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Modeling and analyzing network dynamics of COVID-19 vaccine information propagation in the Chinese Sina Microblog

Author

Listed:
  • Fulian Yin

    (Communication University of China
    Communication University of China)

  • Jinxia Wang

    (Communication University of China)

  • Hongyu Pang

    (Communication University of China)

  • Xin Pei

    (Taiyuan University of Technology)

  • Zhen Jin

    (Shanxi University
    Shanxi University)

  • Jianhong Wu

    (York University)

Abstract

Information about the vaccine is usually spread through heterogeneous networks in reality, where public opinion bursts out faster than in homogeneous networks. Considering the complexity of heterogeneous networks, we develop a network susceptible-forwarding-immune (NET-SFI) model to describe the patterns of information propagation in the actual social network. Classifying the states of nodes according to the number of users can contact in the social network, the NET-SFI model focuses on the network structure and user heterogeneity. We adopt a data-model drive method to conduct the model validation including two types of COVID-19 vaccine information from the Chinese Sina Microblog. Our parameter sensitivity analyses show the important significance of node degree in causing the outbreak of public opinion. Moreover, corresponding conclusions based on our analytic study are conducive to designing valid strategies for vaccine information dissemination.

Suggested Citation

  • Fulian Yin & Jinxia Wang & Hongyu Pang & Xin Pei & Zhen Jin & Jianhong Wu, 2025. "Modeling and analyzing network dynamics of COVID-19 vaccine information propagation in the Chinese Sina Microblog," Computational and Mathematical Organization Theory, Springer, vol. 31(2), pages 161-180, June.
    • Handle: RePEc:spr:comaot:v:31:y:2025:i:2:d:10.1007_s10588-024-09386-x
    DOI: 10.1007/s10588-024-09386-x
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