Public Opinion Evolution in Cyberspace: A Case Analysis of Pelosi's Visit to Taiwan

• Published in: IEEE transactions on computational social systems Vol. 11; no. 1; pp. 1 - 11
• Main Authors: Chen, Tao, Zhang, Baoyu, Wang, Xiao, Zhang, Weishan, Hon, Chitin, Di, Wang, Chen, Long, Li, Qiang, Wang, Fei-Yue
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Autoregressive processes; Clustering; Clustering algorithms; Computing costs; Cyberspace; Digital media; Evolution; Heuristic algorithms; Large-graph cluster; Media; Pelosi visiting Taiwan; Public opinion; public opinion analysis; Social networking (online); Social networks; text cluster; topic model
• ISSN: 2329-924X; 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2023.3239046

The dynamics of public opinion on social media affects people's feeling and minds about international affairs and leads to the reconstruction of societal states for international conflicts. In this article, we analyze the topics' evolution on social media during the Pelosi visit. Such kind of analysis should help the related departments sense and beware the situation effectively and efficiently, and may provide technical supports for proper policy making and responses. To facilitate this purpose, a new method is proposed and an abbreviated large-graph clustering (ALGC) algorithm has been designed to generate documents and topic representation for alleviating the overhead of high computational complexity of large graphs by reducing the dimensionality of the attention matrix and adjacency matrix. The evolution pattern of topics is also analyzed in and between different time periods. Experiment results show that the proposed method performs well, achieving a high clustering accuracy with lower computational cost. The dataset used in this article is also released for public analysis.