A micro-level approach for modeling rumor propagation in online social networks

Online social networks have become the major platforms for information dissemination in recent years. However, rapid propagation of rumors in these networks as a special form of information can greatly influences social lives. Hence, work on rumor propagation models and analysis is under great attention by the research communities. Previously, researchers have proposed various models to explore the dynamics of rumor propagation and analyze steady-state. However, most of them did not consider people’s behavior differences in the spreading or opposing rumor. To overcome this limitation, we assume that individuals have different probability of spreading rumor, spreading anti-rumor and stifling. In this paper we introduce a new model for rumor propagation in social networks considering these differences at micro-level. The proposed model which considered both types of rumor and anti-rumor messages on people decision is an agent-based model in terms of probabilistic automata network. To evaluate the proposed model, we conduct a number of Monte-Carlo simulation experiments on Barabasi-Albert model of social networks that show the accuracy of the proposed model. We also conduct interesting sensitivity analysis to see the effects of different model parameters on the dynamics of the rumor propagation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.