Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Battling the coronavirus ‘infodemic’ among social media users in Kenya and Nigeria


How can we induce social media users to be discerning when sharing information during a pandemic? An experiment on Facebook Messenger with users from Kenya (n = 7,498) and Nigeria (n = 7,794) tested interventions designed to decrease intentions to share COVID-19 misinformation without decreasing intentions to share factual posts. The initial stage of the study incorporated: (1) a factorial design with 40 intervention combinations; and (2) a contextual adaptive design, increasing the probability of assignment to treatments that worked better for previous subjects with similar characteristics. The second stage evaluated the best-performing treatments and a targeted treatment assignment policy estimated from the data. We precisely estimate null effects from warning flags and related article suggestions, tactics used by social media platforms. However, nudges to consider the accuracy of information reduced misinformation sharing relative to control by 4.9% (estimate = −2.3 percentage points, 95% CI = [−4.2, −0.35]). Such low-cost scalable interventions may improve the quality of information circulating online.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Learning stage estimates.
Fig. 2: Response estimates.
Fig. 3: Selected covariate means by restricted targeted policy assignment.
Fig. 4: Headline- and respondent-level treatments tested in the evaluation stage.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available at Source data are provided with this paper.

Code availability

The analysis code that generate the figures, tables and results presented in this study are available at


  1. World Population Review (Facebook, 2022);

  2. Swire-Thompson, B., DeGutis, J. & Lazer, D. Searching for the backfire effect: measurement and design considerations. J. Appl. Res. Mem. Cogn. 9, 286–299 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Even-Dar, E., Mannor, S., Mansour, Y. & Mahadevan, S. Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems. J. Mach. Learn. Res. 7, 1079–1105 (2006).

  4. Caria, S. et al. An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan (CESifo, 2020).

  5. Kasy, M. & Sautmann, A. Adaptive treatment assignment in experiments for policy choice. Econometrica 89, 113–132 (2021).

    Article  MathSciNet  Google Scholar 

  6. Athey, S. et al. Contextual bandits in a survey experiment on charitable giving: within-experiment outcomes versus policy learning. Preprint at (2022).

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

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Guess, A. M. et al. A digital media literacy intervention increases discernment between mainstream and false news in the United States and India. Proc. Natl Acad. Sci. USA 117, 15536–15545 (2020).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  9. Ceylan, G.anderson, I. A. & Wood, W. Sharing of misinformation is habitual, not just lazy or biased. Proc. Natl Acad. Sci. USA 120, e2216614120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pennycook, G. & Rand, D. G. Who falls for fake news? the roles of bullshit receptivity, overclaiming, familiarity and analytic thinking. J. Personal. 88, 185–200 (2020).

    Article  Google Scholar 

  11. Haghdoost, Y. Alcohol poisoning kills 100 Iranians seeking virus protection. Bloomberg Markets (18 March 2020);

  12. Gallotti, R., Valle, F., Castaldo, N., Sacco, P. & De Domenico, M. Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics. Nat. Hum. Behav. 4, 1285–1293 (2020).

    Article  PubMed  Google Scholar 

  13. Bursztyn, L., Rao, A., Roth, C. & Yanagizawa-Drott, D. Opinions as Facts (ECONtribute, 2022).

  14. Pummerer, L. et al. Conspiracy theories and their societal effects during the COVID-19 pandemic. Soc. Psychol. Personal. Sci. 13, 49–59 (2022).

    Article  Google Scholar 

  15. Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K. & Larson, H. J. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat. Hum. Behav. 5, 337–348 (2021).

    Article  PubMed  Google Scholar 

  16. Ecker, U. K. et al. The psychological drivers of misinformation belief and its resistance to correction. Nat. Rev. Psychol. 1, 13–29 (2022).

    Article  Google Scholar 

  17. 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).

    Article  Google Scholar 

  18. 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).

    Article  Google Scholar 

  19. Kreps, S. E. & Kriner, D. Medical Misinformation in the COVID-19 Pandemic (SSRN, 2020).

  20. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bowles, J., Larreguy, H. & Liu, S. Countering misinformation via Whatsapp: preliminary evidence from the COVID-19 pandemic in Zimbabwe. PLoS ONE 15, e0240005 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Goldstein, J. A., Grossman, S. & Startz, M. Belief in COVID-19 misinformation in Nigeria. J. Polit.

  24. Badrinathan, S. Educative interventions to combat misinformation: evidence from a field experiment in India. Am. Polit. Sci. Rev. 115, 1325–1341 (2021).

    Article  Google Scholar 

  25. Ali, A. & Qazi, I. A. Countering misinformation on social media through educational interventions: evidence from a randomized experiment in Pakistan. J. Dev. Econ. 163, 103108 (2023).

    Article  Google Scholar 

  26. Roozenbeek, J., Suiter, J. & Culloty, E. Countering misinformation: evidence, knowledge gaps and implications of current interventions. Eur. Psychol. (2023).

  27. Rosenzweig, L. R., Bergquist, P., Hoffmann Pham, K., Rampazzo, F. & Mildenberger, M. Survey sampling in the global south using Facebook advertisements. Preprint at SocArXiv (2020).

  28. Broockman, D. E., Kalla, J. L. & Sekhon, J. S. The design of field experiments with survey outcomes: a framework for selecting more efficient, robust and ethical designs. Polit. Anal. 25, 435–464 (2017).

    Article  Google Scholar 

  29. Dimakopoulou, M., Athey, S. & Imbens, G. Estimation considerations in contextual bandits. Preprint at (2017).

  30. Dimakopoulou, M., Zhou, Z., Athey, S. & Imbens, G. Balanced linear contextual bandits. Proc. AAAI Conf. Artif. Intell. 33, 3445–3453 (2019).

    Google Scholar 

  31. Zhan, R., Ren, Z., Athey, S. & Zhou, Z. Policy learning with adaptively collected data. Manage. Sci. (2023).

  32. Zhan, R., Hadad, V., Hirshberg, D. A. & Athey, S. Off-policy evaluation via adaptive weighting with data from contextual bandits. in Proc. 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21) 2125–2135 (2021).

  33. Athey, S., Tibshirani, J. & Wager, S. Generalized random forests. Ann. Stat. 47, 1148–1178 (2019).

    Article  MathSciNet  Google Scholar 

  34. Robins, J. M. & Rotnitzky, A. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90, 122–129 (1995).

    Article  MathSciNet  Google Scholar 

  35. Meixler, E. Facebook is dropping its fake news red flag warning after finding it had the opposite effect. TIME (22 December 2017);

  36. Bode, L. & Vraga, E. K. In related news, that was wrong: the correction of misinformation through related stories functionality in social media. J. Commun. 65, 619–638 (2015).

    Article  Google Scholar 

  37. Nyhan, B. & Reifler, J. When corrections fail: the persistence of political misperceptions. Polit. Behav. 32, 303–330 (2010).

    Article  Google Scholar 

  38. Epstein, Z., Sirlin, N., Arechar, A., Pennycook, G. & Rand, D. The social media context interferes with truth discernment. Sci. Adv. 9, eabo6169 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

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

  40. Pennycook, G. & Rand, D. G. Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation. Nat. Commun. 13, 2333 (2022).

  41. 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).

    Article  Google Scholar 

  42. Mosleh, M., Pennycook, G. & Rand, D. G. Self-reported willingness to share political news articles in online surveys correlates with actual sharing on Twitter. PLoS ONE 15, e0228882 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Top 10 African countries with the most Facebook users. ITNews Africa (2016);

  44. 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).

  45. Ghosh, S. Facebook will show people anti-fake news articles when they post false stories. (3 August 2017);

  46. Robins, J. M., Rotnitzky, A. & Zhao, L. P. Estimation of regression coefficients when some regressors are not always observed. J. Am. Stat. Assoc. 89, 846–866 (1994).

    Article  MathSciNet  Google Scholar 

  47. Tibshirani, J., Athey, S. & Wager, S. grf: Generalized Random Forests. R package version 2.3.0 (2020).

  48. Gilens, M. Political ignorance and collective policy preferences. Am. Polit. Sci. Rev. 95, 379–396 (2001).

  49. Martel, C., Pennycook, G. & Rand, D. G. Reliance on emotion promotes belief in fake news. Cogn. Res. Princ. Implic. 5, 47 (2020).

  50. Rosenzweig, L. R., Bago, B., Berinsky, A. J. & Rand, D. G. Happiness and surprise are associated with worse truth discernment of COVID-19 headlines among social media users in Nigeria. Harvard Kennedy School Misinformation Review (10 August 2021);

  51. Bago, B., Rosenzweig, L. R., Berinsky, A. J. & Rand, D. G. Emotion may predict susceptibility to fake news but emotion regulation does not seem to help. Cogn. Emot. 36, 1166–1180 (2022).

  52. Bago, B., Rand, D. G. & Pennycook, G. Fake news, fast and slow: deliberation reduces belief in false (but not true) news headlines. J. Exp. Psychol. 149, 1608–1613 (2020).

  53. Costa, M., Schaffner, B. F. & Prevost, A. Walking the walk? Experiments on the effect of pledging to vote on youth turnout. PLoS ONE 13, e0197066 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Cotterill, S., John, P. & Richardson, L. The impact of a pledge request and the promise of publicity: a randomized controlled trial of charitable donations. Soc. Sci. Q. 94, 200–216 (2013).

    Article  Google Scholar 

  55. Gross, J. J. The emerging field of emotion regulation: an integrative review. Rev. Gen. Psychol. 2, 271–299 (1998).

    Article  Google Scholar 

  56. Yadlowsky, S., Fleming, S., Shah, N., Brunskill, E. & Wager, S. Evaluating treatment prioritization rules via rank-weighted average treatment effects. Preprint at (2021).

Download references


We received advertising credits for this study from Facebook Health and funding from the Golub Capital Social Impact Lab and Office of Naval Research grant no. N00014-19-1-246 (S.A.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. For exceptional research assistance, we thank Z. (J.) Li, R. Ruiz, U. Byambadalai and H. (T.) Zong. We thank J. Davis, S. Grossman, L. Jakli, E. Jee, T. Kumar, E. Palikot and A. Siegel for feedback and comments, as well as the participants of the seminar series of the Development Innovation Lab at the Becker Friedman Institute. We thank J. Kiselik for editorial assistance.

Author information

Authors and Affiliations



M.O.W., L.R. and S.A. designed the research and wrote the paper. M.O.W. and L.R. performed the experimental studies. M.O.W. analysed the data, with input from S.A. and L.R.

Corresponding author

Correspondence to Molly Offer-Westort.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Gordon Pennycook, Xiao Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Figs. 1–5 and Tables 1–13. Supplementary methods materials are presented in Section S1. Supplementary results are presented in Section S2.

Reporting Summary

Source data

Source Data Fig. 1

Discernment measure mean response estimates, standard errors and probabilities for learning stage.

Source Data Fig. 2

Discernment and other sharing measure mean response estimates, standard errors, for evaluation stage.

Source Data Fig. 3

Covariate means, standard errors by assignment group in evaluation stage.

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

Offer-Westort, M., Rosenzweig, L.R. & Athey, S. Battling the coronavirus ‘infodemic’ among social media users in Kenya and Nigeria. Nat Hum Behav (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing