IDEAS home Printed from https://ideas.repec.org/p/rco/dpaper/57.html
   My bibliography  Save this paper

The Impact of Social Media On Belief Formation

Author

Listed:
  • Schwarz, Marco A.

    (University of Innsbruck)

Abstract

Social media are becoming increasingly important in our society and change the way people communicate, how they acquire information, and how they form beliefs. Experts are concerned that the rise of social media may make interaction and information exchange among like-minded individuals more pronounced and therefore lead to increased disagreement in a society. This paper analyzes a learning model with endogenous network formation in which people have different types and live in different regions. I show that when the importance of social media increases, the amount of disagreement in the society first decreases and then increases. Simultaneously people of the same type hold increasingly similar beliefs. Furthermore, people who find it hard to communicate with people in the same region may interact with similar people online and consequently hold extreme beliefs. Finally, I propose a simple way to model people who neglect a potential correlation of signals and show that these people may be made worse off by social media.

Suggested Citation

  • Schwarz, Marco A., 2017. "The Impact of Social Media On Belief Formation," Rationality and Competition Discussion Paper Series 57, CRC TRR 190 Rationality and Competition.
  • Handle: RePEc:rco:dpaper:57
    as

    Download full text from publisher

    File URL: https://rationality-and-competition.de/wp-content/uploads/discussion_paper/57.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ströbel, Johannes & Kuchler, Theresa & Bailey, Michael & Cao, Ruiqing, 2016. "Social Networks and Housing Markets," CEPR Discussion Papers 11272, C.E.P.R. Discussion Papers.
    2. Oliver Falck & Robert Gold & Stephan Heblich, 2014. "E-lections: Voting Behavior and the Internet," American Economic Review, American Economic Association, vol. 104(7), pages 2238-2265, July.
    3. Bohren, J. Aislinn, 2016. "Informational herding with model misspecification," Journal of Economic Theory, Elsevier, vol. 163(C), pages 222-247.
    4. Song, Yangbo, 2016. "Social learning with endogenous observation," Journal of Economic Theory, Elsevier, vol. 166(C), pages 324-333.
    5. Kfir Eliaz & Ran Spiegler, 2006. "Contracting with Diversely Naive Agents," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(3), pages 689-714.
    6. Poy, Samuele & Schüller, Simone, 2016. "Internet and Voting in the Web 2.0 Era: Evidence from a Local Broadband Policy," IZA Discussion Papers 9991, Institute of Labor Economics (IZA).
    7. Markus K. Brunnermeier & Alp Simsek & Wei Xiong, 2014. "A Welfare Criterion For Models With Distorted Beliefs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1753-1797.
    8. Florian Englmaier & Matthias Fahn & Marco A. Schwarz, 2016. "Long-Term Employment Relations when Agents are Present Biased," CESifo Working Paper Series 6159, CESifo.
    9. Stefano DellaVigna & Ulrike Malmendier, 2004. "Contract Design and Self-Control: Theory and Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(2), pages 353-402.
    10. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    11. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.
    12. , & , & ,, 2014. "Dynamics of information exchange in endogenous social networks," Theoretical Economics, Econometric Society, vol. 9(1), January.
    13. 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.
    14. Laurel Harbridge & Neil Malhotra, 2011. "Electoral Incentives and Partisan Conflict in Congress: Evidence from Survey Experiments," American Journal of Political Science, John Wiley & Sons, vol. 55(3), pages 494-510, July.
    15. Edward L. Glaeser & Cass R. Sunstein, 2013. "Why Does Balanced News Produce Unbalanced Views?," NBER Working Papers 18975, National Bureau of Economic Research, Inc.
    16. Watts, Alison, 2001. "A Dynamic Model of Network Formation," Games and Economic Behavior, Elsevier, vol. 34(2), pages 331-341, February.
    17. Matthew Gentzkow & Jesse M. Shapiro, 2011. "Ideological Segregation Online and Offline," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(4), pages 1799-1839.
    18. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    19. Matthew Rabin & Joel L. Schrag, 1999. "First Impressions Matter: A Model of Confirmatory Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 37-82.
    20. 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.
    21. Hojman, Daniel A. & Szeidl, Adam, 2008. "Core and periphery in networks," Journal of Economic Theory, Elsevier, vol. 139(1), pages 295-309, March.
    Full references (including those not matched with items on IDEAS)

    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. Marcos Fernandes, 2019. "Confirmation Bias in Social Networks," Department of Economics Working Papers 19-05, Stony Brook University, Department of Economics.
    2. Jan Hązła & Ali Jadbabaie & Elchanan Mossel & M. Amin Rahimian, 2021. "Bayesian Decision Making in Groups is Hard," Operations Research, INFORMS, vol. 69(2), pages 632-654, March.
    3. Marcos Ross Fernandes, 2023. "Confirmation Bias in Social Networks," Working Papers, Department of Economics 2023_02, University of São Paulo (FEA-USP).
    4. Marcos R. Fernandes, 2022. "Confirmation Bias in Social Networks," Papers 2207.12594, arXiv.org, revised Feb 2023.
    5. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    6. , & ,, 2015. "Information diffusion in networks through social learning," Theoretical Economics, Econometric Society, vol. 10(3), September.
    7. 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.
    8. Arieli, Itai & Babichenko, Yakov & Shlomov, Segev, 2021. "Virtually additive learning," Journal of Economic Theory, Elsevier, vol. 197(C).
    9. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    10. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    11. Sebastiano Della Lena, 2019. "Non-Bayesian Social Learning and the Spread of Misinformation in Networks," Working Papers 2019:09, Department of Economics, University of Venice "Ca' Foscari".
    12. Pooya Molavi & Ceyhun Eksin & Alejandro Ribeiro & Ali Jadbabaie, 2016. "Learning to Coordinate in Social Networks," Operations Research, INFORMS, vol. 64(3), pages 605-621, June.
    13. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    14. 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.
    15. Aymanns, Christoph & Georg, Co-Pierre, 2015. "Contagious synchronization and endogenous network formation in financial networks," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 273-285.
    16. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    17. Bohren, Aislinn & Hauser, Daniel, 2017. "Learning with Heterogeneous Misspecified Models: Characterization and Robustness," CEPR Discussion Papers 12036, C.E.P.R. Discussion Papers.
    18. Aislinn Bohren & Daniel Hauser, 2018. "Social Learning with Model Misspeciification: A Framework and a Robustness Result," PIER Working Paper Archive 18-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Jul 2018.
    19. Bogaçhan Çelen & Sen Geng & Huihui Li, 2018. "Belief Error and Non-Bayesian Social Learning: An Experimental Evidence," GRU Working Paper Series GRU_2018_022, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    20. Foerster, Manuel, 2019. "Dynamics of strategic information transmission in social networks," Theoretical Economics, Econometric Society, vol. 14(1), January.

    More about this item

    Keywords

    social media; network formation; social learning; polarization; homophily; correlation neglect;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • Z10 - Other Special Topics - - Cultural Economics - - - General
    • Z19 - Other Special Topics - - Cultural Economics - - - Other

    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:rco:dpaper:57. 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: Viviana Lalli (email available below). General contact details of provider: https://rationality-and-competition.de .

    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.