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

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.