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.
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Funding
The work was supported by the National Natural Science Foundation of China (No. 62372418); the Beijing Natural Science Foundation (No. 4232015); the State Key Laboratory of Media Convergence and Communication, Communication University of China; the Fundamental Research Funds for the Central Universities; the High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing (Internet Information, Communication University of China). JW was funded by the Natural Science and Engineering Research Council of Canada; and by the Canada Research Chair Program.
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Yin, F., Wang, J., Pang, H. et al. Modeling and analyzing network dynamics of COVID-19 vaccine information propagation in the Chinese Sina Microblog. Comput Math Organ Theory 31, 161–180 (2025). https://doi.org/10.1007/s10588-024-09386-x
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DOI: https://doi.org/10.1007/s10588-024-09386-x