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Social Media Networks, Fake News, and Polarization


  • Marina Azzimonti
  • Marcos Fernandes


We study how the structure of social media networks and the presence of fake news affects the degree of misinformation and polarization in a society. For that, we analyze a dynamic model of opinion exchange in which individuals have imperfect information about the true state of the world and exhibit bounded rationality. Key to the analysis is the presence of internet bots: agents in the network that spread fake news (e.g., a constant flow of biased information). We characterize how agents' opinions evolve over time and evaluate the determinants of long-run misinformation and polarization in the network. To that end, we construct a synthetic network calibrated to Twitter and simulate the information exchange process over a long horizon to quantify the bots' ability to spread fake news. A key insight is that significant misinformation and polarization arise in networks in which only 15% of agents believe fake news to be true, indicating that network externality effects are quantitatively important. Higher bot centrality typically increases polarization and lowers misinformation. When one bot is more influential than the other (asymmetric centrality), polarization is reduced but misinformation grows, as opinions become closer the more influential bot's preferred point. Finally, we show that threshold rules tend to reduce polarization and misinformation. This is because, as long as agents also have access to unbiased sources of information, threshold rules actually limit the influence of bots.

Suggested Citation

  • Marina Azzimonti & Marcos Fernandes, 2018. "Social Media Networks, Fake News, and Polarization," NBER Working Papers 24462, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24462
    Note: EFG POL

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    References listed on IDEAS

    1. Joan Esteban & Carlos Gradín & Debraj Ray, 2007. "An Extension of a Measure of Polarization, with an application to the income distribution of five OECD countries," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(1), pages 1-19, April.
    2. James Andreoni & Tymofiy Mylovanov, 2012. "Diverging Opinions," American Economic Journal: Microeconomics, American Economic Association, vol. 4(1), pages 209-232, 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. Marina Azzimonti-Renzo, 2014. "Partisan conflict," Working Papers 14-19, Federal Reserve Bank of Philadelphia.
    5. Levi Boxell & Matthew Gentzkow & Jesse M. Shapiro, 2017. "Is the Internet Causing Political Polarization? Evidence from Demographics," NBER Working Papers 23258, National Bureau of Economic Research, Inc.
    6. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
    7. Epstein Larry G & Noor Jawwad & Sandroni Alvaro, 2010. "Non-Bayesian Learning," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 10(1), pages 1-20, January.
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    Cited by:

    1. Hunt Allcott & Matthew Gentzkow & Chuan Yu, 2018. "Trends in the Diffusion of Misinformation on Social Media," Papers 1809.05901,
    2. Michele Cantarella & Nicolo' Fraccaroli & Roberto Volpe, 2019. "Does fake news affect voting behaviour?," Department of Economics 0146, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    3. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    4. Marina Azzimonti & Alessandra Fogli & Fabrizio Perri & Mark Ponder, 2020. "Pandemic Control in ECON-EPI Networks," NBER Working Papers 27741, National Bureau of Economic Research, Inc.
    5. Tatsuo Tanaka, 2019. "Does the Internet cause polarization? -Panel survey in Japan-," Keio-IES Discussion Paper Series 2019-015, Institute for Economics Studies, Keio University.
    6. Marcos Fernandes, 2019. "Confirmation Bias in Social Networks," Department of Economics Working Papers 19-05, Stony Brook University, Department of Economics.
    7. Jost, Peter J. & Pünder, Johanna & Schulze-Lohoff, Isabell, 2020. "Fake news - Does perception matter more than the truth?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 85(C).
    8. Hunt Allcott & Matthew Gentzkow & Chuan Yu, 2019. "Trends in the Diffusion of Misinformation on Social Media," NBER Working Papers 25500, National Bureau of Economic Research, Inc.
    9. Domenico, Giandomenico Di & Sit, Jason & Ishizaka, Alessio & Nunan, Daniel, 2021. "Fake news, social media and marketing: A systematic review," Journal of Business Research, Elsevier, vol. 124(C), pages 329-341.

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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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