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“Fake news alert!”: A game of misinformation and news consumption behavior

Author

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  • Lodh, Rishab
  • Dey, Oindrila

Abstract

This paper examines the impact of behavioral factors in propagation of fake news. Using Spence (1978) framework, we find that the perfect Bayesian Nash equilibrium is pooling equilibrium, i.e., fake news producers to mimic actions of true news producer, which is influenced by factors like ideology, awareness, informational utility and fear of missing out information of news- consumers. Interestingly, the chain of fake news can be broken iff degree of awareness is significantly high. A threshold level of awareness level is determined using simulation, beyond which pooling breaks despite of high influence of other factors, which throws light on possible policy interventions.

Suggested Citation

  • Lodh, Rishab & Dey, Oindrila, 2023. "“Fake news alert!”: A game of misinformation and news consumption behavior," MPRA Paper 118371, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:118371
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    File URL: https://mpra.ub.uni-muenchen.de/118371/1/Fake%20News%20Alert%20sub%20%28LR%29.pdf
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    References listed on IDEAS

    as
    1. Garcia-Pires, Armando J. & Kind, Hans Jarle & Sørgard, Lars, 2017. "The effects of strategic news sources on media coverage," Information Economics and Policy, Elsevier, vol. 41(C), pages 28-35.
    2. Dani Rodrik, 2018. "Populism and the economics of globalization," Journal of International Business Policy, Palgrave Macmillan, vol. 1(1), pages 12-33, June.
    3. Francis Bloch & Gabrielle Demange & Rachel Kranton, 2018. "Rumors And Social Networks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 421-448, May.
    4. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    5. Rosie Graham, 2017. "Google and advertising: digital capitalism in the context of Post-Fordism, the reification of language, and the rise of fake news," Palgrave Communications, Palgrave Macmillan, vol. 3(1), pages 1-19, December.
    6. Ispano, Alessandro, 2018. "Information acquisition and the value of bad news," Games and Economic Behavior, Elsevier, vol. 110(C), pages 165-173.
    7. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    8. Xinyan Shi, 2013. "Information disclosure and vaccination externalities," International Journal of Economic Theory, The International Society for Economic Theory, vol. 9(3), pages 229-243, September.
    9. D. J. Flynn & Yusaku Horiuchi & Dong Zhang, 2022. "Misinformation, economic threat and public support for international trade," Review of International Political Economy, Taylor & Francis Journals, vol. 29(2), pages 571-597, March.
    10. Doron Levit, 2020. "Words Speak Louder without Actions," Journal of Finance, American Finance Association, vol. 75(1), pages 91-131, February.
    11. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    12. Matthew Ellman & Fabrizio Germano, 2009. "What do the Papers Sell? A Model of Advertising and Media Bias," Economic Journal, Royal Economic Society, vol. 119(537), pages 680-704, April.
    13. Rho, Sungmin & Tomz, Michael, 2017. "Why Don't Trade Preferences Reflect Economic Self-Interest?," International Organization, Cambridge University Press, vol. 71(S1), pages 85-108, April.
    14. Nuno Alvim & Tiago Pires, 2017. "Optimism and timing of market entry: How beliefs and information distortion create market leadership," International Journal of Economic Theory, The International Society for Economic Theory, vol. 13(3), pages 289-311, September.
    15. Callander, Steven & Wilkie, Simon, 2007. "Lies, damned lies, and political campaigns," Games and Economic Behavior, Elsevier, vol. 60(2), pages 262-286, August.
    16. Hon Foong Cheah, 2016. "Does foreign media entry discipline or provoke local media bias?," International Journal of Economic Theory, The International Society for Economic Theory, vol. 12(4), pages 335-359, December.
    17. Frederic Jenny, 2019. "Populism, Fairness and Competition: Should We Care and What Could We Do?," The Japanese Economic Review, Japanese Economic Association, vol. 70(3), pages 280-297, September.
    18. Frederic Jenny, 2019. "Populism, Fairness and Competition: Should we Care and What Could we do?," The Japanese Economic Review, Springer, vol. 70(3), pages 280-297, September.
    19. Badrinathan, Sumitra, 2021. "Educative Interventions to Combat Misinformation: Evidence from a Field Experiment in India," American Political Science Review, Cambridge University Press, vol. 115(4), pages 1325-1341, November.
    20. Gabriel Carroll & Georgy Egorov, 2019. "Strategic Communication With Minimal Verification," Econometrica, Econometric Society, vol. 87(6), pages 1867-1892, November.
    21. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
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    More about this item

    Keywords

    Fake news; Asymmetric Information; Bayesian games; Signaling; Fact checking;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General

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