Structural Analysis of the Evolution Mechanism of Online Public Opinion and its Development Stages Based on Machine Learning and Social Network Analysis
Zixuan Liu, Xianwen Wu
Abstract
Abstract Internet public opinion is a complex and changeable system, and its trend development is characterized by explosive, evolutionary uncertainty, concealment and interactivity due to the participation of the vast number of Internet users. Today, with the rapid development of network information technology, public opinion has an increasing influence on the stable development of society. Computational intelligence is the frontier field of artificial intelligence development, and computational intelligence is used to mine and analyze public opinion text information and study the evolution of online public opinion. This paper uses the Changchun Changsheng Vaccine Incident as an example, and the netizens’ degree of attention to emergency-related keyword searches in the Baidu Index as a descriptive variable for the development of network public opinion. After applying the optimal segmentation algorithm, the development of public opinion is divided into phases. On this basis, a social network analysis is adopted to analyze the spatial and topological structure of each phase of network public opinion, using data from the Sina Weibo platform. Based on optimal segmentation, the development of network public opinion of the Changchun Changsheng Vaccine Incident can be divided into four phases, namely latent, spreading, control, and stable; each phase has different spatial and topological characteristics. Corresponding policy suggestions on network public opinion governance are put forward for each phase.