An Improved Independent Cascade Model for Opinion Propagation and Prediction in Signed Networks

With the rapid development of social media, the speed and breadth of information dissemination have increased substantially, leading to more complex patterns in the emergence and evolution of online public opinion. Compared to unsigned networks, signed networks more accurately capture supportive and adversarial relationships among users. Although the traditional Polarity-Related Independent Cascade model (IC-P) can describe opinion propagation in signed networks, its capability remains limited when applied to complex social environments. To address this issue, this paper improves the IC-P model by incorporating a Prisoner’s Dilemma game to establish a user propagation-choice mechanism. Furthermore, activation probability and activation thresholds are redesigned from the perspectives of authority effect, homophily, and temporal decay, resulting in an Independent Cascade model incorporating Communication Choice and Polarity (ICC-P). Using three real-world negative public opinion datasets collected from the Sina Weibo platform spanning from March to April 2024, Monte Carlo simulations were conducted and compared with the main baseline models. Experimental results indicate that, relative to the best existing baselines, ICC-P reduces the mean absolute error of the prediction of the propagation scale by approximately 43% and reduces the mean absolute error of the prediction of the sentiment distribution of the nodes by approximately 57%, demonstrating significant improvements in both propagation fitting accuracy and sentiment prediction performance.