Dynamic model with super spreaders and lurker users for preferential information propagation analysis

Fu, Minglei; Jun, Feng; Lande, Dmytro; Dmytrenko, Oleh; Manko, Dmytro; Prakapovich, Ryhor

doi:10.1016/j.physa.2020.125266

Cited by 27 publications

(13 citation statements)

References 34 publications

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“…Equation ( 5) follow the parameter definition in Equation (2), and the new parameters are defined as follows: N t refers to the total population of the system at the t time step; κ represents the immigration rate of the rumor propagation system, and µ represents the emigration rate of the rumor propagation system. To simplify the rumor spreading model, we assumed that the immigration rate was equal to the emigration rate [6][7][8]13], namely, κ = µ. From Equation (5), the total population of the rumor propagation system did not change over time, that is N t = N. Let x = X/N, y = Y/N, w = W/N, z 1 = Z 1 /N, z 2 = Z 2 /N, and x, y, w, z 1 , z 2 , respectively, represent the population density of ignorants, spreaders, skeptics, stiflers who believe the rumor, and stiflers who do not believe the rumor in the rumor spreading system.…”

Section: Rumor Propagation Model Xywz1z2-o In An Open Systemmentioning

confidence: 99%

“…With the exception of rationals, the incubators and wise individuals were introduced by Huo et al [3,8]; the counterattack group was introduced by Zan et al [11]; the exposed individuals by Xia et al [12]. In addition, considering the rumor propagation system has close contact with the outside world, some scholars take the population migration into account [6,7,13]. They first assumed that the rumor propagation system has constant immigration and emigration, then studied the impact of other influencing factors, such as delayed on the rumor spreading and aimed to figure out the stability of the equilibrium point and the final condition of rumor spreading.…”

Section: Introductionmentioning

confidence: 99%

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Spread Mechanism and Control Strategies of Rumor Propagation Model Considering Rumor Refutation and Information Feedback in Emergency Management

Chen

Song

et al. 2021

Symmetry

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The rumor-free equilibrium state and rumor-endemic equilibrium state are two symmetric descriptions of the status of a system. The constant spreading of rumors would affect the smooth operation of emergency management procedures and cause unnecessary social and economic loss. To reduce the negative effect of rumor propagation, in this paper, we introduce a compartmental model of rumor propagation, which considers the rumor refutation of public and information feedback. By deriving mean-field equations that describe the dynamics of the model, we use analytical and numerical solutions of these equations to investigate the threshold and dynamics of the model in both the closed system and open system. The results imply that the initial equilibrium point is not stable and there exists a rumor-free equilibrium point; in the open system, there exists a threshold beyond which rumors can spread; the stability of the initial equilibrium point is related to the threshold R0 = (φ*α)/μ, and there exists a rumor-endemic equilibrium point. The development process of rumor propagation can be divided into four stages: latent period, progressive period, intense period, and recession period. Under the influence of population, rumor spreading can exceed the threshold readily because the migration rate μ is usually less than the proportion of ignorants without critical ability φ, and the rumor spreading process in an open system presents a fluctuating development, the rumor would not disappear in this autonomous system. Based on the analysis, we propose some measures, such as providing open and efficient information queries and exchange platforms, etc.

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Section: Rumor Propagation Model Xywz1z2-o In An Open Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spread Mechanism and Control Strategies of Rumor Propagation Model Considering Rumor Refutation and Information Feedback in Emergency Management

Chen

Song

et al. 2021

Symmetry

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show abstract

“…And the average response period of lurkers seems to be at least twice that of active ones. Taking lurkers' behaviors into consideration, Fu et al [44] proposed SEAIR (Susceptible -Lurker -Super -Normal -Recovered) model to capture diffusion dynamics of super spreaders and lurkers.…”

Section: Impact Of Lurkers On the Information Diffusionmentioning

confidence: 99%

“…Time-series models design a series of mathematical expressions for diffusion scenarios. These achievements include user interaction status and transition rules [44,72,127], user behavior laws (e.g., delay [60,152], distribution of user behaviors [219], opinion adoption [136]), the intensity of social event occurrence [158,228], periodicity [8,97,126], etc. The effectiveness of these mathematical models has been verified in multiple scenarios.…”

Section: ) Data Collection and Processingmentioning

confidence: 99%

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Li,

Xia,

Wang

et al. 2021

Preprint

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Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing.) Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.CCS Concepts: • Human-centered computing → Social networks.

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“…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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Dynamic model with super spreaders and lurker users for preferential information propagation analysis

Cited by 27 publications

References 34 publications

Spread Mechanism and Control Strategies of Rumor Propagation Model Considering Rumor Refutation and Information Feedback in Emergency Management

Spread Mechanism and Control Strategies of Rumor Propagation Model Considering Rumor Refutation and Information Feedback in Emergency Management

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

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

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