Intervention strategies for false information on two-layered networks in public crisis by numerical simulations

Focus
  • 13 Downloads

Abstract

Recently, public crises caused by rumor and other false information are occupying an increasingly high proportion, so how to control the diffusion of false information and decrease the loss of society has become a vital problem. In order to explore intervention strategies, firstly a two-layered network was generated considering real conditions, and its diffusion threshold is calculated based on previous studies. Then, after dividing intervention conditions into before and after the outbreak, intervention strategies were explored from the aspects of network topology and public management. And the results indicated that before the outbreak, the superposition of networks will add to the difficulty in intervention, while an integrated immunization strategy which takes the topology of multiplex network into consideration works well. After the outbreak, the integrated immunization strategy has a relative worse effect. But releasing correct information could receive a better effect on intervention. Besides, releasing correct information in the online network has better effect on the final results than in the offline network. Also, correct information with high acceptance rate will weaken the influence of degree on intervention, which means that nodes with high or low degree have no significant difference in intervention effect.

Keywords

Public crisis Two-layered networks False information Intervention strategies Numerical simulation 

Notes

Acknowledgements

The authors would like to thank the support of the National Natural Science Foundation of China (71301140), Hebei Natural Science Fund (G2015203425) and the Program for Youth Talents by Department of Education in Hebei Province (BJ2017078). The authors are also thankful for the support by the Program for Talents of Third Level and Program for Youth Talents in Hebei Province.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Albert R, Barabsi A (2002) Statistical mechanics of complex networks. Revmodphys 74(1):47–97MathSciNetGoogle Scholar
  2. Buono C, Braunstein LA (2015) Immunization strategy for epidemic spreading on multilayer networks. EPL 109(2):26,001CrossRefGoogle Scholar
  3. Gu YR (2012) The propagation and inhibition of rumors in online social network. Acta Phys Sin 61(23):514–518Google Scholar
  4. Huang J, Jin X (2011) Preventing rumor spreading on small-world networks. J Syst Sci Complex 24(3):449–456MathSciNetCrossRefGoogle Scholar
  5. Jo HH, Moon HT, Baek SK (2003) Immunization dynamics on a 2-layer network model. Quant Biol 361(2):534–542Google Scholar
  6. Lan YX (2012) Research on network rumor diffusion model and countermeasures in the emergency. Inf Sci 9:1334–1338Google Scholar
  7. Li Y (2012) Analysis on topological features of online social networks. Complex Syst Complex Science 9(2):22–37Google Scholar
  8. Madar N, Kalisky T, Cohen R, Ben-Avraham D, Havlin S (2004) Immunization and epidemic dynamics in complex networks. Eur Phys J B 38(2):269–276CrossRefGoogle Scholar
  9. Tian RY, Zhang XF, Liu YJ (2015) Ssic model: a multi-layer model for intervention of online rumors spreading. Phys A Stat Mech Appl 427:181–191CrossRefGoogle Scholar
  10. Wang G (2011) A study on the government reactions in coping with internet rumors based on cases. J Intell 5(4):335–342Google Scholar
  11. Wang X, Li X, Chen G (2006) Complex network theory and application. Tsinghua University Press, BeijingGoogle Scholar
  12. Xiang Z, Chen Y (2016) Dissemination model and impact evaluation of rumor in microblog. Sci Res Manag 37(1):39–47Google Scholar
  13. Zhao D, Li L, Peng H, Luo Q, Yang Y (2013) Multiple routes transmitted epidemics on multiplex networks. Phys Lett A 378(10):770–776MathSciNetCrossRefMATHGoogle Scholar
  14. Zhu X, Liu M, Lu J (2016) Research on the mechanism of fake information diffusion on multi-layered network in public crisis. J China Soc Sci Tech Inf 35(3):265–274Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementYanshan UniversityQinhuangdaoChina
  2. 2.Institute of Systems EngineeringDalian University of TechnologyDalianChina

Personalised recommendations