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Na?ve Learning in Social Networks with Fake News: Bots as a Singularity

Author

Listed:
  • Saeed Badri

    (Vrije Universiteit Amsterdam)

  • Bernd Heidergott

    (Vrije Universiteit Amsterdam)

  • Ines Lindner

    (Vrije Universiteit Amsterdam)

Abstract

We study the impact of bots on social learning in a social network setting. Regular agents receive independent noisy signals about the true value of a variable and then communicate in a network. They na¨?vely update beliefs by repeatedly taking weighted averages of neighbors’ opinions. Bots are agents in the network that spread fake news by disseminating biased information. Our main contributions are threefold. (1) We show that the consensus of the network is a mapping of the interaction rate between the agents and bots and is discontinuous at zero mass of bots. This implies that even a comparatively “infinitesimal” small number of bots still has a sizeable impact on the consensus and hence represents an obstruction to the “wisdom of crowds”. (2) We prove that the consensus gap induced by the marginal presence of bots depends neither on the agent network or bot layout nor on the assumed connection structure between agents and bots. (3) We show that before the ultimate (and bot-infected) consensus is reached, the network passes through a quasi-stationary phase which has the potential to mitigate the harmful impact of bots.

Suggested Citation

  • Saeed Badri & Bernd Heidergott & Ines Lindner, 2022. "Na?ve Learning in Social Networks with Fake News: Bots as a Singularity," Tinbergen Institute Discussion Papers 22-097/II, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20220097
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    References listed on IDEAS

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    1. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    2. Vincent Boucher, 2015. "Structural Homophily," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 235-264, February.
    3. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.
    4. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    5. Glenn Ellison & Drew Fudenberg, 1995. "Word-of-Mouth Communication and Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 93-125.
    6. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    7. Vincent Boucher, 2015. "Structural Homophily," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(1), pages 235-264, February.
    8. Kazutoshi Sasahara & Wen Chen & Hao Peng & Giovanni Luca Ciampaglia & Alessandro Flammini & Filippo Menczer, 2021. "Social influence and unfollowing accelerate the emergence of echo chambers," Journal of Computational Social Science, Springer, vol. 4(1), pages 381-402, May.
    9. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
    10. Ro'ee Levy, 2021. "Social Media, News Consumption, and Polarization: Evidence from a Field Experiment," American Economic Review, American Economic Association, vol. 111(3), pages 831-870, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Fake news; Misinformation; Social networks; Social Media; Wisdom of Crowds;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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