Synergistic control of negative information diffusion in improved semi-randomized epidemic networks

The rapid growth of online social networks has facilitated the sharing of positive information; however, it has also accelerated the dissemination of negative information, leading to widespread public panic and significant socio-economic consequences. Controlling the dissemination of negative information has become a critical issue in the field of information distribution within social networks. Therefore, there is an urgent need to develop effective control schemes that address the co-evolutionary dynamics between the spread of negative information and semi-randomized networks. To this end, we propose a node-based SEIR model that introduces noise interference within a semi-randomized epidemic network to analyze the dynamics of negative information diffusion under conditions of uncertainty. In addition, considering the constraints of control costs, we proposed five control strategies and systematically evaluated the effectiveness of their synergistic implementation in mitigating the propagation of negative information. Using Cesari’s Theorem, we prove the existence and uniqueness of the solution, and derive the optimal control signal through Pontryagin’s principle. Moreover, a forward-backward sweep algorithm is utilized to dynamically allocate resources and minimize costs. The simulation results confirm the accuracy of the theory and validate the effectiveness of the proposed control strategy. It reveals that a combination of correction, feedback, and intervention strategies can yield results comparable to those achieved through all five strategy combinations. Moreover, in the improved semi-randomized epidemic network, a proper level of noise intensity effectively suppresses the spread of negative information, indicating the presence of stochastic resonance.