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Multi-opinion based method for quantifying polarization on social networks

Author

Listed:
  • Maneet Singh

    (Indian Institute of Technology Ropar
    Gautam Buddha University)

  • S. R. S. Iyengar

    (Indian Institute of Technology Ropar)

  • Rishemjit Kaur

    (CSIR-Central Scientific Instruments Organization
    Academy of Scientific and Innovative Research)

Abstract

Social media platforms have emerged as a hub for political and social interactions, and analyzing the polarization of opinions has been gaining attention. In this study, we have proposed a measure to quantify polarization on social networks. The proposed metric, unlike state-of-the-art methods, does not assume a two-opinion case and applies to multiple opinions. We tested our metric on different networks with a multi-opinion scenario and varying degrees of polarization. The scores obtained from the proposed metric were comparable to state-of-the-art methods on binary opinion-based benchmark networks. The technique also differentiated among networks with different levels of polarization in a multi-opinion scenario. We also quantified polarization in a retweet network obtained from Twitter regarding the usage of drugs like hydroxychloroquine or chloroquine in treating COVID-19. Our metric indicated a high level of polarized opinions among the users. These findings suggest uncertainty among users in the benefits of using hydroxychloroquine and chloroquine drugs to treat COVID-19 patients.

Suggested Citation

  • Maneet Singh & S. R. S. Iyengar & Rishemjit Kaur, 2025. "Multi-opinion based method for quantifying polarization on social networks," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-17, August.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:3:d:10.1007_s42001-025-00400-z
    DOI: 10.1007/s42001-025-00400-z
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