IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i12p1906-d1418257.html
   My bibliography  Save this article

A Rumor Propagation Model Considering Media Effect and Suspicion Mechanism under Public Emergencies

Author

Listed:
  • Shan Yang

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

  • Shihan Liu

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

  • Kaijun Su

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

  • Jianhong Chen

    (School of Resource and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

In this paper, we collect the basic information data of online rumors and highly topical public opinions. In the research of the propagation model of online public opinion rumors, we use the improved SCIR model to analyze the characteristics of online rumor propagation under the suspicion mechanism at different propagation stages, based on considering the flow of rumor propagation. We analyze the stability of the evolution of rumor propagation by using the time-delay differential equation under the punishment mechanism. In this paper, the evolution of heterogeneous views with different acceptance and exchange thresholds is studied, using the standard Deffuant model and the improved model under the influence of the media, to analyze the evolution process and characteristics of rumor opinions. Based on the above results, it is found that improving the recovery rate is better than reducing the deception rate, and increasing the eviction rate is better than improving the detection rate. When the time lag τ < 110, it indicates that the spread of rumors tends to be asymptotic and stable, and the punishment mechanism can reduce the propagation time and the maximum proportion of deceived people. The proportion of deceived people increases with the decrease in the exchange threshold, and the range of opinion clusters increases with the decline in acceptance.

Suggested Citation

  • Shan Yang & Shihan Liu & Kaijun Su & Jianhong Chen, 2024. "A Rumor Propagation Model Considering Media Effect and Suspicion Mechanism under Public Emergencies," Mathematics, MDPI, vol. 12(12), pages 1-23, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1906-:d:1418257
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/12/1906/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/12/1906/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zan, Yongli, 2018. "DSIR double-rumors spreading model in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 191-202.
    2. Zhu, Linhe & Guan, Gui, 2019. "Dynamical analysis of a rumor spreading model with self-discrimination and time delay in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    3. Zhang, Ziyu & Mei, Xuehui & Jiang, Haijun & Luo, Xupeng & Xia, Yang, 2023. "Dynamical analysis of Hyper-SIR rumor spreading model," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    4. Hongmei Wang & Liqing Qiu & Chengai Sun, 2023. "ISRT rumor spreading model with different influence mechanisms in social networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-18, January.
    5. Afassinou, Komi, 2014. "Analysis of the impact of education rate on the rumor spreading mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 43-52.
    6. Zhao, Laijun & Wang, Jiajia & Chen, Yucheng & Wang, Qin & Cheng, Jingjing & Cui, Hongxin, 2012. "SIHR rumor spreading model in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2444-2453.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tan, Jipeng & Zhang, Man & Liu, Fengming, 2024. "Online-Offline Higher-Order Rumor Propagation Model Based on Quantum Cellular Automata Considering Social Adaptation," Applied Mathematics and Computation, Elsevier, vol. 461(C).
    2. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.
    3. Lu, Peng & Deng, Liping & Liao, Hongbing, 2019. "Conditional effects of individual judgment heterogeneity in information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 335-344.
    4. Yao Hongxing & Zou Yushi, 2019. "Research on Rumor Spreading Model with Time Delay and Control Effect," Journal of Systems Science and Information, De Gruyter, vol. 7(4), pages 373-389, August.
    5. Li, Dandan & Ma, Jing, 2017. "How the government’s punishment and individual’s sensitivity affect the rumor spreading in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 284-292.
    6. Hosni, Adil Imad Eddine & Li, Kan & Ahmad, Sadique, 2020. "Analysis of the impact of online social networks addiction on the propagation of rumors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    7. Jianhong Chen & Hongcai Ma & Shan Yang, 2023. "SEIOR Rumor Propagation Model Considering Hesitating Mechanism and Different Rumor-Refuting Ways in Complex Networks," Mathematics, MDPI, vol. 11(2), pages 1-22, January.
    8. Zhang, Jing & Wang, Xiaoli & Xie, Yanxi & Wang, Meihua, 2022. "Research on multi-topic network public opinion propagation model with time delay in emergencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    9. Ma, Jing & Zhu, He, 2018. "Rumor diffusion in heterogeneous networks by considering the individuals’ subjective judgment and diverse characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 276-287.
    10. Yin, Fulian & Pang, Hongyu & Xia, Xinyu & Shao, Xueying & Wu, Jianhong, 2021. "COVID-19 information contact and participation analysis and dynamic prediction in the Chinese Sina-microblog," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    11. Javier Cifuentes-Faura & Ursula Faura-Martínez & Matilde Lafuente-Lechuga, 2022. "Mathematical Modeling and the Use of Network Models as Epidemiological Tools," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    12. Li, Ming & Zhang, Hong & Georgescu, Paul & Li, Tan, 2021. "The stochastic evolution of a rumor spreading model with two distinct spread inhibiting and attitude adjusting mechanisms in a homogeneous social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    13. Pan, Cheng & Yang, Lu-Xing & Yang, Xiaofan & Wu, Yingbo & Tang, Yuan Yan, 2018. "An effective rumor-containing strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 80-91.
    14. Huo, Liang’an & Cheng, Yingying & Liu, Chen & Ding, Fan, 2018. "Dynamic analysis of rumor spreading model for considering active network nodes and nonlinear spreading rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 24-35.
    15. Cheng, Yingying & Huo, Liang'an & Zhao, Laijun, 2022. "Stability analysis and optimal control of rumor spreading model under media coverage considering time delay and pulse vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    16. Li, Jiarong & Jiang, Haijun & Yu, Zhiyong & Hu, Cheng, 2019. "Dynamical analysis of rumor spreading model in homogeneous complex networks," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 374-385.
    17. Fu, Minglei & Feng, Jun & Lande, Dmytro & Dmytrenko, Oleh & Manko, Dmytro & Prakapovich, Ryhor, 2021. "Dynamic model with super spreaders and lurker users for preferential information propagation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    18. Zhao, Zhen-jun & Liu, Yong-mei & Wang, Ke-xi, 2016. "An analysis of rumor propagation based on propagation force," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 263-271.
    19. Huo, Liang’an & Cheng, Yingying, 2019. "Dynamical analysis of a IWSR rumor spreading model with considering the self-growth mechanism and indiscernible degree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    20. Lan, Yuexin & Lian, Zhixuan & Zeng, Runxi & Zhu, Di & Xia, Yixue & Liu, Mo & Zhang, Peng, 2020. "A statistical model of the impact of online rumors on the information quantity of online public opinion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1906-:d:1418257. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.