Modeling a Rumor Propagation in Online Social Network: An Optimal Control Approach

Ghazzali, Rachid; Bhih, Amine El; Laaroussi, Adil El Alami; Rachik, Mostafa

doi:10.1155/2020/6724815

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…Using a system of four ODEs, Arindam and Md. Haider [15] proposed the following novel mathematical model of rumor propagation via online media. This topic was investigated as the dynamics of misinformation diffusion across the internet, with these ODEs serving as the model's four compartments.…”

Section: Introductionmentioning

confidence: 99%

Studying the Dynamics of the Rumor Spread Model with Fractional Piecewise Derivative

Alkahtani

Alzaid

2023

Symmetry

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Sensitively altered news, commonly referred to as rumors, can lead an individual, organization, or nation astray, potentially resulting in harm, even to the extent of causing violence among large groups of people. In this digital age, news can be easily twisted and rapidly spread through the internet and social media. It becomes challenging for consumers to discern whether the information they encounter online has been manipulated. Unfortunately, the rise of internet forgeries has facilitated the dissemination of false or distorted information by unscrupulous individuals, particularly on sensitive matters, to serve their own interests. Once a rumor is generated and made public on the internet, it quickly spreads through sharing and discussions by anonymous individuals, sometimes intentionally, without thorough fact-checking. In this manuscript, we investigate the dynamical model of rumor propagation in a social network using the classical Caputo piecewise derivative. We examine the existence and uniqueness of a solution for the aforementioned problem and analyze the equilibrium, stability, boundedness, and positivity of the model. To obtain the numerical simulation of the piecewise derivative, we employ various fractional orders, and the approximate solution of the considered model is found using the fractional piecewise numerical iterative approach of the Newton polynomial. This approach allows us to gain valuable insights into the dynamics of rumor propagation and its effects within a social network.

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Section: Introductionmentioning

confidence: 99%

Studying the Dynamics of the Rumor Spread Model with Fractional Piecewise Derivative

Alkahtani

Alzaid

2023

Symmetry

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“…As a result, the two factors are used as control variables to mitigate the spread of false information [ 43 ]. It can generally use network control strategies such as forced silence, information labeling, administrator control, and controlling the relative density of individual nodes to suppress online information transmission [ 4 , 44 – 46 ].…”

Section: Introductionmentioning

confidence: 99%

“…Few scholars consider the transmission individuals’ attributes. Generally, most scholars primarily focus on controlling and transmitting negative information in networks [ 44 , 46 ]. On the other hand, confidential information must be transmitted via the contact for information that must be taught in person, such as high technology or practical skills.…”

Section: Introductionmentioning

confidence: 99%

Dynamical analysis and optimal control of the developed information transmission model

Kang

Hou

et al. 2022

PLoS ONE

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Information transmission significantly impacts social stability and technological advancement. This paper compares the phenomenon of “Super transmission” and “Asymptomatic infection” in COVID-19 transmission to information transmission. The former is similar to authoritative information transmission individuals, whereas the latter is similar to individuals with low acceptance in information transmission. It then constructs an S2EIR model with transmitter authority and individual acceptance levels. Then, it analyzes the asymptotic stability of information-free and information-existence equilibrium on a local and global scale, as well as the model’s basic reproduction number, R0. Distinguished with traditional studies, the population density function and Hamiltonian function are constructed by taking proportion of “Super transmitter” and proportion of hesitant group turning into transmitters as optimization control variables. Based on the Pontryagin maximum principle, an optimal control strategy is designed to effectively facilitate information transmission. The numerical simulation corroborates the theoretical analysis results and the system’s sensitivity to control parameter changes. The research results indicate that the authoritative “Super transmitter” has a beneficial effect on information transmission. In contrast, the “Asymptomatic infected individual” with poor individual acceptance level negatively affects information transmission.

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“…e network is a vital concept to understand modern society structure and components of the society. e concept of the network has been applied not only to people but also to social network [1][2][3][4][5][6][7], biological network [8][9][10][11][12], metabolic regulation network [13], online social network [14,15], sports social network [16,17], structure of neural network [18][19][20], etc. As a result, the data for storing network information has become complex and diverse.…”

Section: Introductionmentioning

confidence: 99%

A Simple Visualization Method for Three-Dimensional (3D) Network

Kim

Lee

Park

et al. 2021

Discrete Dynamics in Nature and Society

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The network is a concept that can be seen a lot in many areas of research. It is used to describe and interpret datasets in various fields such as social network, biological network, and metabolic regulation network. As a result, network diagrams appeared in various forms, and methods for visualizing the network information are being developed. In this article, we present a simple method with a weight of information data to visualize the network diagram for the three-dimensional (3D) network. The generic method of network visualization is a circular representation with many intersections. When dealing with a lot of data, the three-dimensional network graphics, which can be rotated, are easier to analyze than the two-dimensional (2D) network. The proposed algorithm focuses on visualizing three factors: the position and size of the nodes and the thickness of the edge between linked nodes. In the proposed method, an objective function is defined, which consists of two parts to locate the nodes: (i) a constraint for given distance, which is the weight of the relationship among all the data, and (ii) the mutual repulsive force among the given nodes. We apply the gradient descent method to minimize the objective function. The size of the nodes and the thickness of the edges are defined by using the weight of each node and the weight between other nodes associated with it, respectively. To demonstrate the performance of the proposed algorithm, the relationships of the characters in the two novels are visualized using 3D network diagram.

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Modeling a Rumor Propagation in Online Social Network: An Optimal Control Approach

Cited by 8 publications

References 25 publications

Studying the Dynamics of the Rumor Spread Model with Fractional Piecewise Derivative

Studying the Dynamics of the Rumor Spread Model with Fractional Piecewise Derivative

Dynamical analysis and optimal control of the developed information transmission model

A Simple Visualization Method for Three-Dimensional (3D) Network

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