A Novel SIR-Based Model for Containing Misinformation on Social Media

Abstract

The widespread dissemination of misinformation through social media poses
significant challenges. With the increasing prevalence of social media, vast
amounts of information - both accurate and inaccurate - can be rapidly shared among a large audience. This information often plays a critical role in
shaping public opinion and influencing significant events, such as elections.
This paper addresses the urgent need for more effective models to combat
misinformation. We propose a novel approach based on the
Susceptible-Infective-Removed (SIR) model, traditionally used in studying
information diffusion. Our modified model, termed SIRMIS (SIR-based Misinformation Spreading model),
integrates stochastic differential equations (SDEs) to account for the inherent randomness and uncertainty in information diffusion processes on social networks. SIRMIS offers insights
into the dynamics of misinformation propagation, the role of accurate
information in counteracting falsehoods, and the estimation of the peak
number of misinformed users.
The stability of the stochastic equations within the SIRMIS model is rigorously proven using Lyapunov stability theory, ensuring that the model reliably predicts the conditions under which misinformation can be controlled or eradicated.
Our findings demonstrate that enhancing the
dissemination of true information can significantly reduce or even halt the
spread of misinformation. Furthermore, we discuss the linear and global
stability of the proposed model, emphasizing its potential in effectively
mitigating the impact of misinformation on social networks.