Performance Analysis of a Self-Organized Network Dynamics Model for Public Opinion Information

Abstract

With the rise of social networks, various types of information have emerged in the vision field in a complex manner, making it crucial to analyze the propagation patters of online public opinion to effectively guide information dissemination. To elucidate the dynamics of information dissemination, this paper proposes a directed network information based on self-organized network information dissemination scenario. This model takes into account the influence of networks formed by users spontaneously and distinguishes the dissemination population based on the in-and out-degree of user nodes in the network. To assess the model's performance, it is evaluated using real retweets from Chinese Sina Weibo, considering the impact of user interactions on information dissemination. Comparing real data with model-fitted data, the proposed model-based evaluation and numerical analysis demonstrate that the forwarding and transfer probabilities align with actual information dissemination. Furthermore, the evaluation sensitivity analyses describe the key factors influencing information dissemination, aiding decision-making in formulating strategies to guide public opinion. To quantify the importance of these factors, assessment metrics are introduced, such as the propagation regeneration number.