IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2210.01267.html
   My bibliography  Save this paper

Learning from Viral Content

Author

Listed:
  • 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 and robustness.

Suggested Citation

  • Krishna Dasaratha & Kevin He, 2022. "Learning from Viral Content," Papers 2210.01267, arXiv.org, revised Aug 2023.
  • Handle: RePEc:arx:papers:2210.01267
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2210.01267
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

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

    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. Abhijit Banerjee & Olivier Compte, 2024. "Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning," Journal of Political Economy, University of Chicago Press, vol. 132(8), pages 2790-2829.
    2. Gu, Chen & Guo, Xu & Zhang, Chengping, 2022. "Analyst target price revisions and institutional herding," International Review of Financial Analysis, Elsevier, vol. 82(C).
    3. Ruomeng Cui & Dennis J. Zhang & Achal Bassamboo, 2019. "Learning from Inventory Availability Information: Evidence from Field Experiments on Amazon," Management Science, INFORMS, vol. 65(3), pages 1216-1235, March.
    4. 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.
    5. Cao, Melanie & Shi, Shouyong, 2006. "Signaling in the Internet craze of initial public offerings," Journal of Corporate Finance, Elsevier, vol. 12(4), pages 818-833, September.
    6. Wei He & Qian Wang, 2020. "The peer effect of corporate financial decisions around split share structure reform in China," Review of Financial Economics, John Wiley & Sons, vol. 38(3), pages 474-493, July.
    7. Kraemer, Carlo & Noth, Markus & Weber, Martin, 2006. "Information aggregation with costly information and random ordering: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 59(3), pages 423-432, March.
    8. Fishman, Arthur & Fishman, Ram & Gneezy, Uri, 2019. "A tale of two food stands: Observational learning in the field," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 101-108.
    9. Cavatorta, Elisa & Guarino, Antonio & Huck, Steffen, 2024. "Social learning with partial and aggregate information: Experimental evidence," Games and Economic Behavior, Elsevier, vol. 146(C), pages 292-307.
    10. Jacob K. Goeree & Leeat Yariv, 2015. "Conformity in the lab," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 15-28, July.
    11. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    12. Boğaçhan Çelen & Kyle Hyndman, 2012. "An experiment of social learning with endogenous timing," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 251-268, September.
    13. Bohl, Martin T. & Branger, Nicole & Trede, Mark, 2017. "The case for herding is stronger than you think," Journal of Banking & Finance, Elsevier, vol. 85(C), pages 30-40.
    14. Ennis, Huberto M. & Keister, Todd, 2005. "Government policy and the probability of coordination failures," European Economic Review, Elsevier, vol. 49(4), pages 939-973, May.
    15. D'Arcangelis, Anna Maria & Rotundo, Giulia, 2021. "Herding in mutual funds: A complex network approach," Journal of Business Research, Elsevier, vol. 129(C), pages 679-686.
    16. SHIMAMOTO Daichi & Yu Ri KIM & TODO Yasuyuki, 2019. "The Effect of Social Interactions on Exporting Activities: Evidence from Micro, Small, and Medium-Sized Enterprises in rural Vietnam," Discussion papers 19020, Research Institute of Economy, Trade and Industry (RIETI).
    17. G. Rejikumar & Aswathy Asokan-Ajitha & Sofi Dinesh & Ajay Jose, 2022. "The role of cognitive complexity and risk aversion in online herd behavior," Electronic Commerce Research, Springer, vol. 22(2), pages 585-621, June.
    18. Philippe Jehiel, 2022. "Analogy-Based Expectation Equilibrium and Related Concepts:Theory, Applications, and Beyond," Working Papers halshs-03735680, HAL.
    19. George Kapetanios & James Mitchell & Yongcheol Shin, 2010. "A Nonlinear Panel Model of Cross-sectional Dependence," Working Papers 673, Queen Mary University of London, School of Economics and Finance.
    20. Wang, Peiwen & Chen, Minghua & Wu, Ji & Yan, Yuanyun, 2023. "Do peer effects matter in bank risk? Some cross-country evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).

    More about this item

    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:arx:papers:2210.01267. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.