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Analysis and Control of Rumor Propagation Model Considering Multiple Waiting Phases

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  • Hai Wu

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China)

  • Xin Yan

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China)

  • Shengxiang Gao

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China)

  • Zhongying Deng

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)

  • Haiyang Chi

    (Information Center, Kunming University, Kunming 650214, China)

Abstract

Rumors pose serious harm to society and exhibit a certain degree of repetitiveness. Existing rumor propagation models often have simple rules and neglect the repetitiveness of rumors. Therefore, we propose a new SCWIR rumor propagation model (susceptible, commented, waited, infected, recovered) by introducing the user’s repeated waiting behavior to simulate the potential for rumors to lie dormant and spread opportunistically. First, we present the dynamic equations of the model, then introduce three influencing factors to improve the model. Next, by solving for the equilibrium points and the basic reproduction number, we discuss the local and global stability of the rumor-free/rumor equilibrium points. Finally, we perform numerical simulations to analyze the effects of different factors on rumor propagation. The results show that the introduction of the multiple waiting mechanism helps simulate the repetitiveness of rumor propagation. Among the rumor suppression strategies, the effectiveness, from highest to lowest, is as follows: government intervention, information dissemination and popularization, and accelerated rumor value decay, with government intervention playing a decisive role. Information dissemination can reduce the intensity of rumors at the source.

Suggested Citation

  • Hai Wu & Xin Yan & Shengxiang Gao & Zhongying Deng & Haiyang Chi, 2025. "Analysis and Control of Rumor Propagation Model Considering Multiple Waiting Phases," Mathematics, MDPI, vol. 13(2), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:312-:d:1570381
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    References listed on IDEAS

    as
    1. Yanchao Liu & Pengzhou Zhang & Lei Shi & Junpeng Gong, 2023. "A Survey of Information Dissemination Model, Datasets, and Insight," Mathematics, MDPI, vol. 11(17), pages 1-30, August.
    2. Qian Zhang & Xianyong Li & Yajun Du & Jian Zhu & Miguel à ngel López, 2021. "Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-12, June.
    3. Yanyi Nie & Liming Pan & Tao Lin & Wei Wang, 2022. "Information spreading on metapopulation networks with heterogeneous contacting," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 33(03), pages 1-13, March.
    4. Yao, Yao & Xiao, Xi & Zhang, Chengping & Dou, Changsheng & Xia, Shutao, 2019. "Stability analysis of an SDILR model based on rumor recurrence on social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
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