In this paper, we discuss the scenario where true and false information coexist in networks and interact with individuals. Our model is extended from the classic SIR (Susceptible-Infectious-Recovered) model in epidemiology, where individuals are categorized into three states representing whether they have encountered information. Additionally, in order to make the model more realistic, we incorporated attributes that reflect individuals' preferences for true or false information. We then use differential equations to analyze the spread of true and false information in the model, deriving stable conditions through stability analysis. Finally, we use numerical results to illustrate how each state in the model evolves over time, showing that even if initially there are an equal number of individuals preferring true and false information, the final outcome will be that more individuals accept true information than false information as the information spreads through the model.