Global dynamics analysis and control of a rumor spreading model in online social networks

Zhu, Linhe; Zhou, Xiao; Li, Yimin

doi:10.1016/j.physa.2019.04.139

Cited by 34 publications

(13 citation statements)

References 15 publications

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“…The proposed model is verified by simulation results. Furthermore, Zhu et al [50] using the same model as in [49] obtained a local and global equilibrium as well as calculated the basic reproduction number using the next generation matrix concept. The authors explained the effect of time delay on rumor propagation and developed an effective control mechanism.…”

Section: Related Workmentioning

confidence: 99%

Defensive Modeling of Fake News Through Online Social Networks

Shrivastava

Kumar

Ojha

et al. 2020

IEEE Trans. Comput. Soc. Syst.

123

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Online social networks (OSNs) have become an integral mode of communication among people and even nonhuman scenarios can also be integrated into OSNs. The evergrowing rise in the popularity of OSNs can be attributed to the rapid growth of Internet technology. OSN becomes the easiest way to broadcast media (news/content) over the Internet. In the wake of emerging technologies, there is dire need to develop methodologies, which can minimize the spread of fake messages or rumors that can harm society in any manner. In this article, a model is proposed to investigate the propagation of such messages currently coined as fake news. The proposed model describes how misinformation gets disseminated among groups with the influence of different misinformation refuting measures. With the onset of the novel coronavirus-19 pandemic, dubbed COVID-19, the propagation of fake news related to the pandemic is higher than ever. In this article, we aim to develop a model that will be able to detect and eliminate fake news from OSNs and help ease some OSN users stress regarding the pandemic. A system of differential equations is used to formulate the model. Its stability and equilibrium are also thoroughly analyzed. The basic reproduction number (R 0) is obtained which is a significant parameter for the analysis of message spreading in the OSNs. If the value of R 0 is less than one (R 0 < 1), then fake message spreading in the online network will not be prominent, otherwise if R 0 > 1 the rumor will persist in the OSN. Realworld trends of misinformation spreading in OSNs are discussed. In addition, the model discusses the controlling mechanism for untrusted message propagation. The proposed model has also been validated through extensive simulation and experimentation.

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Section: Related Workmentioning

confidence: 99%

Defensive Modeling of Fake News Through Online Social Networks

Shrivastava

Kumar

Ojha

et al. 2020

IEEE Trans. Comput. Soc. Syst.

123

View full text Add to dashboard Cite

show abstract

“…Thus, this study intends to analyze the dissemination mechanism of CLPP in FSN. The epidemic model is one of the microscopic dissemination models and has therefore been widely used in knowledge, information, low-carbon technology, and rumor dissemination [22][23][24][25]. In addition, the dissemination models of CLPP and infectious diseases are the same.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Dissemination of Cultivated Land Protection Policies in China

Zhang

Tang

2021

Land

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Cultivated land protection is the top priority of the national economy in China and the livelihood of people. Cultivated land protection policies (CLPP) play an important role in the protection of cultivated land. However, the process of dissemination of CLPP on social networks of farmers has problems, such as distortion of policy content, single dissemination channels, low level of farmers’ knowledge, and low dissemination efficiency. For revealing the characteristics of the dissemination of CLPP in the farmers’ social networks (FSN), this study combines the Suspected–Exposed–Infected–Recovered–Suspected (SEIRS) epidemic model to construct a model of CLPP dissemination suitable for FSN. In addition, a numerical simulation of the dissemination process of CLPP is conducted on the FSN, and the influence of the structural characteristics of the FSN and different model parameters on the dissemination of CLPP is analyzed. Results show that (1) the dissemination rate between farmers in FSN has a significant impact on the scale and speed of CLPP. A greater initial dissemination rate corresponds to faster speed and larger scale of CLPP dissemination. (2) A greater node degree in FSN means stronger dissemination ability for CLPP. Therefore, identifying structural holes (opinion leaders) in FSN can effectively promote the dissemination of CLPP. (3) The SEIRS model can dynamically describe the evolution law of CLPP dissemination process over time through the four states of farmer nodes of suspected, exposed, infected, and recovered. Numerical simulation results show that the immune degradation rate is proportional to CLPP. However, the direct immunization rate is inversely proportional. The increase in immune degradation rate can reduce the number of recovered farmers and improve the efficiency of CLPP dissemination. On the basis of the abovementioned conclusions, this study draws policy recommendations to increase the scale and speed of CLPP dissemination in China.

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“…This problem is referred to as influence maximization (IM) in networks. Influence maximization can be applied in many areas, such as bioinformatics research [5][6], online marketing [7][8], political elections [9], recommendations [10][11], public opinion guidance [12] and many other fields.…”

Section: I Introductionmentioning

confidence: 99%

Influence Maximization in Signed Networks by Enhancing the Negative Influence

Dai

2021

IEEE Access

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With the advancement of science and technology, research on influence maximization in networks has become a hot spot. In social networks, there is not only a positive relationship among nodes but also a negative impact, and the negative impact often plays a greater role than the positive impact, as observed for shopping websites or online votes. This paper proposes a method based on an independent cascade model by emphasizing the negative impact in symbolic networks to solve the problem of influence maximization. First, an algorithm that is based on an independent path and that emphasizes negative influence is designed to obtain the probability among nodes. Based on the activation probability, an algorithm is proposed to identify nodes that could have the greatest impact on the influence increment from the seeds. Finally, the seed set is confirmed based on the influence in the corresponding symbol network. In the experiment performed on real-world network data, the result indicate that the proposed algorithm causes more substantial influence propagation than do other algorithms.

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Global dynamics analysis and control of a rumor spreading model in online social networks

Cited by 34 publications

References 15 publications

Defensive Modeling of Fake News Through Online Social Networks

Defensive Modeling of Fake News Through Online Social Networks

Modeling and Simulation of Dissemination of Cultivated Land Protection Policies in China

Influence Maximization in Signed Networks by Enhancing the Negative Influence

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