基于主题和预防模型的微博谣言检测

针对微博短文本存在的特征提取困难及微博谣言传播浪费网络资源的问题,提出了基于主题和预防模型的微博谣言检测.对微博进行主题提取,按主题分类后提取基于用户、传播结构、内容三方面的统计特征.将样本与官方谣言子集中的微博进行相似度计算,将其值与传统特征进行特征融合之后作为统计特征进入有监督的机器学习.实验结果表明,相对于传统的有监督机器学习,该方法将微博谣言检测的性能提升了3%左右,同时实现了谣言预防.

Rumor Detection in Microblogs Based on Topic and Prevention Model

In view of the difficulty in extracting features of Weibo''s short texts and the existence of a large number of officially certified microblogs that had not been used efficiently, a microblog rumors detection method was proposed based on topics and prevention model. Firstly, the official rumors were extracted and categorized according to the subject and were organized according to the user, spread frame and content characters, forming the subsets of official rumors based on a certain topic. And then, the similarity between the microblog and the official rumors with an identical topic was calculated. Merging the values with the traditional features, the result was taken as statistical features put into supervised machine learning. Finally, some experiments were carried out to validate the detection method. The results show that, compared with the traditional supervised machine learning, the method can improve the performance of Weibo rumors detection by about 3%, and can achieve rumor prevention.