基于梯度提升决策树的微博虚假消息检测

微博是信息共享的重要平台，同时，也成为虚假消息产生和推广的重要平台，虚假消息的传播严重扰乱了社会秩序。为了快速、有效地识别微博虚假消息，提出一种基于梯度提升决策树（GBDT）的虚假消息检测方法。首先，从评论的角度分析微博虚假消息和真实消息之间存在的差异，在此基础上提取评论中的文本内容、用户属性，信息传播和时间特性的分类特征；然后，基于分类特征，采用GBDT算法实现微博虚假消息识别模型；最后，在两个真实的微博数据集上进行验证。实验结果表明，基于GBDT的识别模型能有效提高微博虚假消息检测的准确率。

Micro-blog misinformation detection based on gradient boost decision tree

Micro-blog has become an important platform for information sharing. Meanwhile, it is also one of the main ways for spreading of different misinformation. In order to detect the micro-blog misinformation quickly and effectively, a method based on Gradient Boost Decision Tree (GBDT) was proposed. Firstly, classification features of content, user properties, information dissemination and time characteristic were extracted from the comments of micro-blog. Then an identification model based on GBDT algorithm was proposed to detect misinformation. Finally, two real micro-blog datasets were used to verify the efficiency and effectiveness of the model. The experimental results show that the proposed model can effectively improve the accuracy of micro-blog misinformation detection.