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Learning from Viral Content

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  • Krishna Dasaratha
  • Kevin He

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, and then decide which stories to share. The observed sample of stories depends on what predecessors share as well as 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.

Suggested Citation

  • Krishna Dasaratha & Kevin He, 2022. "Learning from Viral Content," Papers 2210.01267, arXiv.org, revised Mar 2026.
    • Handle: RePEc:arx:papers:2210.01267
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    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. Rachel Kranton & David McAdams, 2024. "Social Connectedness and Information Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 16(1), pages 33-62, February.
    3. 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).
    4. 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.
    5. 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.
    6. Iijima, Ryota & Oyama, Daisuke, 2025. "Mean-field approximation of forward-looking population dynamics," Journal of Economic Theory, Elsevier, vol. 230(C).
    7. Yiangos Papanastasiou, 2020. "Fake News Propagation and Detection: A Sequential Model," Management Science, INFORMS, vol. 66(5), pages 1826-1846, May.
    8. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    9. 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.
    10. 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.
    11. Michel BenaÔm & J–rgen W. Weibull, 2003. "Deterministic Approximation of Stochastic Evolution in Games," Econometrica, Econometric Society, vol. 71(3), pages 873-903, May.
    12. 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.
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    Cited by:

    1. Tuval Danenberg & Drew Fudenberg, 2024. "Endogenous Attention and the Spread of False News," Papers 2406.11024, arXiv.org, revised Feb 2026.

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