Modeling Information Diffusion In Online Social Networks Using SEI Epidemic Model

Kumar, Sanjay; Saini, Muskan; Goel, Muskan; Aggarwal, Nipun

doi:10.1016/j.procs.2020.04.073

Cited by 17 publications

(11 citation statements)

References 9 publications

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“…We also applied the proposed metric to real-world networks where the dynamics processes can be described by epidemic models [24][25][26][27]. For example, the virus spread in e-mail networks, the information transfer in neural networks, and rumor diffusion in online social networks.…”

Section: Resultsmentioning

confidence: 99%

A Novel Metric to Quantify the Real-Time Robustness of Complex Networks With Respect to Epidemic Models

et al. 2022

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Spread velocity, epidemic threshold, and infection density at steady state are three non-negligible features describing the spread of epidemics. Combining these three features together, a new network robustness metric with respect to epidemics was proposed in this paper. The real-time robustness of the network was defined and analyzed. By using the susceptible–infected (SI) and susceptible–infected–susceptible (SIS) epidemic models, the robustness of different networks was analyzed based on the proposed network robustness metric. The simulation results showed that homogeneous networks present stronger robustness than do heterogeneous networks at the early stage of the epidemic, and the robustness of the heterogeneous networks becomes stronger than that of the homogeneous ones with the progress of the epidemic. Moreover, the irregularity of the degree distribution decreases the network robustness in homogeneous networks. The network becomes more vulnerable as the average degree grows in both homogeneous and heterogeneous networks.

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Section: Resultsmentioning

confidence: 99%

A Novel Metric to Quantify the Real-Time Robustness of Complex Networks With Respect to Epidemic Models

et al. 2022

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“…The current research related to the transmission of public opinion on social networks mainly focuses on dissemination model [7][8][9] , node preference [10,11] , rumor diffusion [12,13] , network topology [14][15][16] , etc. First, various methods have been proposed to study the dissemination model of network public opinion.…”

Section: Literature Reviewmentioning

confidence: 99%

Public Opinion Dissemination with Incomplete Information on Social Network: A Study Based on the Infectious Diseases Model and Game Theory

Yuan

et al. 2021

Complex Syst. Model. Simul.

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Given the fragmentation of public opinion dissemination and the lag of network users' cognition, the paper analyzes public opinion dissemination with incomplete information, which can provide reference for us to control and guide the spread of public opinion. Based on the derivative and secondary radiation of public opinion dissemination with incomplete information, the Susceptible-Susceptible-Infected-Recovered-Recovered-Infected (SSIR R-I ) model is proposed. Given the interaction between users, the Deffuant opinion dynamics model and evolutionary game theory are introduced to simulate the public opinion game between dissemination and immune nodes. Finally, the numerical simulation and results analysis are given. The results reveal that the rate of opinion convergence significantly affects disseminating public opinion, which is positively correlated with the promotion effect of the dissemination node and negatively correlated with the suppression effect of the immune node of public opinion dissemination. Derivative and secondary radiations have different effects on public opinion dissemination in the early stage, but promote public opinion dissemination in the later stage. The dominant immune nodes have an apparent inhibitory effect on the spread of public opinion; nevertheless, they cannot block the dissemination of public opinion.

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“…The largest use of social media data comes from microblogs, as much as 46% [1]. Twitter data can be used in a remarkably diverse number of research studies, such as sentiment analyses [3,4], text analyses [5][6][7][8], opinion analyses [9,10], as well as analyses of influence or information diffusion [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Referring to [36], the determination of a research question (RQ) is based on the research objectives. We studied the topic of information diffusion on Twitter in our research, so we read several articles related to information diffusion, such as [12,14,21]. Varshney et al [12] aimed to predict the probability of information diffusion and used a Bayesian network method based on tweet/retweet metrics.…”

Section: Introductionmentioning

confidence: 99%

“…Varshney et al [12] aimed to predict the probability of information diffusion and used a Bayesian network method based on tweet/retweet metrics. Kumar et al [14] used the susceptible-exposed-infected (SEI) model to model the diffusion of information on Twitter. Meanwhile, Oo and Lwin [21] used PageRank to measure the influence of users on Twitter.…”

Section: Introductionmentioning

confidence: 99%

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Information Diffusion Model in Twitter: A Systematic Literature Review

et al. 2021

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Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics are sparse, especially on the use of continuous time Markov chain (CTMC). This paper examines the following topics: (1) the purposes of studies about information diffusion on Twitter, (2) the methods adopted to model information diffusion on Twitter, (3) the metrics applied, and (4) measures used to determine influencer rankings. We employed a systematic literature review (SLR) to explore the studies related to information diffusion on Twitter extracted from four digital libraries. In this paper, a two-stage analysis was conducted. First, we implemented a bibliometric analysis using VOSviewer and R-bibliometrix software. This approach was applied to select 204 papers after conducting a duplication check and assessing the inclusion–exclusion criteria. At this stage, we mapped the authors’ collaborative networks/collaborators and the evolution of research themes. Second, we analyzed the gap in research themes on the application of CTMC information diffusion on Twitter. Further filtering criteria were applied, and 34 papers were analyzed to identify the research objectives, methods, metrics, and measures used by each researcher. Nonhomogeneous CTMC has never been used in Twitter information diffusion modeling. This finding motivates us to further study nonhomogeneous CTMC as a modeling approach for Twitter information diffusion.

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Modeling Information Diffusion In Online Social Networks Using SEI Epidemic Model

Cited by 17 publications

References 9 publications

A Novel Metric to Quantify the Real-Time Robustness of Complex Networks With Respect to Epidemic Models

A Novel Metric to Quantify the Real-Time Robustness of Complex Networks With Respect to Epidemic Models

Public Opinion Dissemination with Incomplete Information on Social Network: A Study Based on the Infectious Diseases Model and Game Theory

Information Diffusion Model in Twitter: A Systematic Literature Review

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