IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v523y2019icp21-39.html
   My bibliography  Save this article

An evolutionary game model for analysis of rumor propagation and control in social networks

Author

Listed:
  • Askarizadeh, Mojgan
  • Tork Ladani, Behrouz
  • Manshaei, Mohammad Hossein

Abstract

Nowadays, social networks are widely used as fast and ubiquitous media for sharing information. Rumor as unverified information also considerably spreads in social networks. The study of how rumor spreads and how it can be controlled, plays an important role in reducing social and psychological damages of rumor in social networks. Although recent researches have mainly focused on epidemic models and structure of social networks, they ignore the impact of people’s decision on rumor process. In this paper, an evolutionary game model is proposed to analyze the rumor process in social network considering the impacts of people’s decisions on rumor propagation and control. The model considers a rumor control mechanism via sending anti-rumor messages through rumor control centers. Factors affecting the people’s decisions including social anxiety, people’s attitude toward rumor/anti-rumor, strength of rumor/anti-rumor, influence of rumor control centers, and participation of people in discussions are studied in the model. The proposed game model is analyzed by replicator dynamics equations and simulation of the imitation update rule on a synthetic (Barabasi–Albert) and two real-world graphs of Twitter and Facebook. We further analyze the model in various environments considering people characteristics and society situation. Also we use a real rumor dataset of Twitter (Pheme dataset) to first compare the trends of people strategies (rumor/anti-rumor spreader and ignorant) derived by the model with the real trends of the traits of people in the rumor spreading on Twitter. Then we conduct a number of sensitivity analysis experiments to show the impact of different factors on rumor process. In fact, we analyze the trends of people strategies in Pheme dataset assuming various possible conditions. The analysis show that propagation of convincing anti-rumor messages and locating rumor control centers impact debunking the rumor. Moreover, it is shown that people attitude toward rumor/anti-rumor has significant impact on rumor spreading. Besides, factors such as social anxiety and strength of rumor accelerates rumor propagation.

Suggested Citation

  • Askarizadeh, Mojgan & Tork Ladani, Behrouz & Manshaei, Mohammad Hossein, 2019. "An evolutionary game model for analysis of rumor propagation and control in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 21-39.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:21-39
    DOI: 10.1016/j.physa.2019.01.147
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119301530
    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.2019.01.147?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. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Zhang, Nan & Huang, Hong & Su, Boni & Zhao, Jinlong & Zhang, Bo, 2014. "Dynamic 8-state ICSAR rumor propagation model considering official rumor refutation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 333-346.
    3. Li, Dandan & Ma, Jing & Tian, Zihao & Zhu, Hengmin, 2015. "An evolutionary game for the diffusion of rumor in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 51-58.
    4. Xia-Xia Zhao & Jian-Zhong Wang, 2014. "Dynamical Behaviors of Rumor Spreading Model with Control Measures," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-11, June.
    5. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331.
    6. Laijun Zhao & Jiajia Wang & Rongbing Huang, 2015. "Immunization against the Spread of Rumors in Homogenous Networks," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-17, May.
    7. Zhang, Yan, 2013. "The impact of other-regarding tendencies on the spatial vaccination game," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 209-215.
    8. Sahafizadeh, Ebrahim & Tork Ladani, Behrouz, 2018. "The impact of group propagation on rumor spreading in mobile social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 412-423.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amaral, Marco A. & Oliveira, Marcelo M. de & Javarone, Marco A., 2021. "An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    2. Ding, Haixin & Xie, Li, 2023. "Simulating rumor spreading and rebuttal strategy with rebuttal forgetting: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    3. 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).
    4. Sahafizadeh, Ebrahim & Tork Ladani, Behrouz, 2023. "Soft rumor control in mobile instant messengers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    5. Nicolosi, Gabriel & Friesz, Terry & Griffin, Christopher, 2022. "Approximation of optimal control surfaces for 2 × 2 skew-symmetric evolutionary game dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).

    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. Sahafizadeh, Ebrahim & Tork Ladani, Behrouz, 2023. "Soft rumor control in mobile instant messengers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Lu, Peng & Yao, Qi & Lu, Pengfei, 2019. "Two-stage predictions of evolutionary dynamics during the rumor dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 349-369.
    3. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.
    4. 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.
    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. Xue Yang & Zhiliang Zhu & Hai Yu & Yuli Zhao & Li Guo, 2019. "Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-11, December.
    7. Chen, Guanghua, 2019. "ILSCR rumor spreading model to discuss the control of rumor spreading in emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 88-97.
    8. 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.
    9. Chen Jianhong & Song Qinghua & Zhou Zhiyong, 2017. "Agent-Based Simulation of Rumor Propagation on Social Network Based on Active Immune Mechanism," Journal of Systems Science and Information, De Gruyter, vol. 5(6), pages 571-584, December.
    10. Blazquez-Soriano, Amparo & Ramos-Sandoval, Rosmery, 2022. "Information transfer as a tool to improve the resilience of farmers against the effects of climate change: The case of the Peruvian National Agrarian Innovation System," Agricultural Systems, Elsevier, vol. 200(C).
    11. Martin L. Weitzman, 2015. "A Voting Architecture for the Governance of Free-Driver Externalities, with Application to Geoengineering," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(4), pages 1049-1068, October.
    12. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    13. Guo Weilong & Minca Andreea & Wang Li, 2016. "The topology of overlapping portfolio networks," Statistics & Risk Modeling, De Gruyter, vol. 33(3-4), pages 139-155, December.
    14. Kobayashi, Teruyoshi & Takaguchi, Taro, 2018. "Identifying relationship lending in the interbank market: A network approach," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 20-36.
    15. Konstantinos Antoniadis & Kostas Zafiropoulos & Vasiliki Vrana, 2016. "A Method for Assessing the Performance of e-Government Twitter Accounts," Future Internet, MDPI, vol. 8(2), pages 1-18, April.
    16. Yin, Fulian & Jiang, Xinyi & Qian, Xiqing & Xia, Xinyu & Pan, Yanyan & Wu, Jianhong, 2022. "Modeling and quantifying the influence of rumor and counter-rumor on information propagation dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    17. Maness, Michael & Cirillo, Cinzia, 2016. "An indirect latent informational conformity social influence choice model: Formulation and case study," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 75-101.
    18. Lomi, Alessandro & Fonti, Fabio, 2012. "Networks in markets and the propensity of companies to collaborate: An empirical test of three mechanisms," Economics Letters, Elsevier, vol. 114(2), pages 216-220.
    19. Zhang, Xuxi & Liu, Xianping & Lewis, Frank L. & Wang, Xia, 2020. "Bipartite tracking consensus of nonlinear multi-agent systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    20. Bing Han & Liyan Yang, 2013. "Social Networks, Information Acquisition, and Asset Prices," Management Science, INFORMS, vol. 59(6), pages 1444-1457, June.

    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:523:y:2019:i:c:p:21-39. 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.