G-GERT network model of online public opinion reversal based on kernel and grey degree
Shuli Yan, Xiangyan Zeng, Pingping Xiong, Na Zhang
Abstract
Purpose In recent years, online public opinion reversal incidents have been occurring frequently, which has increased the complexity of the evolution of online public opinion, and they have become a difficult issue for public opinion management and control. It is of great significance to explore the regularity of online public opinion reversal. Design/methodology/approach Combined with the grey characteristics of online public opinion information, a grey graphical evaluation review technique (G-GERT) network model is constructed based on kernel and grey degree, and the frequency, probability and time of online public opinion reversal nodes are calculated using C -marking method and Z -marking method. Findings Throughout the online public opinion reversal events, there are all repeated outbreak nodes occurring, so the authors regard the repeated occurrence of outbreak nodes as reversal. According to the average frequency, probability and time of repeated outbreak nodes in the G-GERT network model, the authors predict the corresponding key information of reversal. It can simulate the evolution process of public opinion events accurately. Originality/value The G-GERT network model based on kernel and grey degree reveals the regulation of public opinion reversal, predicts the frequency, probability and time of reversal nodes, which are the most concerned and difficult issues for decision-makers. The model provides the decision basis and reference for government decision-making departments.