An Information Dissemination Model Based on the Rumor and Antirumor and Cognitive Game

The rapid spread of online rumors has significant negative impacts on the online ecosystem and social order, which is closely tied to users’ cognitive traits. To explore the mechanisms of rumor propagation driven by cognition and mitigate its harm, we propose an information diffusion model based on the rumor, antirumor, and cognitive game. First, to address the uncertainty and difficulty in quantifying cognitive biases, and considering the advantages of fuzzy logic theory in handling uncertainty, a fuzzy logic-based algorithm for measuring user cognitive biases is proposed. Moreover, recognizing the nonlinear relationships among various factors, polynomial functions are introduced as the output of fuzzy rules to more accurately describe these complex relationships. Second, regarding the symbiotic and antagonistic relationships among multiple types of rumor information under the influence of cognitive biases, and leveraging the strengths of game theory in analyzing complex systems characterized by coexistence of symbiosis and antagonism, an evolutionary game-based rumor-antirumor user behavior mechanism is developed. This provides a robust theoretical foundation for understanding user state transitions and their evolutionary patterns. Finally, integrating the aforementioned research, and considering that the dynamic evolution of user cognition leads to variations in trust responses and attitudes toward rumor and antirumor information, the concepts of trust states—trust in rumor (TR) and trust in antirumor (TA)—are introduced into the classical susceptible-infectious-recovered (SIR) model. On this basis, a rumor propagation susceptible-trused-infectious-recovered (STIR) model incorporating user cognitive biases and evolutionary game theory is further constructed. Experimental results demonstrate that this model effectively reveals the game dynamics of multiple types of rumor information, providing a more efficient framework for studying rumor propagation in social networks.