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Modeling misinformation spread for policy evaluation: a parsimonious framework

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We develop a parsimonious framework for evaluating the efficacy of different approaches for limiting the spread of misinformation. We use this framework and simulation studies to determine the evolution of truthful and fake messages on social media platforms and then investigate the following policy interventions: (1) our suggested approach of having the platform require senders of messages to also state their perceived (possibly incorrect) veracity of the message, (2) provide some accuracy nudge to increase the number of potential readers who can accurately identify fake messages, (3) have the platform flag fake messages, and (4) have the platform demote or down-rank fake messages. We find that when a significant number of senders are able to correctly identify the veracity of the message, the market can self-regulate under our suggested approach. If this is not the case, we find that augmenting our approach with any of the other approaches is effective in reducing the spread of misinformation.

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Data Availability

The simulation code is available upon request from the authors.


  1. Note, person type is message specific, i.e., a person can be sophisticated for one message but naive for another.

  2. Our main results on the reading and posting dynamics of fake messages remain valid even without specifying a particular relationship between \({P}_{T}^{N}\) and \({P}_{F}^{N}\).

  3. Plots on the extensions for these three interventions are in the Online Appendix.


  • Acemoglu, D., Ozdaglar, A., & Siderius, J. (2023). A Model of online misinformation. Review of Economic Studies.

  • Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236.

    Article  Google Scholar 

  • Anderson, E. T., & Simester, D. I. (2014). Reviews without a purchase: Low ratings, loyal customers, and deception. Journal of Marketing Research, 51(3), 249–269.

    Article  Google Scholar 

  • Stewart, A. J.,  Arechar, A. A., Rand, D. G., Plotkin, J. B. (2023). The game theory of fake news. (

  • Deng, Y., Staelin, R., Wang, W., & Boulding, W. (2018). Consumer sophistication, word-of-mouth and “false” promotions. Journal of Economic Behavior & Organization, 152, 98–123.

    Article  Google Scholar 

  • Fong, J.,  Guo, T., Rao, A. (2023). Debunking misinformation about consumer products: Effects on beliefs and purchase behavior. Journal of Marketing Research, 0(0).

  • He, S., Hollenbeck, B., & Proserpio, D. (2022). The market for fake reviews. Marketing Science, 41(5), 896–921.

    Article  Google Scholar 

  • Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018). The science of fake news. Science, 359(6380), 1094–1096.

    Article  Google Scholar 

  • Pennycook, G., & Rand, D. G. (2021). The psychology of fake news. Trends in Cognitive Sciences, 25(5), 388–402.

    Article  Google Scholar 

  • Pennycook, Gordon, Epstein, Ziv, Mosleh, Mohsen, Arechar, Antonio A., Eckles, Dean, & Rand, David G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592(7855), 590–595.

    Article  Google Scholar 

  • Rao, A. (2022). Deceptive claims using fake news advertising: The impact on consumers. Journal of Marketing Research, 59(3), 534–554.

    Article  Google Scholar 

  • Staelin, R., Urbany, J. E., & Ngwe, D. (2023). Competition and the regulation of fictitious pricing. Journal of Marketing, 87(6), 826–846.

  • Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.

    Article  Google Scholar 

  • Walter, N., Jonathan Cohen, R., Holbert, L., & Morag, Y. (2020). Fact-checking: A meta-analysis of what works and for whom. Political Communication, 37(3), 350–375.

    Article  Google Scholar 

  • Ward, A. F., Zheng, J., & Broniarczyk, S. M. (2023). I share, therefore I know? Sharing online content-even without reading it-inflates subjective knowledge. Journal of Consumer Psychology, 33(3), 469–488.

    Article  Google Scholar 

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The authors would like to thank Tong Guo, Charles Staelin, and Nils Wernerfelt for their useful comments. All errors are our own.

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Correspondence to Richard Staelin.

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Deng, Y., Staelin, R. Modeling misinformation spread for policy evaluation: a parsimonious framework. Mark Lett (2024).

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