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.

中文翻译：

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基于核和灰度的网络舆情逆转G-GERT网络模型

目的

近年来，网络舆情逆转事件频发，增加了网络舆情演化的复杂性，成为舆情管控的难题。探索网络舆论逆转的规律性具有重要意义。

设计/方法/方法

结合网络舆情信息的灰色特征，基于核和灰度构建灰色图形评价技术（G-GERT）网络模型，利用C计算网络舆情反转节点的频率、概率和时间-标记法和Z标记法。

发现

纵观网络舆情逆转事件，都有反复发生的爆发节点，因此作者将爆发节点的重复发生视为逆转。作者根据G-GERT网络模型中重复爆发节点的平均频率、概率和时间，预测出对应的逆转关键信息。它可以准确模拟舆情事件的演变过程。

原创性/价值

基于核和灰度的G-GERT网络模型揭示了舆情反转的规律，预测反转节点的频率、概率和时间，是决策者最关心和最难的问题。该模型为政府决策部门提供决策依据和参考。