Affiliation:
1. Rajkiya Engineering College, Azamgarh, India
Abstract
Online social networks (OSNs) have emerged as the most convenient platforms for transmitting and communicating media, including news and electronic content. It is imperative to develop technology that can mitigate the spread of fake information/rumors, which badly harm society. This chapter employs an epidemic approach to develop a model for controlling and examining the dissemination of fake information on OSNs. The model is designed in the form of a system of fractional differential equations, exploring the real-world effects of misinformation propagation in OSNs with memory effect. It incorporates the concept of physics-informed neural networks with approximation based on the theory of functional connection and extreme learning machines. The proposed model elucidates the impact of various measures for correcting misinformation and shows how misinformation spreads across different groups. The validity of the suggested OSN model is confirmed through extensive computational analysis and investigation.