IDEAS home Printed from https://ideas.repec.org/p/chf/rpseri/rp2258.html
   My bibliography  Save this paper

Fake News in Social Networks

Author

Listed:
  • Christoph Aymanns

    (London School of Economics & Political Science (LSE) - London School of Economics; University of St. Gallen - School of Finance)

  • Jakob Foerster

    (University of Oxford)

  • Co-Pierre Georg

    (University of Cape Town; Deutsche Bundesbank)

  • Matthias Weber

    (University of St. Gallen - School of Finance; Swiss Finance Institute)

Abstract

We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in unaccustomed populations and in populations that have adapted to the presence of fake news. In particular the latter is challenging for existing methods. We find that a fake-news attack is more effective if it targets highly connected people and people with weaker private information. Attacks are more effective when the disinformation is spread across several agents than when the disinformation is concentrated with more intensity on fewer agents. Furthermore, fake news spread less well in balanced networks than in clustered networks. We test a part of these findings in a human-subject experiment. The experimental evidence provides support for the predictions from the model. This suggests that our model is suitable to analyze the spread of fake news in social networks.

Suggested Citation

  • Christoph Aymanns & Jakob Foerster & Co-Pierre Georg & Matthias Weber, 2022. "Fake News in Social Networks," Swiss Finance Institute Research Paper Series 22-58, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp2258
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4173312
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ardi Tampuu & Tambet Matiisen & Dorian Kodelja & Ilya Kuzovkin & Kristjan Korjus & Juhan Aru & Jaan Aru & Raul Vicente, 2017. "Multiagent cooperation and competition with deep reinforcement learning," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-15, April.
    2. Sanjeev Goyal & Stephanie Rosenkranz & Utz Weitzel & Vincent Buskens, 2017. "Information Acquisition and Exchange in Social Networks," Economic Journal, Royal Economic Society, vol. 127(606), pages 2302-2331, November.
    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. 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.
    5. Elchanan Mossel & Allan Sly & Omer Tamuz, 2015. "Strategic Learning and the Topology of Social Networks," Econometrica, Econometric Society, vol. 83(5), pages 1755-1794, September.
    6. Boris van Leeuwen & Theo Offerman & Arthur Schram, 2020. "Competition for Status Creates Superstars: an Experiment on Public Good Provision and Network Formation," Journal of the European Economic Association, European Economic Association, vol. 18(2), pages 666-707.
    7. 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.
    8. Sanjeev Goyal & Stephanie Rosenkranz & Utz Weitzel & Vincent Buskens, 2017. "Information Acquisition and Exchange in Social Networks," Economic Journal, Royal Economic Society, vol. 127(606), pages 2302-2331, November.
    9. Acemoglu, Daron & Ozdaglar, Asuman & ParandehGheibi, Ali, 2010. "Spread of (mis)information in social networks," Games and Economic Behavior, Elsevier, vol. 70(2), pages 194-227, November.
    10. Lesley Chiou & Catherine Tucker, 2018. "Fake News and Advertising on Social Media: A Study of the Anti-Vaccination Movement," NBER Working Papers 25223, National Bureau of Economic Research, Inc.
    11. Alexander J. Stewart & Mohsen Mosleh & Marina Diakonova & Antonio A. Arechar & David G. Rand & Joshua B. Plotkin, 2019. "Information gerrymandering and undemocratic decisions," Nature, Nature, vol. 573(7772), pages 117-121, September.
    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. Bertin Martens & Luis Aguiar & Estrella Gomez Herrera & Frank Muller, 2018. "The digital transformation of news media and the rise of disinformation and fake news," JRC Working Papers on Digital Economy 2018-02, Joint Research Centre.
    2. Andrew Cullen & Tansu Alpcan & Alexander Kalloniatis, 2025. "Game-Theoretic Analysis of Adversarial Decision Making in a Complex Socio-Physical System," Dynamic Games and Applications, Springer, vol. 15(3), pages 709-728, July.
    3. Shimon Kogan & Tobias J Moskowitz & Marina Niessner, 2023. "Social Media and Financial News Manipulation," Review of Finance, European Finance Association, vol. 27(4), pages 1229-1268.
    4. Bryan Fong, 2021. "Analysing the behavioural finance impact of 'fake news' phenomena on financial markets: a representative agent model and empirical validation," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.

    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. repec:osf:socarx:y4mkd_v1 is not listed on IDEAS
    2. Christoph Aymanns & Jakob Foerster & Co-Pierre Georg, 2017. "Fake News in Social Networks," Working Papers on Finance 1804, University of St. Gallen, School of Finance.
    3. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    4. 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.
    5. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    6. Jakob Grazzini & Domenico Massaro, 2021. "Dispersed information, social networks, and aggregate behavior," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 1129-1148, July.
    7. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    8. Gallo, E. & Langtry, A., 2020. "Social Networks, Confirmation Bias and Shock Elections," Cambridge Working Papers in Economics 2099, Faculty of Economics, University of Cambridge.
    9. Fang, Aili & Wang, Lin & Wei, Xinjiang, 2019. "Social learning with multiple true states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 375-386.
    10. Edoardo Gallo & Alastair Langtry, 2020. "Social networks, confirmation bias and shock elections," Papers 2011.00520, arXiv.org.
    11. Itai Arieli & Fedor Sandomirskiy & Rann Smorodinsky, 2020. "On social networks that support learning," Papers 2011.05255, arXiv.org.
    12. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    13. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    14. 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.
    15. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.
    16. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    17. Ionel Popescu & Tushar Vaidya, 2019. "Averaging plus Learning Models and Their Asymptotics," Papers 1904.08131, arXiv.org, revised Jul 2023.
    18. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska & Emily Tanimura, 2015. "Strategic influence in social networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01158168, HAL.
    19. Arieli, Itai & Koren, Moran & Smorodinsky, Rann, 2022. "The implications of pricing on social learning," Theoretical Economics, Econometric Society, vol. 17(4), November.
    20. Battiston, Pietro & Stanca, Luca, 2015. "Boundedly rational opinion dynamics in social networks: Does indegree matter?," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 400-421.
    21. Margherita Comola & Agnieszka Rusinowska & Marie Claire Villeval, 2024. "Competing for Influence in Networks Through Strategic Targeting [En compétition pour l'influence dans les réseaux grâce au ciblage stratégique]," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-04706311, HAL.

    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:chf:rpseri:rp2258. 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: Ridima Mittal (email available below). General contact details of provider: https://edirc.repec.org/data/fameech.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.