Stability analysis of a SAIR rumor spreading model with control strategies in online social networks

2020 ◽  
Vol 526 ◽  
pp. 1-19
Author(s):  
Linhe Zhu ◽  
Bingxu Wang
2017 ◽  
Vol 28 (05) ◽  
pp. 1750061 ◽  
Author(s):  
Ping Jiang ◽  
Xiangbin Yan

This paper establishes a novel Susceptible-Infected-Removed (SIR) rumor spreading model for online social networks (OSNs). The model utilizes the node degree to describe the dynamic changes of the number of rumor spreaders and it can be regarded as an extension of the traditional SIR model. Stability analysis of the model reveals that the spreader in social networks has a basic reproduction number. If the basic reproduction number is less than 1, then rumors will disappear. Otherwise, rumors will persist. According to this result, we can predict the trend of rumor spreading. Then we propose an immune-structure SIR model to explore the control method of rumor spreading. Stability analysis and numerical simulation of the model indicate that immunizing susceptible individual is an effective method to control rumors. Further, the immune-structure model explains that the network structure decides the choice of immune methods. Our findings offer some new insights to control the spread of rumors on OSNs.


2018 ◽  
Vol 29 (09) ◽  
pp. 1850078 ◽  
Author(s):  
Yongcong Luo ◽  
Jing Ma

We explore the impact of positive news on rumor spreading in this paper. It is a fact that most of the rumors related to hot events or emergencies can be propagated rapidly on the hotbed of online social networks. In Chinese words, it is better to divert rather than block. Therefore, we propose the spreading model [Formula: see text] in which positive news is a good factor to guide rumor spreading. Based on transition probability method, we have got the spreading parameters of the [Formula: see text] model by running the rumor spreading process in online social networks with scale-free characteristics. The results give a good proof that improving the activity of the positive news spreader [Formula: see text] derived from the [Formula: see text] model can guide and restrain the spreading speed of rumor smoothly.


Author(s):  
Wang Hongmei ◽  
Qiu Liqing ◽  
Tan Kun ◽  
Cui Junwei

As an important area of social networks, rumor spread has attracted the attention of many scholars. It aims to explore the rumor propagation, and to propose effective measures to curb the further spread of rumors. Different from some existing works, this paper believes that susceptible persons affected by rumor-refuting information will first enter the critical state, while ones who related to rumors will directly turn into the spread state. Therefore, this paper proposes a Susceptible-Infectious-Critical-Recovered (SICR) rumor model. In addition, considering that infectious persons with high levels of refuting rumors may cause emotional resonance among individuals, this model adds a connecting edge from the recovered to the infectious who are triggered by the information of refuting the rumors. First, the basic regeneration number [Formula: see text] is obtained by using the next generation matrix method. Then, the global stability of the rumor-free equilibrium [Formula: see text] and the persistence of rumor propagation are proved in detail in theoretical analysis. The simulation results show that the existence of a critical state can reduce the influence of rumors. Rumor refutation mechanism, as soon as possible to curb the spread of rumors, is an effective measure.


2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Yuan Xu ◽  
Renjie Mei ◽  
Yujie Yang ◽  
Zhengmin Kong

It is of great practical significance to figure out the propagation mechanism and outbreak condition of rumor spreading on online social networks. In our paper, we propose a multi-state reinforcement diffusion model for rumor spreading, in which the reinforcement mechanism is introduced to depict individual willingness towards rumor spreading. Multiple intermediate states are introduced to characterize the process that an individual's diffusion willingness is enhanced step by step. We study the rumor spreading process with the proposed reinforcement diffusion mechanism on two typical networks. The outbreak thresholds of rumor spreading on both two networks are obtained. Numerical simulations and Monte Carlo simulations are conducted to illustrate the spreading process and verify the correctness of theoretical results. We believe that our work will shed some light on understanding how human sociality affects the rumor spreading on online social networks.


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