IDEAS home Printed from https://ideas.repec.org/p/pen/papers/25-021.html

Learning from Viral Content

Author

Listed:
  • Krishna Dasaratha

    (Boston University)

  • Kevin He

    (University of Pennsylvania)

Abstract

We study learning on social media with an equilibrium model of users interacting with shared news stories. Rational users arrive sequentially, observe an original story (i.e., a private signal) and a sample of predecessors’ stories in a news feed, then decide which stories to share. The observed sample of stories is jointly determined by predecessors’ sharing behavior and the sampling algorithm generating news feeds. We focus on how often this algorithm selects more viral (i.e., widely shared) stories. Showing users viral stories can increase information aggregation, but it can also generate steady states where most shared stories are wrong. These misleading steady states self-perpetuate, as users who observe wrong stories develop wrong beliefs, and thus rationally continue to share them. Finally, we describe several consequences for platform design and robustness.

Suggested Citation

  • Krishna Dasaratha & Kevin He, 2025. "Learning from Viral Content," PIER Working Paper Archive 25-021, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:25-021
    as

    Download full text from publisher

    File URL: https://economics.sas.upenn.edu/system/files/working-papers/25-021%20PIER%20Paper%20Submission.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luca P. Merlino & Paolo Pin & Nicole Tabasso, 2023. "Debunking Rumors in Networks," American Economic Journal: Microeconomics, American Economic Association, vol. 15(1), pages 467-496, February.
    2. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    3. T Renee Bowen & Danil Dmitriev & Simone Galperti, 2023. "Learning from Shared News: When Abundant Information Leads to Belief Polarization," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(2), pages 955-1000.
    4. Michel BenaÔm & J–rgen W. Weibull, 2003. "Deterministic Approximation of Stochastic Evolution in Games," Econometrica, Econometric Society, vol. 71(3), pages 873-903, May.
    5. Rachel Kranton & David McAdams, 2024. "Social Connectedness and Information Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 16(1), pages 33-62, February.
    6. Gordon Pennycook & Ziv Epstein & Mohsen Mosleh & Antonio A. Arechar & Dean Eckles & David G. Rand, 2021. "Shifting attention to accuracy can reduce misinformation online," Nature, Nature, vol. 592(7855), pages 590-595, April.
    7. Daron Acemoglu & Asuman Ozdaglar & James Siderius, 2024. "A Model of Online Misinformation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(6), pages 3117-3150.
    8. Dennis E. Smallwood & John Conlisk, 1979. "Product Quality in Markets Where Consumers are Imperfectly Informed," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 93(1), pages 1-23.
    9. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    10. Iijima, Ryota & Oyama, Daisuke, 2025. "Mean-field approximation of forward-looking population dynamics," Journal of Economic Theory, Elsevier, vol. 230(C).
    11. Yiangos Papanastasiou, 2020. "Fake News Propagation and Detection: A Sequential Model," Management Science, INFORMS, vol. 66(5), pages 1826-1846, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hsu, Chin-Chia & Ajorlou, Amir & Jadbabaie, Ali, 2025. "A game-theoretic model of misinformation spread on social networks," Games and Economic Behavior, Elsevier, vol. 153(C), pages 386-407.
    2. Tuval Danenberg & Drew Fudenberg, 2024. "Endogenous Attention and the Spread of False News," Papers 2406.11024, arXiv.org, revised Feb 2026.
    3. Yasushi Asako & Yoshio Kamijo & Daiki Kishishita & Masayuki Odora, 2026. "Fighting Fake News with Peer Feedback: Theory and Experiment," Working Papers 2531, Waseda University, Faculty of Political Science and Economics.
    4. Dana Sisak & Philipp Denter, 2024. "Truth, Lies, and Social Ties: When Image Concerns Fuel Fake News," Papers 2410.19557, arXiv.org, revised Nov 2025.
    5. Matzke, Christina & Wirth, Benedikt, 2009. "Product Pricing when Demand Follows a Rule of Thumb," Bonn Econ Discussion Papers 3/2009, University of Bonn, Bonn Graduate School of Economics (BGSE).
    6. Della Lena, Sebastiano, 2024. "The spread of misinformation in networks with individual and social learning," European Economic Review, Elsevier, vol. 168(C).
    7. Gonzalo Cisternas & Jorge Vásquez, 2022. "Misinformation in Social Media: The Role of Verification Incentives," Staff Reports 1028, Federal Reserve Bank of New York.
    8. Jonas Hedlund & Carlos Oyarzun, 2018. "Imitation in heterogeneous populations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(4), pages 937-973, June.
    9. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    10. Krishna Dasaratha & Kevin He, 2022. "Learning from Viral Information," Papers 2210.01267, arXiv.org, revised Jun 2026.
    11. Sobel, Joel, 2000. "Economists' Models of Learning," Journal of Economic Theory, Elsevier, vol. 94(2), pages 241-261, October.
    12. Conlisk, John & Gong, Jyh-Chyi & Tong, Ching H., 2000. "Imitation and the dynamics of norms," Mathematical Social Sciences, Elsevier, vol. 40(2), pages 197-213, September.
    13. H Peyton Young & Itai Arieli & Yakov Babichenko & Ron Peretz, 2019. "The Speed of Innovation Diffusion in Social Networks," Economics Series Working Papers 884, University of Oxford, Department of Economics.
    14. Camargo, Braz, 2014. "Learning in society," Games and Economic Behavior, Elsevier, vol. 87(C), pages 381-396.
    15. Itai Arieli & Yakov Babichenko & Ron Peretz & H. Peyton Young, 2019. "The Speed of Innovation Diffusion in Social Networks," Economics Papers 2019-W07, Economics Group, Nuffield College, University of Oxford.
    16. Matzke, Christina & Wirth, Benedikt, 2008. "A non-standard approach to a market with boundedly rational consumers and strategic firms. Part I: A microfoundation for the evolution of sales," Bonn Econ Discussion Papers 10/2008, University of Bonn, Bonn Graduate School of Economics (BGSE).
    17. Wilhite, Allen, 2014. "Network structure, games, and agent dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 225-238.
    18. Itai Arieli & Yakov Babichenko & Ron Peretz & H. Peyton Young, 2020. "The Speed of Innovation Diffusion in Social Networks," Econometrica, Econometric Society, vol. 88(2), pages 569-594, March.
    19. McFadden, Daniel L & Train, Kenneth E, 1996. "Consumers' Evaluation of New Products: Learning from Self and Others," Journal of Political Economy, University of Chicago Press, vol. 104(4), pages 683-703, August.
    20. Yann Lecorps & Marie Obidzinski & Yves Oytana, 2026. "More Information, Less Reliability? Technological Change and Fact-Finding in Human Rights Investigations," Working Papers AFED 26-01, Association Francaise d'Economie du Droit (AFED).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pen:papers:25-021. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Administrator (email available below). General contact details of provider: https://edirc.repec.org/data/deupaus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.