In this paper, I introduce an agent-based approach that leverages Markov models to formalize and understand information propagation within social networks. This methodology aims to explain the internal information states of agents and employs probabilistic model checking to analyze these models. Initially, I provide a comprehensive overview of the current research on information diffusion in social networks. Following this, I delve into the fundamentals of model checking and illustrate how this technique can be used to assess model accuracy, particularly in managing large networks with high precision. To demonstrate the effectiveness of this approach, I conduct a series of experiments. The results show that this paper provides a robust and effective method for analyzing complex information diffusion and network behavior.