Peng Li-Jie, Shao Xi-Gao, Huang Wan-Ming
School of Mathematics and Statistics ScienceLudong University Yantai 264025 China.
IEEE Access. 2021 Mar 17;9:44162-44172. doi: 10.1109/ACCESS.2021.3066242. eCollection 2021.
The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the "CRITIC" method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion.
近年来互联网的迅速发展导致了社交媒体网络的激增，人们可以在网上聚集分享信息、知识和观点。然而，一旦重大突发事件出现，网络舆论往往会产生极具误导性的内容，大量信息可能会引起公众的震动。因此，我们需要在网络舆论预警中对潜在危机进行正确预测并及时识别。鉴于此，本研究充分考虑发展特征和传播特性，构建了一个包含4个一级指标和13个二级指标的网络舆论预警指标体系。通过“CRITIC”方法计算各指标权重，从而得到各时间点的综合评价值并划分网络舆论预警等级。然后，利用基于遗传算法的反向传播神经网络（GA-BP）建立网络舆论预警模型。最后，以重大突发公共卫生事件新冠肺炎疫情为例进行实证分析。结果表明，与传统分类方法如BP神经网络、决策树、随机森林、支持向量机和朴素贝叶斯相比，GA-BP神经网络对网络舆论预警具有更高的准确率。因此，本研究构建的指标体系和预警模型具有良好的可行性，可为网络舆论相关研究提供参考。
