Optimal Control of ISTR Rumor Propagation Model with Social Reinforcement in Heterogeneous Network

The wide spread of rumor is undoubtedly harmful to social stability; we should try to lower the effect of rumor on society. Therefore, it is reasonable to put forward the rumor control strategy on the basis of the study of the law of rumor propagation. Firstly, the ISTR model of rumor is established by including influencing factors of true information spreader and social reinforcement. And by using the next generation matrix method, the basic reproduction number of rumor is obtained. Then, in order to minimize the adverse effects of rumors, through introducing two control strategies of scientific knowledge popularization and refutation of rumors, the optimal control problem is established. And through using Pontryagin’s Minimum Principle, the optimal solution of the rumor propagation model is solved. Finally, through theoretical analysis and numerical simulation, some results can be obtained. The results show that adding true information spreaders into the rumor model can effectively control the rumor propagation, and social reinforcement plays a significance role in rumor. The results also prove that these two control strategies can effectively inhibit the propagation of rumors. With the addition of control strategies, the number of true information spreaders increases, while the number of rumor spreaders decreases.

Spreading predictability in complex networks

Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations.

Spreading Predictability in Complex Networks

Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infected probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this paper, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future. Experimental results on synthetic and real networks demonstrate that the predicted infected individuals have rather consistency with the actual infected ones.

A Bio-Inspired Defensive Rumor Confinement Strategy in Online Social Networks

Online social networks (OSNs) are used to connect people and propagate information around the globe. Along with information propagation, rumors also penetrate across the OSNs in a massive order. Controlling the rumor propagation is utmost important to reduce the damage it causes to society. Educating the individual participants of OSNs is one of the effective ways to control the rumor faster. To educate people in OSNs, this paper proposes a defensive rumor control approach that spreads anti-rumors by the inspiration from the immunization strategies of social insects. In this approach, a new information propagation model is defined to study the defensive nature of true information against rumors. Then, an anti-rumor propagation method with a set of influential spreaders is employed to defend against the rumor. The proposed approach is compared with the existing rumor containment approaches and the results indicate that the proposed approach works well in controlling the rumors.

“To Retweet Each and Every Rumor”

This chapter discusses the practice of rumor diffusion via mobile communication, or what I call “mobile rumoring,” against the hegemonic discourse of the authorities. What drives easy diffusion and proliferation of rumor via mobile phones and other ICTs, even in the face of intensified rumor control, surveillance, and punishment from the regime? As the answer unfolds, the Chinese state’s strategies to suppress communication in the guise of so-called rumor unwittingly establishes the socio-cultural foundation for rumor proliferation, while the official denunciation of rumor becomes a political opportunity that triggers the collective practice of rumor diffusion against the authorities. The call for rumor dissemination thus becomes both an action—a type of tactic, or covert resistance—and a frame to contest communication control and political manipulation by the authorities. Both contested identity and dissenting emotion join as counter-authority initiatives that represent the dynamic behind mobile rumoring in China today.

A rumor control competition model considering intervention of the official rumor-refuting information

To avoid social and economic losses, governments need to control rumor propagation by releasing official rumor-refuting information (ORI) to dispel the rumors. Therefore, understanding the complex competition mechanism between ORI and rumors is key to successfully refuting rumors. This paper presents an in-depth analysis of the function of ORI in suppressing and quashing rumors. First, the influencing factors were determined from which a competition model was constructed and the quenched mean-field method employed to determine the rumor outbreak threshold. It was found that public cognition could affect the rumor threshold and lead to rumor depletion, which was confirmed in a model simulation. The simulation results also indicated that government credibility and the release time of ORI played a critical role in controlling rumors. Specifically, it was found that when government credibility was high, the ORI release was able to quash the rumor spread. And it is shown that the government should release the ORI as soon as possible, and ensure the continuous dissemination of ORI to dispel rumors effectively.

A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy

With the rapid development of social networks, it has become extremely important to evaluate the propagation capabilities of the nodes in a network. Related research has wide applications, such as in network monitoring and rumor control. However, the current research on the propagation ability of network nodes is mostly based on the analysis of the degree of nodes. The method is simple, but the effectiveness needs to be improved. Based on this problem, this paper proposes a method that is based on Tsallis entropy to detect the propagation ability of network nodes. This method comprehensively considers the relationship between a node’s Tsallis entropy and its neighbors, employs the Tsallis entropy method to construct the TsallisRank algorithm, and uses the SIR (Susceptible, Infectious, Recovered) model for verifying the correctness of the algorithm. The experimental results show that, in a real network, this method can effectively and accurately evaluate the propagation ability of network nodes.

Evolutionary information dynamics over social networks: a review

Purpose The purpose of this paper is to have a review on the analysis of information diffusion based on evolutionary game theory. People now get used to interact over social networks, and one of the most important functions of social networks is information sharing. Understanding the mechanisms of the information diffusion over social networks is critical to various applications including online advertisement and rumor control. Design/methodology/approach It has been shown that the graphical evolutionary game theory (EGT) is a very efficient method to study this problem. Findings By applying EGT to information diffusion, the authors could predict every small change in the process, get the detailed dynamics and finally foretell the stable states. Originality/value In this paper, the authors provide a general review on the evolutionary game-theoretic framework for information diffusion over social network by summarizing the results and conclusions of works using graphical EGT.