: the impact of partisanship in an experimental setting. Clim. Change 148, 61–80 (2018).

    Google Scholar 

  23. Berinsky, A. J. Rumors and health care reform: experiments in political misinformation. Br. J. Polit. Sci. 47, 241–262 (2017).

    Google Scholar 

  24. Prike, T. & Ecker, U. K. Effective correction of misinformation. Curr. Opin. Psychol. 54, 101712 (2023).

    PubMed  Google Scholar 

  25. Swire, B., Berinsky, A. J., Lewandowsky, S. & Ecker, U. K. H. Processing political misinformation: comprehending the Trump phenomenon. R. Soc. Open Sci. 4, 160802 (2017).

    PubMed  PubMed Central  Google Scholar 

  26. Liu, X., Qi, L., Wang, L. & Metzger, M. J. Checking the fact-checkers: the role of source type, perceived credibility, and individual differences in fact-checking effectiveness. Commun. Res. https://doi.org/10.1177/00936502231206419 (2023).

    Article  Google Scholar 

  27. Tsfati, Y. & Cappella, J. N. Do people watch what they do not trust? Exploring the association between news media skepticism and exposure. Commun. Res. 30, 504–529 (2003).

    Google Scholar 

  28. Amazeen, M. A. & Bucy, E. P. Conferring resistance to digital disinformation: the inoculating influence of procedural news knowledge. J. Broadcast. Electron. Media 63, 415–432 (2019).

    Google Scholar 

  29. Frederick, S. Cognitive reflection and decision making. J. Econ. Perspect. 19, 25–42 (2005).

    Google Scholar 

  30. Guess, A. M. & Munger, K. Digital literacy and online political behavior. Polit. Sci. Res. Methods 11, 110–128 (2023).

    Google Scholar 

  31. Pennycook, G., Binnendyk, J., Newton, C. & Rand, D. G. A practical guide to doing behavioral research on fake news and misinformation. Collabra Psychol. 7, 25293 (2021).

    Google Scholar 

  32. Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G. & Rand, D. G. Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol. Sci. 31, 770–780 (2020).

    PubMed  Google Scholar 

  33. Pennycook, G. et al. Shifting attention to accuracy can reduce misinformation online. Nature 592, 590–595 (2021).

    CAS  PubMed  Google Scholar 

  34. Bhardwaj, V., Martel, C. & Rand, D. G. Examining accuracy-prompt efficacy in combination with using colored borders to differentiate news and social content online. Harv. Kennedy Sch. Misinformation Rev. 4 (2023).

  35. Ajzen, I. & Fishbein, M. Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychol. Bull. 84, 888 (1977).

    Google Scholar 

  36. Scott, C. L. Interpersonal trust: a comparison of attitudinal and situational factors. Hum. Relat. 33, 805–812 (1980).

    Google Scholar 

  37. Önkal, D., Gönül, M. S., Goodwin, P., Thomson, M. & Öz, E. Evaluating expert advice in forecasting: users’ reactions to presumed vs. experienced credibility. Int. J. Forecast. 33, 280–297 (2017).

    Google Scholar 

  38. Sekiguchi, T. & Nakamaru, M. How intergenerational interaction affects attitude–behavior inconsistency. J. Theor. Biol. 346, 54–66 (2014).

    PubMed  Google Scholar 

  39. Altay, S., Hacquin, A.-S. & Mercier, H. Why do so few people share fake news? It hurts their reputation. N. Media Soc. 24, 1303–1324 (2022).

    Google Scholar 

  40. Orchinik, R., Dubey, R., Gershman, S. J., Powell, D. & Bhui, R. Learning from and about climate scientists. Preprint at https://osf.io/preprints/psyarxiv/ezua5 (2023).

  41. Walter, N. & Tukachinsky, R. A meta-analytic examination of the continued influence of misinformation in the face of correction: how powerful is it, why does it happen, and how to stop it? Commun. Res. 47, 155–177 (2020).

    Google Scholar 

  42. Yaqub, W., Kakhidze, O., Brockman, M. L., Memon, N. & Patil, S. Effects of credibility indicators on social media news sharing intent. In Proc. of the 2020 CHI Conference on Human Factors in Computing Systems 1–14 (ACM, 2020).

  43. Pan, C. A. et al. Comparing the perceived legitimacy of content moderation processes: contractors, algorithms, expert panels, and digital juries. Proc. ACM Hum. Comput. Interact. 6, 1–31 (2022).

    CAS  Google Scholar 

  44. Stencel, M., Luther, J. & Ryan, E. Fact-checking census shows slower growth. Poynter https://www.poynter.org/fact-checking/2021/fact-checking-census-shows-slower-growth/ (2021).

  45. Funke, D. Distrust in mainstream media is spilling over to fact-checking. Poynter https://www.poynter.org/fact-checking/2018/distrust-in-mainstream-media-is-spilling-over-to-fact-checking/ (2018).

  46. Rich, T. S., Milden, I. & Wagner, M. T. Research note: Does the public support fact-checking social media? It depends who and how you ask. Harv. Kennedy Sch. Misinformation Rev. 1 (2020).

  47. Lees, J., McCarter, A. & Sarno, D. M. Twitter’s disputed tags may be ineffective at reducing belief in fake news and only reduce intentions to share fake news among Democrats and Independents. J. Online Trust Saf. 1, 3 (2022).

  48. Jennings, J. & Stroud, N. J. Asymmetric adjustment: partisanship and correcting misinformation on Facebook. N. Media Soc. 25, 1501–1521 (2023).

    Google Scholar 

  49. Graham, M. H. & Porter, E. Increasing demand for fact-checking. Preprint at https://osf.io/preprints/osf/wdahm (2023).

  50. Sharevski, F., Alsaadi, R., Jachim, P. & Pieroni, E. Misinformation warnings: Twitter’s soft moderation effects on COVID-19 vaccine belief echoes. Comput. Secur. 114, 102577 (2022).

    PubMed  Google Scholar 

  51. Mosleh, M., Martel, C., Eckles, D. & Rand, D. Perverse downstream consequences of debunking: being corrected by another user for posting false political news increases subsequent sharing of low quality, partisan, and toxic content in a Twitter field experiment. In Proc. of the 2021 CHI Conference on Human Factors in Computing Systems 1–13 (ACM, 2021).

  52. Lyons, B., Mérola, V., Reifler, J. & Stoeckel, F. How politics shape views toward fact-checking: evidence from six European countries. Int. J. Press. 25, 469–492 (2020).

    Google Scholar 

  53. Porter, E. & Wood, T. J. The global effectiveness of fact-checking: evidence from simultaneous experiments in Argentina, Nigeria, South Africa, and the United Kingdom. Proc. Natl Acad. Sci. USA 118, e2104235118 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Arechar, A. A. et al. Understanding and combatting misinformation across 16 countries on six continents. Nat. Hum. Behav. 7, 1502–1513 (2023).

    PubMed  Google Scholar 

  55. Stagnaro, M. N., Druckman, J., Arechar, A. A., Willer, R. & Rand, D. Representativeness versus attentiveness: Assessing nine opt-in online survey samples. Preprint at https://osf.io/preprints/psyarxiv/h9j2d (2024).

  56. Pennycook, G. & Rand, D. G. Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc. Natl Acad. Sci. USA 116, 2521–2526 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Allcott, H., Braghieri, L., Eichmeyer, S. & Gentzkow, M. The welfare effects of social media. Am. Econ. Rev. 110, 629–676 (2020).

    Google Scholar 

  58. Sirlin, N., Epstein, Z., Arechar, A. A. & Rand, D. G. Digital literacy is associated with more discerning accuracy judgments but not sharing intentions. Harvard Kennedy School (HKS) Misinformation Review 2 (2021).

  59. Berinsky, A. J., Margolis, M. F. & Sances, M. W. Separating the shirkers from the workers? Making sure respondents pay attention on self‐administered surveys. Am. J. Polit. Sci. 58, 739–753 (2014).

    Google Scholar 

  60. Rosen, G., Harbath, K., Gleicher, N. & Leathern, R. Helping to protect the 2020 US elections. Facebook Newsroom https://about.fb.com/news/2019/10/update-on-election-integrity-efforts/ (2019).

Download references

Acknowledgements

We thank N. Stagnaro and A. Arechar for invaluable assistance with survey experiment data collection. We also thank B. Tappin and A. Bear for insightful comments on statistical procedures. We gratefully acknowledge funding via the National Science Foundation Graduate Research Fellowship, grant number 174530. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Authors and Affiliations

Authors

Contributions

C.M. and D.G.R. designed the studies and experiments. C.M. implemented the study design, collected the data and analysed the data. C.M. drafted the paper. D.G.R. provided critical revisions. All authors approved the final paper for submission.

Corresponding author

Correspondence to Cameron Martel.

Ethics declarations

Competing interests

Other work by D.G.R. has been funded by gifts from Meta and Google. The remaining author declares no competing interests.

Ethics and Inclusion statement

Our experimental procedures were approved by the MIT Committee on the Use of Humans as Experimental Subjects (protocol numbers E-2443 and E-4195).

Peer review

Peer review information

Nature Human Behaviour thanks Sacha Altay and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary methods, results, figures and tables.

Reporting Summary

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Martel, C., Rand, D.G. Fact-checker warning labels are effective even for those who distrust fact-checkers. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01973-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41562-024-01973-x

  • Springer Nature Limited

Navigation