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Coherence and polarization in complex networks

Author

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  • Babak Ravandi

    (Purdue University)

  • Fatma Mili

    (University of North Carolina at Charlotte)

Abstract

In a community, individuals hold opinions and views that are shaped in part by their sources of information and their social network. These opinions and views are rarely uniformly distributed through the possible spectrum. They can exhibit different patterns. We are, in particular, interested in two opposite patterns: polarization, where views are concentrated around extreme positions, and coherence, where views are closer to the center, more moderate positions. We seek to create a model of views evolution and their convergence towards one or another of the patterns. Furthermore, we focus on applying interventions to the structure of networks to demote the polarization issue and influence a network toward convergence of cohered views. Our results show adding links between the weakly-connected nodes reduces polarization, implying the weakly-connected nodes can form bridges between extreme views.

Suggested Citation

  • Babak Ravandi & Fatma Mili, 2019. "Coherence and polarization in complex networks," Journal of Computational Social Science, Springer, vol. 2(2), pages 133-150, July.
    • Handle: RePEc:spr:jcsosc:v:2:y:2019:i:2:d:10.1007_s42001-019-00036-w
    DOI: 10.1007/s42001-019-00036-w
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    References listed on IDEAS

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    Cited by:

    1. Tobias Blanke & Tommaso Venturini, 2022. "A network view on reliability: using machine learning to understand how we assess news websites," Journal of Computational Social Science, Springer, vol. 5(1), pages 69-88, May.

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