IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0078433.html
   My bibliography  Save this article

Social Influence and the Collective Dynamics of Opinion Formation

Author

Listed:
  • Mehdi Moussaïd
  • Juliane E Kämmer
  • Pantelis P Analytis
  • Hansjörg Neth

Abstract

Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.

Suggested Citation

  • Mehdi Moussaïd & Juliane E Kämmer & Pantelis P Analytis & Hansjörg Neth, 2013. "Social Influence and the Collective Dynamics of Opinion Formation," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-8, November.
  • Handle: RePEc:plo:pone00:0078433
    DOI: 10.1371/journal.pone.0078433
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0078433
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0078433&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0078433?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Roger E. Kasperson & Ortwin Renn & Paul Slovic & Halina S. Brown & Jacque Emel & Robert Goble & Jeanne X. Kasperson & Samuel Ratick, 1988. "The Social Amplification of Risk: A Conceptual Framework," Risk Analysis, John Wiley & Sons, vol. 8(2), pages 177-187, June.
    3. Galam, Serge & Jacobs, Frans, 2007. "The role of inflexible minorities in the breaking of democratic opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 366-376.
    4. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    5. Pawel Sobkowicz, 2009. "Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-11.
    6. Iain D. Couzin & Jens Krause & Nigel R. Franks & Simon A. Levin, 2005. "Effective leadership and decision-making in animal groups on the move," Nature, Nature, vol. 433(7025), pages 513-516, February.
    7. Galam, Serge, 1997. "Rational group decision making: A random field Ising model at T = 0," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 238(1), pages 66-80.
    8. Jan Lorenz, 2007. "Continuous Opinion Dynamics Under Bounded Confidence: A Survey," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(12), pages 1819-1838.
    9. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    10. Fang Wu & Bernardo A. Huberman, 2004. "Social Structure and Opinion Formation," Computational Economics 0407002, University Library of Munich, Germany.
    11. Michael Mäs & Andreas Flache & Dirk Helbing, 2010. "Individualization as Driving Force of Clustering Phenomena in Humans," PLOS Computational Biology, Public Library of Science, vol. 6(10), pages 1-8, October.
    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. Mehdi Moussaïd, 2013. "Opinion Formation and the Collective Dynamics of Risk Perception," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-8, December.
    2. YunHong Zhang & QiPeng Liu & SiYing Zhang, 2017. "Opinion formation with time-varying bounded confidence," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
    3. Michael T Gastner & Nikolitsa Markou & Gunnar Pruessner & Moez Draief, 2014. "Opinion Formation Models on a Gradient," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, 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. Mehdi Moussaïd, 2013. "Opinion Formation and the Collective Dynamics of Risk Perception," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-8, December.
    2. Fan, Kangqi & Pedrycz, Witold, 2016. "Opinion evolution influenced by informed agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 431-441.
    3. Catherine A. Glass & David H. Glass, 2021. "Social Influence of Competing Groups and Leaders in Opinion Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 799-823, October.
    4. Tiwari, Mukesh & Yang, Xiguang & Sen, Surajit, 2021. "Modeling the nonlinear effects of opinion kinematics in elections: A simple Ising model with random field based study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    5. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    6. Shane T. Mueller & Yin-Yin Sarah Tan, 2018. "Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization," Journal of Computational Social Science, Springer, vol. 1(1), pages 15-48, January.
    7. Castro, Luis E. & Shaikh, Nazrul I., 2018. "A particle-learning-based approach to estimate the influence matrix of online social networks," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 1-18.
    8. Fan, Kangqi & Pedrycz, Witold, 2015. "Emergence and spread of extremist opinions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 87-97.
    9. Pawel Sobkowicz, 2009. "Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-11.
    10. Francisco J. León-Medina & Jordi Tena-Sánchez & Francisco J. Miguel, 2020. "Fakers becoming believers: how opinion dynamics are shaped by preference falsification, impression management and coherence heuristics," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 385-412, April.
    11. Song, Xiao & Shi, Wen & Tan, Gary & Ma, Yaofei, 2015. "Multi-level tolerance opinion dynamics in military command and control networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 322-332.
    12. Song, Xiao & Zhang, Shaoyun & Qian, Lidong, 2013. "Opinion dynamics in networked command and control organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5206-5217.
    13. AskariSichani, Omid & Jalili, Mahdi, 2015. "Influence maximization of informed agents in social networks," Applied Mathematics and Computation, Elsevier, vol. 254(C), pages 229-239.
    14. Kurmyshev, Evguenii & Juárez, Héctor A. & González-Silva, Ricardo A., 2011. "Dynamics of bounded confidence opinion in heterogeneous social networks: Concord against partial antagonism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2945-2955.
    15. Si, Xia-Meng & Liu, Yun & Xiong, Fei & Zhang, Yan-Chao & Ding, Fei & Cheng, Hui, 2010. "Effects of selective attention on continuous opinions and discrete decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3711-3719.
    16. Quanbo Zha & Gang Kou & Hengjie Zhang & Haiming Liang & Xia Chen & Cong-Cong Li & Yucheng Dong, 2020. "Opinion dynamics in finance and business: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-22, December.
    17. Benjamin Cabrera & Björn Ross & Daniel Röchert & Felix Brünker & Stefan Stieglitz, 2021. "The influence of community structure on opinion expression: an agent-based model," Journal of Business Economics, Springer, vol. 91(9), pages 1331-1355, November.
    18. Alexis Poindron, 2019. "A general model of synchronous updating with binary opinions," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02372486, HAL.
    19. Qian, Shen & Liu, Yijun & Galam, Serge, 2015. "Activeness as a key to counter democratic balance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 187-196.
    20. Muhammad Umar B. Niazi & A. Bülent Özgüler, 2021. "A Differential Game Model of Opinion Dynamics: Accord and Discord as Nash Equilibria," Dynamic Games and Applications, Springer, vol. 11(1), pages 137-160, March.

    More about this item

    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:plo:pone00:0078433. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.