Skip to main content
SpringerLink
Log in

Multidimensional Model of Opinion Dynamics in Social Networks: Polarization Indices

  • CONTROL SCIENCES
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

We consider a multidimensional model of opinion dynamics in social networks. Within the framework of the model, the dynamics of two interconnected information processes in a social network is studied. The first process is the process of spreading of the excitation in the network of agents and their actions observed from outside (e.g., in the form of messages posted in social media). The second process, which has a connection with the first, is the formation of agents’ opinions (which are a characteristic of their internal state). We demonstrate that the proposed model of opinion dynamics is flexible and allows taking into account the significant effects of opinion formation in social networks, including consensus or agreement of opinions, preservation of differences in agents’ opinions, and even polarization of opinions. We propose approaches to measuring the polarization of opinions and present simulation results. We show that the proposed polarization index for a network allows one to distinguish and evaluate situations with meaningfully different multidimensional distributions of opinions in a social network as well as to find directions of the greatest polarization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Similar content being viewed by others

REFERENCES

  1. Chkhartishvili, A.G., Gubanov, D.A., and Novikov, D.A., Social Networks: Models of Information Influence, Control and Confrontation, Cham, Switzerland: Springer Int. Publ., 2019.

    Book  Google Scholar 

  2. De Groot, M.H., Reaching a consensus, J. Am. Stat. Assoc., 1974, no. 69, pp. 118–121.

  3. Kozitsin, I.V. and Belolipetskii, A.A., Opinion convergence in the Krasnoshchekov model, J. Math. Sociol., 2019, vol. 43, no. 2, pp. 104–121.

    Article  MathSciNet  Google Scholar 

  4. Myers, D.G., Social Psychology, New York: McGraw-Hill, 1999.

    Google Scholar 

  5. Deffuant, G., Neau, D., Amblard, F., and Weisbuch, G., Mixing beliefs among interacting agents, Adv. Complex Syst., 2000, vol. 03, pp. 87–98.

    Article  Google Scholar 

  6. Parsegov, S.E., Proskurnikov, A.V., Tempo, R., and Friedkin, N.E., Novel multidimensional models of opinion dynamics in social networks, IEEE Trans. Autom. Control, 2017, vol. 62, no. 5, pp. 2270–2285.

    Article  MathSciNet  Google Scholar 

  7. Friedkin, N.E., Proskurnikov, A.V., Tempo, R., and Parsegov, S.E., Network science on belief system dynamics under logic constraints, Science, 2016, vol. 354, no. 6310, pp. 321–326.

    Article  MathSciNet  Google Scholar 

  8. Altafini, C., Consensus problems on networks with antagonistic interactions, IEEE Trans. Autom. Control, 2014, vol. 58, no. 4, pp. 935–946.

    Article  MathSciNet  Google Scholar 

  9. Petrov, A. and Proncheva, O., Modeling propaganda battle: decision-making, homophily, and echo chambers, Conf. Artif. Intell. Nat. Lang. (2018), pp. 197–209.

  10. Sirbu, A., Pedreschi, D., Giannotti, F., and Kertész, J., Algorithmic bias amplifies opinion fragmentation and polarization: a bounded confidence model, PLOS ONE, 2019, no. 3(14), pp. e0213246–. https://doi.org/10.1371/journal.pone.0213246

  11. Chkhartishvili, A.G. and Gubanov, D.A., A conceptual approach to online social networks analysis, Autom. Remote Control, 2015, vol. 76, no. 8, pp. 1455–1462.

    Article  MathSciNet  Google Scholar 

  12. Chkhartishvili, A.G. and Gubanov, D.A., Influence levels of users and meta-users of a social network, Autom. Remote Control, 2018, vol. 79, no. 3, pp. 545–553.

    Article  MathSciNet  Google Scholar 

  13. Hegselmann, R. and Krause, U., Opinion dynamics and bounded confidence models, analysis and simulation, J. Artif. Soc. Social Simul., 2002. vol. 5. no. 3.

  14. Hegselmann, R. and Krause, U., Opinion dynamics under the influence of radical groups, charismatic leaders, and other constant signals: a simple unifying model, Networks Heterog. Media, 2015, vol. 10, no. 3, pp. 477–509.

    Article  MathSciNet  Google Scholar 

  15. Jager, W. and Amblard, F., Uniformity, bipolarization and pluriformity captured as generic stylized behavior with an agent-based simulation model of attitude change, Comput. & Math. Org. Theory, 2005, vol. 10, no. 4, pp. 295–303.

    Article  Google Scholar 

  16. Maes, M. and Bischofberger, L., Will the Personalization of Online Social Networks Foster Opinion Polarization?, 2015. Available at SSRN 2553436.2015.

  17. Takács, K., Flache, A., and Mäs, M., Discrepancy and disliking do not induce negative opinion shifts, PLOS ONE, 2016, vol. 11, no. 6, p. e0157948.

  18. Gentzkow, M., Polarization in 2016, in Toulouse Network for Information Technology Whitepaper, 2016.

  19. Abramowitz, A.I. and Saunders, K.L., Is polarization a myth?, J. Polit., 2008, vol. 70, no. 2, pp. 542–555.

    Article  Google Scholar 

  20. Esteban, J.-M. and Ray, D., On the measurement of polarization, Econ. J. Econ. Soc., 1994, vol. 62, no. 4, pp. 819–851.

    MATH  Google Scholar 

  21. Aleskerov, F.T. and Golubenko, M.A., On the evaluation of a symmetry of political views and polarization of society. Working paper WP7/2003/04, Preprint of Higher School Econ., Moscow, 2003.

  22. Gubanov, D.A. and Petrov, I.V., Multidimensional model of opinion polarization in social networks, in Proc. 12th Int. Conf. “Management of Large-Scale System Development” (MLSD), Moscow: IEEE, 2019, pp. 1200–1202. https://ieeexplore.ieee.org/document/8910967

Download references

Funding

This work was supported by the Russian Foundation of Basic Research, project no. 18-29-22042.

Author information

Authors and Affiliations

  1. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, 117997, Russia

    D. A. Gubanov, I. V. Petrov & A. G. Chkhartishvili

Authors
  1. D. A. Gubanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. V. Petrov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. G. Chkhartishvili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to D. A. Gubanov, I. V. Petrov or A. G. Chkhartishvili.

Additional information

Translated by V. Potapchouck

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gubanov, D.A., Petrov, I.V. & Chkhartishvili, A.G. Multidimensional Model of Opinion Dynamics in Social Networks: Polarization Indices. Autom Remote Control 82, 1802–1811 (2021). https://doi.org/10.1134/S0005117921100167

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117921100167

Keywords

Search

Navigation