The stochastic evolution of a rumor spreading model with two distinct spread inhibiting and attitude adjusting mechanisms in a homogeneous social network

In this paper, we propose and analyze from a stability viewpoint a deterministic, ODE-based class of rumor spreading models with two distinct inhibiting and adjusting mechanisms, together with its corresponding stochastic counterpart. For the deterministic model, a threshold parameter R 0 defined ad hoc, called the basic influence number, is used to ascertain whether the rumors are prevailing or not. If R 0 < 1 , the rumor-free equilibrium is found to be locally asymptotically stable, while if R 0 > 1 it is shown that there is at least one additional rumor-prevailing equilibrium, which is necessarily locally asymptotically stable. For the stochastic model, we first show that there exists a unique global solution. Subsequently, we investigate the asymptotic behavior of the stochastic system around the equilibria of the deterministic system by constructing suitable Lyapunov functionals. Furthermore, numerical simulations are given to illustrate, support and enhance our theoretical analysis.

[1]  Jiajia Wang,et al.  Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks , 2013 .

[2]  Kazuki Kawachi,et al.  Deterministic models for rumor transmission , 2008 .

[3]  Xin Wang,et al.  Public discourse and social network echo chambers driven by socio-cognitive biases , 2020, ArXiv.

[4]  Guanghua Chen,et al.  ILSCR rumor spreading model to discuss the control of rumor spreading in emergency , 2019, Physica A: Statistical Mechanics and its Applications.

[5]  Yamir Moreno,et al.  Dynamics of rumor spreading in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Linhe Zhu,et al.  The dynamics analysis of a rumor propagation model in online social networks , 2019, Physica A: Statistical Mechanics and its Applications.

[7]  Hernán A. Makse,et al.  CUNY Academic Works , 2022 .

[8]  J. Watmough,et al.  Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. , 2002, Mathematical biosciences.

[9]  G. Caldarelli,et al.  The spreading of misinformation online , 2016, Proceedings of the National Academy of Sciences.

[10]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[11]  Mingfeng He,et al.  Rumor spreading model with the different attitudes towards rumors , 2018, Physica A: Statistical Mechanics and its Applications.

[12]  T. Shibutani Improvised News: A Sociological Study of Rumor , 1966 .

[13]  D. Zanette Dynamics of rumor propagation on small-world networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Komi Afassinou Analysis of the impact of education rate on the rumor spreading mechanism , 2014 .

[15]  Bo Song,et al.  Rumor spreading model considering hesitating mechanism in complex social networks , 2015 .

[16]  Jiajia Wang,et al.  2SI2R rumor spreading model in homogeneous networks , 2014 .

[17]  Jiajia Wang,et al.  SIR rumor spreading model in the new media age , 2013 .

[18]  Yongli Zan,et al.  DSIR double-rumors spreading model in complex networks , 2018 .

[19]  Xia-Xia Zhao,et al.  Dynamical Behaviors of Rumor Spreading Model with Control Measures , 2014 .

[20]  Daniel P Maki,et al.  Mathematical models and applications , 1973 .

[21]  Miriam J. Metzger,et al.  The science of fake news , 2018, Science.

[22]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

[23]  D. Zanette Critical behavior of propagation on small-world networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Jing Ma,et al.  How the government's punishment and individual's sensitivity affect the rumor spreading in online social networks , 2017 .

[25]  Yi Zhang,et al.  A Rumor Spreading Model considering the Cumulative Effects of Memory , 2015 .

[26]  Salma M. Al-Tuwairqi,et al.  Qualitative Analysis of a Rumor Transmission Model with Incubation Mechanism , 2015 .

[27]  G. Dimitriu,et al.  Global Sensitivity Approach for the Human Immunodeficiency Virus Pathogenesis with Cytotoxic T-Lymphocytes and Infected Cells in Eclipse Phase , 2019, 2019 E-Health and Bioengineering Conference (EHB).

[28]  S Mahieu,et al.  Scaling fields in the two-dimensional Abelian sandpile model. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  D. Kendall,et al.  Epidemics and Rumours , 1964, Nature.

[30]  U. Horst Dynamic Systems of Social Interactions , 2010 .

[31]  Xin Wang,et al.  Homophily on social networks changes evolutionary advantage in competitive information diffusion , 2019, ArXiv.

[32]  Youguo Wang,et al.  Rumor spreading model with noise interference in complex social networks , 2017 .

[33]  Youguo Wang,et al.  Rumor diffusion model with spatio-temporal diffusion and uncertainty of behavior decision in complex social networks , 2018, Physica A: Statistical Mechanics and its Applications.