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

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

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  • NETL
  • NSTL
  • Springer Nature
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 suscep-tible-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.

COVID-19 vaccineHeterogeneous networkInformation propagationDynamic modelSina Microblog

Fulian Yin、Jinxia Wang、Hongyu Pang、Xin Pei、Zhen Jin、Jianhong Wu

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State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, People's Republic of China||College of Information and Communication Engineering, Communication University of China, Beijing 100024, People's Republic of China

College of Information and Communication Engineering, Communication University of China, Beijing 100024, People's Republic of China

School of Mathematics, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China

Complex Systems Research Center, Shanxi University, Taiyuan 030006, People's Republic of China||Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan 030006, People's Republic of China

Fields-CQAM Laboratory of Mathematics for Public Health, Laboratory for Industrial and Applied Mathematics, York University, N613 Ross Bldg. 4700, Keele St., Toronto, ON M3J1P3, Canada

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2025

10.1007/s10588-024-09386-x

Computational & mathematical organization theory

Computational & mathematical organization theory

ISSN：1381-298X
年,卷(期)：2025.31(2)
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