Analyzing Online News Dissemination Patterns via Social Network Hypergraph Model

This study aims to develop a novel method for analyzing the complex dissemination patterns of online media news using a social network hypergraph model, addressing the limitations of traditional graph models in capturing many-to-many relationships in news dissemination. The author integrates news content, user nodes, and topic tags into a multi-dimensional hypergraph structure. The approach includes detailed analysis of key elements of news dissemination across four dimensions (subject, content, channel, and effect), construction of the hypergraph model, and design of mechanisms for extracting dissemination paths and evaluating influencing factors. Experiments were conducted on real-world data from multiple social platforms to validate the method's effectiveness. The results demonstrate that the proposed hypergraph model outperforms traditional models (GCN, GAT, and RF) in terms of accuracy, F1 value, and error control. The model effectively reflects the complex structure and dynamic evolution of news dissemination, revealing significant factors such as user activity, topic sensitivity, and structural entropy. This research offers a new perspective on understanding and optimizing online news dissemination by leveraging the hypergraph model's ability to capture multi-dimensional interactions. It provides a more comprehensive and accurate analysis framework, laying a theoretical foundation for constructing efficient information dissemination models.