Modeling the Co-evolving Polarization of Opinion and News Propagation Structure in Social Media

  • Hafizh Adi PrasetyaEmail author
  • Tsuyoshi Murata
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)


The recent visibility of polarization in online social media is an alarming phenomenon with plausible detriments to the health of our democratic process and discussion online. It prompted a new wave of interest in works that attempt to seek explanation to it via opinion modeling. In this paper, we offer one of such explanations by proposing a polarizing opinion model built on the idea of how news sharing online changes our opinion. By considering news propagation as the vehicle to polarization, we are able to see how the polarization of opinion intermingles with the polarized structure of news propagation found in numerous empirical works, something that has been missing from previous opinion models. The model polarized on exposure to polarized news of a reasonable degree but converges otherwise. We also performed systematic exploration of the model parameters and discuss how the behavior of the model mimics the behavior found in real social media.


Online polarization Opinion modeling News propagation 



This work was supported by JSPS Grant-in-Aid for Scientific Research(B) (Grant Number 17H01785), JST CREST (Grant Number JPMJCR1687), and Indonesia Endowment Fund for Education.


  1. 1.
    Anderson, M., et al.: Activism in the social media age. Pew Internet & American Life Project. Available via Pew Internet. (2018). Cited 12 Aug 2018
  2. 2.
    Bakshy, E., Messing, S., Adamic, L.A.: Exposure to ideologically diverse news and opinion on facebook. Science 348(6239), 1130–1132 (2015)Google Scholar
  3. 3.
    Banisch, S., Eckehard, O.: Opinion polarization by learning from social feedback. Available via arXiv preprint. arXiv:1704.02890v2 [physics.soc-ph] (2017). Cited 20 Aug 2018
  4. 4.
    Bessi, A., et al.: Users polarization on facebook and youtube. PloS one 11(8), e0159641 (2016)Google Scholar
  5. 5.
    Boutet, A., Kim, H., Yoneki, E.: Whats in twitter, i know what parties are popular and who you are supporting now!. Soc. Netw. Anal. Min. 3(4), 1379–1391 (2013)Google Scholar
  6. 6.
    Castelló, X., Baronchelli, A., Loreto, V.: Consensus and ordering in language dynamics. Eur. Phys. J. B 71(4), 557–564 (2009)Google Scholar
  7. 7.
    Clifford, Peter, Sudbury, Aidan: A model for spatial conflict. Biometrika 60(3), 581–588 (1973)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Conover, M., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., Flammini, A.: Political polarization on twitter. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. The AAAI Press, Menlo Park, California (2011)Google Scholar
  9. 9.
    DeGroot, M.H.: Reaching a consensus. J. Am. Stat. Assoc. 69(345), 118–121 (1974)Google Scholar
  10. 10.
    Feldman, L.: The opinion factor: the effects of opinionated news on information processing and attitude change. Polit. Commun. 28(2), 163–181 (2011)Google Scholar
  11. 11.
    Feller, A., Kuhnert, M., Sprenger, T.O., Welpe, I.M.: Divided they tweet: the network structure of political microbloggers and discussion topics. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. The AAAI Press, Menlo Park, California (2011)Google Scholar
  12. 12.
    Flache, A., Macy, M.W.: Small worlds and cultural polarization. J. Math. Sociol. 35(1–3), 146–176 (2011)Google Scholar
  13. 13.
    Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78(6), 1360–1380 (1973)Google Scholar
  14. 14.
    Gregg, B.D.: Frequency trails: modes and modality. Available online. (2018). Cited 20 Aug 2018
  15. 15.
    Guerra, P.H.C., Meira Jr,W., Cardie, C., Kleinberg, R.: A measure of polarization on social media networks based on community boundaries. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media. The AAAI Press, Menlo Park, California (2013)Google Scholar
  16. 16.
    Himelboim, I., McCreery, S., Smith, M.: Birds of a feather tweet together: integrating network and content analyses to examine cross-ideology exposure on twitter. J. Comput-Mediat. Commun. 18(2), 154–174 (2013)Google Scholar
  17. 17.
    Jones, D.A.: The polarizing effect of new media messages. Int. J. Public Opin. Res. 14(2), 158–174 (2002)Google Scholar
  18. 18.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. ACM New York, New York (2003)Google Scholar
  19. 19.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)CrossRefGoogle Scholar
  20. 20.
    Lorenz, J.: Continuous opinion dynamics under bounded confidence: a survey. Int. J. Mod. Phys. C 18(12), 1819–1838 (2007)Google Scholar
  21. 21.
    Malarz, K., Gronek, P., Krzysztof K.: Zaller-Deffuant model of mass opinion. J. Artif. Soc. Soc. Simul. 14, 1 (2011)Google Scholar
  22. 22.
    Mäs, M., Flache, A.: Differentiation without distancing. Explaining bi-polarization of opinions without negative influence. PloS one. 8(11), e74516 (2013)Google Scholar
  23. 23.
    Mobilia, M.: Does a single zealot affect an infinite group of voters? Phys. Rev. Lett. 91(2), 028701 (2003)Google Scholar
  24. 24.
    Nyczka, P., Sznajd-Weron, K.: Anticonformity or independence? Insights from statistical physics. J. Stat. Phys. 151(1–2), 174–202 (2013)Google Scholar
  25. 25.
    Rychwalska, A., Magdalena R.-K.: Polarization on social media: when group dynamics leads to societal divides. Hawaii International Conference on System Sciences 2018. Hawaii (2018)Google Scholar
  26. 26.
    Shearer, E., Jeffrey G.: News Use Across Social Media Platforms 2017. Pew Research Center. Available via Journalism. (2017). Cited 12 Aug 2018
  27. 27.
    Sîrbu, A., et al. Algorithmic bias amplifies opinion polarization: a bounded confidence model. arXiv:1803.02111v1 [physics.soc-ph] (2018). Cited 20 Aug 2018
  28. 28.
    Sobkowicz, P.: Modelling opinion formation with physics tools: call for closer link with reality. J. Artif. Soc. Soc. Simul. 12(1), 11 (2009)Google Scholar
  29. 29.
    Sobkowicz, P.: Extremism without extremists: Deffuant model with emotions. Frontiers. Physics 3, 17 (2015)Google Scholar
  30. 30.
    Sunstein, Cass R.: The law of group polarization. J. Polit. Philos. 10(2), 175–195 (2002)CrossRefGoogle Scholar
  31. 31.
    Sunstein, C.R.: 2.0. Princeton University Press, Princeton, New Jersey (2007)Google Scholar
  32. 32.
    Sznajd-Weron, Katarzyna, Sznajd, Jozef: Opinion evolution in closed community. Int. J. Mod. Phys. C 11(06), 1157–1165 (2000)CrossRefGoogle Scholar
  33. 33.
    Sznajd-Weron, K., Tabiszewski, M., Timpanaro, A.M.: Phase transition in the Sznajd model with independence. EPL (Europhys. Lett.) 96(4), 48002 (2011)CrossRefGoogle Scholar
  34. 34.
    Vicario, M.D., et al.: The spreading of misinformation online. Proc. Natl. Acad. Sci. 113(3), 554–559 (2016)CrossRefGoogle Scholar
  35. 35.
    Vicario, M.D., et al.: Mapping social dynamics on facebook: the Brexit debate. Soc. Netw. 50, 6–16 (2017)CrossRefGoogle Scholar
  36. 36.
    Villi, M., Matikainen, J., Khaldarova, I.: Recommend, tweet, share: User-distributed content (UDC) and the convergence of news media and social networks. Media Convergence Handbook-Vol, vol. 1, pp. 289–306. Springer, Berlin (2016)Google Scholar
  37. 37.
    Zollo, F., et al.: Debunking in a world of tribes. PloS one. 12(7), e0181821 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tokyo Institute of TechnologyMeguroJapan

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