摘要： 新媒体对热点事件的迅速播报，使得舆情反转现象时有发生，识别舆情反转的影响因素，在事件发生之初预测是否会发生舆情反转有助于突发事件管理部门预判舆情发展方向，及时进行舆情引导，维护媒体公信力和网络生态环境健康发展。收集2017—2020年间的38个热点事件的热门微博，从事件、用户、信息、传播四个方面提出议程设置度、信息平衡性、微博报道时效性、评论/转发时效性、事件曝光者类型等30个特征，使用XGBoost计算不同特征在舆情反转预测中的重要性，结合逻辑回归、决策树、随机森林、XGBoost、高斯朴素贝叶斯五种机器学习方法构建舆情反转预测模型，并对模型进行训练和评估，找出最优预测模型。特征重要性实验结果表明，信息平衡性、事件曝光者类型、事件类型对于舆情反转预测的影响最为显著。五种预测模型中，基于随机森林和XGBoost的预测模型综合表现最好。本文分别从媒体、公众和平台三个方面对舆情反转事件的判别和治理提出了建议。

Abstract: The rapid broadcasting of hot events by new media makes the phenomenon of public opinion reversal occur from time to time. It is important to identify the influence factors of public opinion reversal, which can help predict the reversal of public opinion at the beginning of public events. Successful prediction of public opinion reversal can help emergency management departments predict and guide the development trend of public opinion in time. It also helps enhance the credibility of the media and maintain the healthy development of the online ecological environment. We crawled the hot microblog posts of the topics about 38 hot events on Sina Weibo from 2017 to 2020. Based on the previous research, we proposed the public opinion reversal prediction models that consisted of 30 features from four aspects of events, users, information, and dissemination. The XGBoost technique was used to calculate the importance of different features in the public opinion reversal prediction model. The Logistic Regression, Decision Tree, Random Forest, XGBoost and Gaussian Naive Bayes techniques were used to construct the public opinion reversal prediction models. The models were trained and evaluated in the experiment to find out the optimal prediction model. The experimental result showed that the information balance, the type of the user who exposes the event, and the event type had the most significant impact on the prediction performance of the public opinion reversal model. The prediction models based on the Random Forest and XGBoost techniques achieved the best performance among all the five public opinion reversal models. Suggestions were also made on the discrimination and governance of public opinion reversal from three aspects, i.e., media, the public and social media platform.

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