Dynamical analysis of rumor spreading model with impulse vaccination and time delay

Huo, Liang’an; Ma, Chenyang

doi:10.1016/j.physa.2016.12.024

Cited by 34 publications

(15 citation statements)

References 16 publications

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“…When people hear rumors, most people will not immediately become spreaders and contemplate whether the rumors are true. In view of this, Huo et al 20 proposed the XWYZ model. Compared with the SIR model, this model considered the new group W, which denotes people who hesitate to spread rumors.…”

Section: Rumor Spreading Model Considering Rumor Credibility Correlamentioning

confidence: 99%

Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality

Chen

Wang

2020

Sci Rep

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The study of rumor spreading or rumor controlling is important and necessary because rumors can cause serious negative effects on society. The process of rumor spreading is influenced by many factors. In this paper, we suggest that people with different personalities will behave differently after hearing rumors. Thus, we divide the population into two types: radical people and steady people. Furthermore, we suggest that the credibility of rumors and the correlation between rumors and people’s lives are important factors that will influence the spread of rumors. Based on these considerations, we propose the SEIsIrR model. We establish differential equations to describe the dynamics of the rumor spreading process in homogeneous and heterogeneous networks. Using the Jacobian matrix and next generation matrix, we obtain the spreading threshold of the SEIsIrR model and discuss the relationship of the spreading threshold between homogeneous networks and heterogeneous networks. We employ a real rumor dataset obtained from Twitter to verify the SEIsIrR model and perform numerical simulations in Watts-Strogatz (WS) networks and Barabasi-Albert (BA) networks to verify the obtained spreading thresholds and discuss the impacts of these factors on the rumor spreading process and the differences in the rumor spreading processes between WS networks and BA networks. The simulation results show that these factors influence the speed and range of rumor spreading.

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Section: Rumor Spreading Model Considering Rumor Credibility Correlamentioning

confidence: 99%

Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality

Chen

Wang

2020

Sci Rep

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“…It is generally appreciated that time de- lays are unavoidable in real life, especially with regard to the process of rumor spreading. Until now, there are few articles on the subject of rumor spreading in which time delays were taken into account [32][33][34]. These results, however, were only established in homogeneous networks and a single-language environment.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Dynamics of the rumor-spreading model with hesitation mechanism in heterogenous networks and bilingual environment

Yang

Jiang

et al. 2020

Adv Differ Equ

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In this paper, a novel rumor-spreading model is proposed under bilingual environment and heterogenous networks, which considers that exposures may be converted to spreaders or stiflers at a set rate. Firstly, the nonnegativity and boundedness of the solution for rumor-spreading model are proved by reductio ad absurdum. Secondly, both the basic reproduction number and the stability of the rumor-free equilibrium are systematically discussed. Whereafter, the global stability of rumor-prevailing equilibrium is explored by utilizing Lyapunov method and LaSalle’s invariance principle. Finally, the sensitivity analysis and the numerical simulation are respectively presented to analyze the impact of model parameters and illustrate the validity of theoretical results.

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“…The rumor propagation models with rational consciousness [14], psychological differences [15,16], media reports [17], official refutation [18] and other factors were also considered. In addition, since individuals may not respond to rumors in time, [19,20] studied the rumor propagation models with contact lag between susceptible and infected people. Since various individuals have distinct abilities to spread and absorb information, it is of greater practical significance to study the rumor propagation model [21][22][23][24] under heterogeneous networks.…”

Section: Introductionmentioning

confidence: 99%

The spread and control of rumors in a multilingual environment

Jiang

et al. 2020

Nonlinear Dyn

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This paper studies the rumor propagation model with heterogeneous networks in a multilingual environment. Firstly, a rumor propagation model with two language spreaders, in which the immunologic mechanism is considered in the ignorant, is proposed on heterogeneous networks. Secondly, the basic reproduction number and the dynamic behaviors are analyzed by using the next-generation matrix method and Lyapunov stability theory, respectively. Moreover, two control strategies are designed to effectively suppress the spread of the rumor. The one is continuous control strategy. By applying real-time control to the spreaders, the rumor spreading time can be greatly reduced and the rumor can die out in a short time. The other is event-triggered impulsive control strategy, which can effectively reduce the consumption of resources and ensure the extinction of the rumor. Finally, the correctness of theoretical analysis and the feasibility of control methods are verified by numerical simulations.

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Dynamical analysis of rumor spreading model with impulse vaccination and time delay

Cited by 34 publications

References 16 publications

Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality

Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality

Dynamics of the rumor-spreading model with hesitation mechanism in heterogenous networks and bilingual environment

The spread and control of rumors in a multilingual environment

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