IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v603y2022ics0378437122004459.html
   My bibliography  Save this article

An agent-based model of opinion dynamics with attitude-hiding behaviors

Author

Listed:
  • Zhu, Jiefan
  • Yao, Yiping
  • Tang, Wenjie
  • Zhang, Haoming

Abstract

People might remain silent or give an opinion that is inconsistent with their private convictions when there is a divergence between their own views and the opinion voiced in their surrounding environment. This behavior suppresses the spread of weak opinions and affects the evolution of public opinion. Current opinion dynamics models do not fully consider this practice of attitude hiding. Thus, they are unable to reflect the phenomenon caused by individuals keeping silent. To solve this problem, based on online social networks, we propose an opinion dynamics model considering attitude-hiding behavior. Individuals perceive opinion climate based on feedbacks of expressions in history and use it to assess popularity of their newly opinions. They may keep silent and adjust expressible opinions to show obedience to the opinion climate. Experimental results and comparison with two related models indicate that our model can simulate the evolution of multiple public opinions associated with reality. Consequently, it provides a clearer explanation of the law and cause of the evolution of public opinion from the viewpoint of individuals’ behaviors.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004459
    DOI: 10.1016/j.physa.2022.127662
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122004459
    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.2022.127662?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. 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.
    2. Luo, Yun & Li, Yuke & Sun, Chudi & Cheng, Chun, 2022. "Adapted Deffuant–Weisbuch model with implicit and explicit opinions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    3. Hossein Noorazar & Kevin R. Vixie & Arghavan Talebanpour & Yunfeng Hu, 2020. "From classical to modern opinion dynamics," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(07), pages 1-60, July.
    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. 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.
    6. 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.
    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. Shang, Lihui & Zhao, Mingming & Ai, Jun & Su, Zhan, 2021. "Opinion evolution in the Sznajd model on interdependent chains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. 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.
    3. 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).
    4. 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.
    5. Toth, Gabor & Galam, Serge, 2022. "Deviations from the majority: A local flip model," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    6. 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).
    7. 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.
    8. Agnieszka Kowalska-Styczeń & Krzysztof Malarz, 2020. "Noise induced unanimity and disorder in opinion formation," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-22, July.
    9. Wang, Chaoqian, 2021. "Opinion dynamics with bilateral propaganda and unilateral information blockade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    10. Muslim, Roni & Wella, Sasfan A. & Nugraha, Ahmad R.T., 2022. "Phase transition in the majority rule model with the nonconformist agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    11. 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.
    12. 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.
    13. Shane T. Mueller & Yin-Yin Sarah Tan, 2018. "Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization," Journal of Computational Social Science, Springer, vol. 1(1), pages 15-48, January.
    14. Castro, Luis E. & Shaikh, Nazrul I., 2018. "A particle-learning-based approach to estimate the influence matrix of online social networks," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 1-18.
    15. 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.
    16. Yaofeng Zhang & Renbin Xiao, 2015. "Modeling and Simulation of Polarization in Internet Group Opinions Based on Cellular Automata," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-15, August.
    17. 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).
    18. Li, Tingyu & Zhu, Hengmin, 2020. "Effect of the media on the opinion dynamics in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    19. 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).
    20. 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.

    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:603:y:2022:i:c:s0378437122004459. 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.