Propagation and Intervention of Positive and Negative Online Public Opinion Based on ABC-SPNS Model

Online public opinion is disseminated and propagated by netizens, and predicting and intervening in public opinion through the law of netizens' emotional contagion is a highly practical and realistic approach. This study proposes an ABC-SPNS model based on the SIS epidemiological transmission model and Artificial Bee Colony (ABC) algorithm to simulate, predict, and intervene in public opinion. Additionally, this model is combined with the Particle Swarm Optimization (PSO) algorithm and simulated using actual epidemic news data in MATLAB software. The results demonstrate that positive and negative public opinions show a competitive relationship during public opinion dissemination, and the ABC-SPNS model, which is based on the law of emotional contagion, can simulate the evolution of public opinion. When combined with an optimal intervention algorithm, this model has a practical effect for public opinion guidance and control.