IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v566y2021ics0378437120309444.html
   My bibliography  Save this article

Opinion dynamics with bilateral propaganda and unilateral information blockade

Author

Listed:
  • Wang, Chaoqian

Abstract

In the struggle for discourse power, different communities propagate different political values to their people, and some communities additionally adopt Internet censorship to block values that differ from their own. In blockaded communities, some people manage to bypass the blockade. Some of these people are no longer influenced by the propaganda of their communities and become independent, while others remain affected by it and are unswerving. On this basis, we propose an opinion dynamics model of two opposite groups with bilateral propaganda and a unilateral information blockade, and divide the population into four categories: the independent bypassing agents, the unswerving bypassing agents, the remaining agents in the blockaded group, and the agents in the blockade-free group. In the model, the opinion evolution follows the Deffuant–Weisbuch expression. The bilateral propaganda works in opinion evolution, while the unilateral information blockade works in networks of agents. We discuss the model in regard to two scenarios. First, we assume that the agents are well-mixed, deduce the Hegselmann–Krause expression of the model in the mean field, and obtain a theoretical solution. The results show that there are optimum levels of the intensity of bilateral propaganda and of the proportion of bypassing agents that enable the blockaded group to best control the discourse power, and the blockaded group is doomed to be unable to compete for the discourse power through institutional reform. Secondly, we assume that the agents follow scale-free networks which better model the Internet, and we acquire results by Monte Carlo simulations. The results show that there are optimum points of the intensity of bilateral propaganda and of the proportion of bypassing agents for the blockaded group or the blockade-free group to best control the discourse power in different situations. Moreover, the more independent agents there are, the closer the discourse power inclines towards the blockade-free group.

Suggested Citation

  • Wang, Chaoqian, 2021. "Opinion dynamics with bilateral propaganda and unilateral information blockade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    • Handle: RePEc:eee:phsmap:v:566:y:2021:i:c:s0378437120309444
    DOI: 10.1016/j.physa.2020.125646
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120309444
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.125646?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xi Chen & Shen Zhao & Wei Li, 2019. "Opinion Dynamics Model Based on Cognitive Styles: Field-Dependence and Field-Independence," Complexity, Hindawi, vol. 2019, pages 1-12, February.
    2. Gimenez, M.C. & Revelli, J.A. & Lama, M.S. de la & Lopez, J.M. & Wio, H.S., 2013. "Interplay between social debate and propaganda in an opinion formation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 278-286.
    3. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    4. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    5. Shang, Lihui & Chen, Shan, 2019. "Opinion dynamics with decentralized proportional–integral control strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    6. Fu, Guiyuan & Zhang, Weidong & Li, Zhijun, 2015. "Opinion dynamics of modified Hegselmann–Krause model in a group-based population with heterogeneous bounded confidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 558-565.
    7. Yan Liu & Lipo Mo, 2018. "Noise-Induced Truth Seeking of Heterogeneous Hegselmann-Krause Opinion Dynamics," Advances in Mathematical Physics, Hindawi, vol. 2018, pages 1-6, November.
    8. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    9. Cheng, Chun & Yu, Changbin, 2019. "Opinion dynamics with bounded confidence and group pressure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 532(C).
    10. Jędrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna, 2018. "Impact of memory on opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 306-315.
    11. Huang, Changwei & Dai, Qionglin & Han, Wenchen & Feng, Yuee & Cheng, Hongyan & Li, Haihong, 2018. "Effects of heterogeneous convergence rate on consensus in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 428-435.
    12. Guillaume Deffuant & David Neau & Frederic Amblard & Gérard Weisbuch, 2000. "Mixing beliefs among interacting agents," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 3(01n04), pages 87-98.
    13. Wang, Chaoqian, 2020. "Dynamics of conflicting opinions considering rationality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    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. Han, Wenchen & Gao, Shun & Huang, Changwei & Yang, Junzhong, 2022. "Non-consensus states in circular opinion model with repulsive interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    2. Han, Wenchen & Feng, Yuee & Qian, Xiaolan & Yang, Qihui & Huang, Changwei, 2020. "Clusters and the entropy in opinion dynamics on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    3. Glass, Catherine A. & Glass, David H., 2021. "Opinion dynamics of social learning with a conflicting source," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    4. Huang, Changwei & Hou, Yongzhao & Han, Wenchen, 2023. "Coevolution of consensus and cooperation in evolutionary Hegselmann–Krause dilemma with the cooperation cost," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    5. María Cecilia Gimenez & Luis Reinaudi & Ana Pamela Paz-García & Paulo Marcelo Centres & Antonio José Ramirez-Pastor, 2021. "Opinion evolution in the presence of constant propaganda: homogeneous and localized cases," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(1), pages 1-11, January.
    6. Lu, Xi & Mo, Hongming & Deng, Yong, 2015. "An evidential opinion dynamics model based on heterogeneous social influential power," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 98-107.
    7. Tiwari, Mukesh & Yang, Xiguang & Sen, Surajit, 2021. "Modeling the nonlinear effects of opinion kinematics in elections: A simple Ising model with random field based study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    8. Karataieva, Tatiana & Koshmanenko, Volodymyr & Krawczyk, Małgorzata J. & Kułakowski, Krzysztof, 2019. "Mean field model of a game for power," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 535-547.
    9. Quanbo Zha & Gang Kou & Hengjie Zhang & Haiming Liang & Xia Chen & Cong-Cong Li & Yucheng Dong, 2020. "Opinion dynamics in finance and business: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-22, December.
    10. Benjamin Cabrera & Björn Ross & Daniel Röchert & Felix Brünker & Stefan Stieglitz, 2021. "The influence of community structure on opinion expression: an agent-based model," Journal of Business Economics, Springer, vol. 91(9), pages 1331-1355, November.
    11. Hou, Jian & Li, Wenshan & Jiang, Mingyue, 2021. "Opinion dynamics in modified expressed and private model with bounded confidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    12. Calvelli, Matheus & Crokidakis, Nuno & Penna, Thadeu J.P., 2019. "Phase transitions and universality in the Sznajd model with anticonformity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 518-523.
    13. Takesue, Hirofumi, 2023. "Relative opinion similarity leads to the emergence of large clusters in opinion formation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    14. Zhu, Jiefan & Yao, Yiping & Tang, Wenjie & Zhang, Haoming, 2022. "An agent-based model of opinion dynamics with attitude-hiding behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    15. Ni, Xuelian & Xiong, Fei & Pan, Shirui & Chen, Hongshu & Wu, Jia & Wang, Liang, 2023. "How heterogeneous social influence acts on human decision-making in online social networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    16. Huang, Changwei & Dai, Qionglin & Han, Wenchen & Feng, Yuee & Cheng, Hongyan & Li, Haihong, 2018. "Effects of heterogeneous convergence rate on consensus in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 428-435.
    17. Catherine A. Glass & David H. Glass, 2021. "Social Influence of Competing Groups and Leaders in Opinion Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 799-823, October.
    18. Pérez-Llanos, Mayte & Pinasco, Juan Pablo & Saintier, Nicolas, 2020. "Opinion attractiveness and its effect in opinion formation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    19. Maciel, Marcelo V. & Martins, André C.R., 2020. "Ideologically motivated biases in a multiple issues opinion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    20. Xi Chen & Shen Zhao & Wei Li, 2019. "Opinion Dynamics Model Based on Cognitive Styles: Field-Dependence and Field-Independence," Complexity, Hindawi, vol. 2019, pages 1-12, February.

    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:eee:phsmap:v:566:y:2021:i:c:s0378437120309444. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.