Impact of message fatigue in information-disease coupled dynamics on temporal simplicial networks

Abstract

During information diffusion, individuals typically experience fatigue and mental exhaustion due to repeated exposure to similar information, which is a state called message fatigue. To address message fatigue in information diffusion and group interactions, we developed a novel coupled information-disease spreading model within the framework of multiplex temporal networks. Here, the information network simulates the process of disease-related information diffusion using a temporal simplicial network. Furthermore, we utilize a threshold model to characterize the effects of message fatigue. We describe the dynamic evolutionary equations of the model by the microscopic Markov chain (MMC) approach to determine the epidemic threshold. We also validated the proposed model using a number of Monte Carlo (MC) simulations. The experimental results of the MC simulations and MMC theoretical results are in good agreement. Our research has shown that message fatigue can lead to a two-stage change in the information-disease coupled spreading. Raising the threshold for message fatigue and preventing a large number of individuals from experiencing it can help inhibit the disease spreading. In short, excessive publicizing of related-disease information can backfire. The results can facilitate us to accurately capture the spreading characteristics of real-world diseases.