Generalized Defensive Modeling of Fake News Propagation in Social Networks Using Fractional Differential Equations

IF 4.5 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-19 DOI:10.1109/TCSS.2024.3492097

Alfredo De Santis;Eslam Farsimadan;Leila Moradi;Francesco Palmieri

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引用次数: 0

Abstract

The rapid progress of Internet technology has led to a strong increase in the use of online social networks for disseminating information on the Internet. In this scenario, it is crucial to establish approaches that can effectively reduce the diffusion of false information (fake news) that can potentially cause harm to society. A defensive approach, based on integer-order differential equations, has been recently developed to analyze the effects of verification and blocking of users for containing the spread of fake news. Starting from it, we introduce a novel fractional model providing a more accurate, powerful, and realistic representation of the transmission of fake news messages. The model aims to predict the spread of such messages, by better considering the effect of the system's status evolution over time. The use of fractional differential equations to schematize the propagation of fake news results in incorporating a greater amount of memory information and better considering hereditary properties of the system of interest, also capturing its hidden nonlinear dynamics, mainly related to fractality and multiscale nature.

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基于分数阶微分方程的社交网络假新闻传播广义防御建模

互联网技术的飞速发展导致越来越多的人使用在线社交网络在互联网上传播信息。在这种情况下，建立能有效减少虚假信息（假新闻）传播的方法至关重要，因为虚假信息可能会对社会造成危害。最近，一种基于整阶微分方程的防御方法被开发出来，用于分析验证和阻止用户以遏制假新闻传播的效果。在此基础上，我们引入了一个新颖的分数模型，为假新闻信息的传播提供了更准确、更强大、更真实的表征。该模型旨在通过更好地考虑系统状态随时间演变的影响来预测此类信息的传播。使用分式微分方程来描绘假新闻的传播，可以纳入更多的记忆信息，更好地考虑相关系统的遗传特性，还能捕捉其隐藏的非线性动态，这主要与分形和多尺度性质有关。

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来源期刊

IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)

CiteScore

10.00

自引率

20.00%

发文量

316

期刊介绍： IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.