A Competitive Guidance Topic Propagation Model Based on Evolutionary Game Theory

Le résumé fourni par la source

In contemporary society, online public opinion exerts significant influence, and guiding its trajectory is essential for maintaining social stability. To capture the interaction and evolution of competing guiding information during topic dissemination, a competitive guidance topic propagation model based on evolutionary game theory is developed. First, to address the interactive influence of multiple topic features, a multifactor analysis framework is established. Based on positive and negative sentiment, a metric for topic controversy is designed. Considering the effectiveness of multiple regression in measuring diverse features, multiple regression is introduced to evaluate the driving forces of different topic characteristics. This approach allows a detailed analysis of how various features affect both the dissemination impact and the dissemination potential. Second, to address the competition and cooperation among different types of guidance information, evolutionary game theory is introduced to design a user participation-driven game mechanism. The mechanism is used to analyze the competitive and cooperative dynamics between diverse guidance messages. It reveals the strategic landscape of positive and negative information dissemination and provides a theoretical foundation for modeling user state transitions. Finally, to address the dynamic nature of user state changes and considering the similarity between infectious disease transmission and topic propagation, a suspicion–hesitation–positive and negative information–immunity model is proposed. The model allows fine-grained modeling of user state transitions and provides effective theoretical support for public opinion management. Experimental results demonstrate that the proposed model effectively fits the propagation trends of guiding topics on the given dataset and captures competitive and cooperative relationships among different types of guiding information. Compared with baseline models, performance improves by at least 2.8%, providing a solution for modeling and analyzing the propagation process of guiding topics.

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