Skip to main content
SpringerLink
Log in

Ternary interaction evolutionary game of rumor and anti-rumor propagation under government reward and punishment mechanism

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

To aid in designing effective rumor control strategies, combined with system dynamics, the paper proposed a ternary interaction evolutionary game model of participants of rumor and anti-rumor propagation in social network platform, involving the media, government, and netizens. In this model, rules are established to classify source microblogs, comments, and forwards on the real social network platform Sina Weibo as rumors or anti-rumors. It quantifies the ambiguity and severity of rumors, the certainty and evidentiality of anti-rumors, the sentiment of society, and the influence of users, embedding them into the evolutionary game model. Sensitivity analyses were carried out based on the real data from social network platform Sina Weibo to investigate how the exogenous variables affect different evolutionary stable strategies. Experiments demonstrate that the government could adopt loose regulation coupled with a strict but reasonable reward and punishment mechanism, which can promote the media to make true reports and relieve its pressure of rumor regulation. And the cost of the government’s strict regulation and netizens’ anti-rumor propagation have significant impact on their choices when the government chooses loose regulation as its evolutionary stable strategy, while they have a tiny impact on their choice when the government chooses strict regulation as its evolutionary stable strategy. But reducing the cost of media and netizens to search for dispelling evidence can encourage them to propagate anti-rumor information in different situations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

References

  1. Daley, D.J., Kendall, D.G.: Stochastic rumours. IMA J. Appl. Math. 1(1), 42–55 (1965)

    Article  MathSciNet  Google Scholar 

  2. Maki, D.P., Thompson, M.: Mathematical Models and Applications. Prentice-Hall, Englewood Cliffs (1973)

    Google Scholar 

  3. Zhu, L.H., He, L.: Pattern formation in a reaction–diffusion rumor propagation system with Allee effect and time delay. Nonlinear Dyn. 107(3), 3041–3063 (2022)

    Article  Google Scholar 

  4. ArifulKabir, K.M., Kuga, K., Jun, T.: Analysis of SIR epidemic model with information spreading of awareness. Chaos Solitons Fractals 119, 118–125 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  5. Pan, W.Q., Yan, W.J., Hu, Y.H., et al.: Dynamic analysis of a SIDRW rumor propagation model considering the effect of media reports and rumor refuters. Nonlinear Dyn. 111(4), 3925–3936 (2023)

    Article  Google Scholar 

  6. Alshammari, F.S., Khan, M.A.: Dynamic behaviors of a modified SIR model with nonlinear incidence and recovery rates. Alex. Eng. J. 60(3), 2997–3005 (2021)

    Article  Google Scholar 

  7. Zhang, M.L., Qin, S.M., Zhu, X.X.: Information diffusion under public crisis in BA scale-free network based on SEIR model—taking COVID-19 as an example. Phys. A. 571(1), 125848 (2021)

    Article  Google Scholar 

  8. Zhang, Z.Y., Mei, X.H., Jiang, H.J., et al.: Dynamical analysis of hyper-SIR rumor spreading model. Appl. Math. Comput. 446, 127887 (2023)

    MathSciNet  MATH  Google Scholar 

  9. Yu, S., Yu, Z., Jiang, H., et al.: The dynamics and control of 2I2SR rumor spreading models in multilingual online social networks. Inf. Sci. 581, 18–41 (2021)

    Article  MathSciNet  Google Scholar 

  10. Guo, H., Yin, Q., Xia, C., et al.: Impact of information diffusion on epidemic spreading in partially mapping two-layered time-varying networks. Nonlinear Dyn. 105, 3819–3833 (2021)

    Article  Google Scholar 

  11. Shao, Q., Xia, C., Wang, L., et al.: A new propagation model coupling the offline and online social networks. Nonlinear Dyn. 98, 2171–2183 (2019)

    Article  Google Scholar 

  12. Li, Z.M., Du, X.Y., Zhao, Y., et al.: Lifecycle research of social media rumor refutation effectiveness based on machine learning and visualization technology. Inf. Process. Manag. 59(6), 103077 (2022)

    Article  Google Scholar 

  13. Ding, K., Zhang, J.: Multi-party privacy conflict management in online social networks: a network game perspective. IEEE/ACM Trans. Netw. 28, 1–14 (2020)

    Article  Google Scholar 

  14. Xiao, Y., Chen, D., Wei, S., et al.: Rumor propagation dynamic model based on evolutionary game and anti-rumor. Nonlinear Dyn. 1, 95 (2019)

    Google Scholar 

  15. Yin, F.L., Jiang, X.Y., Qian, X.Q., et al.: Modeling and quantifying the influence of rumor and counter-rumor on information propagation dynamics. Chaos Solitons Fractals 162, 112392 (2022)

    Article  MathSciNet  Google Scholar 

  16. Huang, H., Meng, Z., Shen, H.: Competitive and complementary influence maximization in social network: a follower’s perspective. Knowl. Based Syst. 213, 106600 (2021)

    Article  Google Scholar 

  17. Culda, L.C., Kaslik, E., Neamţu, M.: Stability and bifurcations in a general Cournot duopoly model with distributed time delays. Chaos Solitons Fractals 162, 112424 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  18. Paek, H.J., Thomas, H.: Mediating and moderating roles of trust in government in effective risk rumor management: a test case of radiation-contaminated seafood in South Korea. Risk Anal. Off. Publ. Soc. Risk Anal. 39(12), 2653–2667 (2019)

    Google Scholar 

  19. Askarizadeh, M., Ladani, B.T., Manshaei, M.H.: An evolutionary game model for analysis of rumor propagation and control in social networks. Phys. A 523, 21–39 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhu, L.H., Yang, F., Guan, G., et al.: Modeling the dynamics of rumor diffusion over complex networks. Inf. Sci. 562, 240–258 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  21. Wang, B., Chen, G., Fu, L., et al.: Drimux: dynamic rumor influence minimization with user experience in social networks. IEEE Trans. Knowl. Data Eng. 29(10), 2168–2181 (2017)

    Article  Google Scholar 

  22. Qiu, L., Tian, X., Zhang, J., et al.: LIDDE: a differential evolution algorithm based on local-influence-descending search strategy for influence maximization in social networks. J. Netw. Comput. Appl. 178, 102973 (2021)

    Article  Google Scholar 

  23. Zubiaga, A., Liakata, M., Procter, R., et al.: Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLoS ONE 11(3), 1–29 (2016)

    Article  Google Scholar 

  24. Hisashi, O., Martin, A.N.: The replicator equation on graphs. J. Theor. Biol. 243(1), 86–97 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  25. Cheng, Y.Y., Huo, L.A., Zhao, L.J.: Stability analysis and optimal control of rumor spreading model under media coverage considering time delay and pulse vaccination. Chaos Solitons Fractals 157, 111931 (2022)

    Article  MathSciNet  Google Scholar 

  26. Xia, Y., Jiang, H.J., Yu, Z.Y., et al.: Dynamic analysis and optimal control of a reaction-diffusion rumor propagation model in multi-lingual environments. J. Math. Anal. Appl. 521(2), 126967 (2023)

    Article  MathSciNet  MATH  Google Scholar 

  27. Sopory, P., Day, A.M., Novak, J.M., et al.: Communicating uncertainty during public health emergency events: a systematic review. Rev. Commun. Res. 7, 67–108 (2019)

    Article  Google Scholar 

  28. Fang, S.W., Zhao, N., Chen, N.: Analyzing and predicting network public opinion evolution based on group persuasion force of populism. Phys. A 525, 809–824 (2019)

    Article  Google Scholar 

  29. Cristea, F., Weishaar, H., Geurts, B., et al.: A comparative analysis of experienced uncertainties in relation to risk communication during COVID19: A four-country study. Glob. Health 18(1), 66 (2022)

    Article  Google Scholar 

  30. Huo, L.A., Wang, L., Song, N.X., et al.: Rumor spreading model considering the activity of spreaders in the homogeneous network. Phys. A 468, 855–865 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  31. Sahafizadeh, E., Tork, L.B.: The impact of group propagation on rumor spreading in mobile social networks. Phys. A 506, 412–423 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  32. Ke, Y., Zhu, L.H., Wu, P., et al.: Dynamics of a reaction–diffusion rumor propagation model with non-smooth control. Appl. Math. Comput. 435, 127478 (2022)

    MathSciNet  MATH  Google Scholar 

  33. Yin, Q., Wang, Z., Xia, C.: Information-epidemic co-evolution propagation under policy intervention in multiplex networks. Nonlinear Dyn. 111, 14583–14595 (2023)

    Article  Google Scholar 

  34. Wang, Z., Xia, C.: Co-evolution spreading of multiple information and epidemics on two-layered networks under the influence of mass media. Nonlinear Dyn. 102, 3039–3052 (2020)

    Article  Google Scholar 

  35. Barnard, C.: The Functions of the Executive. Harvard University Press, Cambridge (1938)

    Google Scholar 

Download references

Funding

This work was supported by the General project of Humanities and social sciences research project of Ministry of Education (Grant Number 20YJA870021).

Author information

Authors and Affiliations

  1. College of Economy and Management, Yanshan University, Hebei Street, Qinhuangdao, 066004, Hebei Province, China

    Simeng Qin, Mingli Zhang & Haiju Hu

  2. Hebei Construction Material Vocational and Technical College, Wenyu Street, Qinhuangdao, 066004, Hebei Province, China

    Mingli Zhang

  3. Key Laboratory for Health Care with Chinese Medicine of Hebei Province, Xingyuan Street, Shijiazhuang, 050200, Hebei Province, China

    Mingli Zhang

Authors
  1. Simeng Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingli Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haiju Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZM contributed to the study conception and design. Material preparation, data collection and analysis were performed by QS. The first draft of the manuscript was written by QS. HH commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mingli Zhang.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 29 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qin, S., Zhang, M. & Hu, H. Ternary interaction evolutionary game of rumor and anti-rumor propagation under government reward and punishment mechanism. Nonlinear Dyn 111, 21409–21439 (2023). https://doi.org/10.1007/s11071-023-08962-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-023-08962-1

Keywords

Search

Navigation