MODELING RUMOR AND INFORMATION PROPAGATION IN SOCIAL NETWORKS THROUGH AN SLBRS EPIDEMIC MODEL

Source abstract

The rapid expansion of online social networks has significantly accelerated the dissemination of information, opinions, rumors, and misinformation, making the study of information diffusion an important interdisciplinary research area. Traditional compartmental epidemic models have been widely employed to describe spreading processes; however, most assume homogeneous mixing among individuals and fail to capture the heterogeneous interaction patterns that characterize real social networks. To address this limitation, this paper proposes a graph-based Susceptible–Latent–Breaking-out–Recovered–Susceptible (SLBRS) epidemic model for information diffusion on complex social networks. In the proposed framework, users are classified into five compartments: susceptible individuals who have not yet received the information, latent individuals who have been exposed but are not yet sharing it, breaking-out individuals who actively disseminate the information, recovered individuals who have ceased participating in the diffusion process, and re-susceptible individuals who become vulnerable to future information. The communication structure is represented by a network graph, where information transmission occurs only between connected users through the network adjacency matrix, providing a realistic description of localized interactions. The mathematical model is formulated as a system of nonlinear differential equations, and its equilibrium points, basic reproduction number, and local stability conditions are derived using linearization and the Routh–Hurwitz criterion. The influence of network topology on the epidemic threshold is also investigated through spectral properties of the adjacency matrix. The proposed model provides a comprehensive framework for analyzing rumor propagation, misinformation spread, and viral information diffusion in complex social networks. Furthermore, it offers valuable insights for designing effective intervention strategies to suppress harmful information and optimize information dissemination in digital communication systems.