A novel SIR-based model for containing misinformation on social media

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 results indicate that systematically increasing the exposure of accurate, verified information to key segments of the population can slow down or even completely combat the spread of misinformation. This effect is particularly pronounced when the true information is disseminated by trusted sources, highlighting the importance of credibility in combating falsehoods. 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.