基于评论异常度的新浪微博谣言识别方法

The Method of Sina Weibo Rumor Detecting Based on Comment Abnormality

以微博为代表的社交媒体在为公众提供信息共享平台的同时,也为谣言提供了可乘之机.开展微博中谣言的识别和清理方法研究,对维护社会的安全稳定有着重要的现实意义.本文针对新浪微博平台中谣言识别的问题,提出了一种基于评论异常度的微博谣言识别方法.首先采用D-S理论实现微博评论异常度的计算方法;然后利用评论异常度与微博的内容特征、传播特征、用户特征对微博进行抽象表示;最后再利用SVM(Support vector machine)构建一个基于评论异常度的谣言识别模型,实现对新浪微博中谣言微博的识别.实验表明,本文提出的谣言识别模型对新浪微博中谣言识别具有较好的效果,谣言微博识别的F1值达到了96.2%,相较于现有文献的最好结果提高了1.3%.

Microblog plays an important role in social network service, while providing an information communication platform for users, it also provides a loophole for rumors. It is of great practical significance to automatically detect and clean up rumors in microblogs for the security and stability of society. In this paper, a rumor detecting method based on comment abnormality is presented. Firstly, we use D-S theory to implement the calculation method of comment abnormality. And then, we combine the comment abnormality, text features, propagation features and user characteristics to abstractly represent Sina Weibo. Finally, we use SVM(Support vector machine) to build a rumor detecting model based on comment abnormality. The experimental results show that the rumor detecting model proposed can effectively improve the detecting performance. And the F-measure of the rumor detecting is up to 96.2 %, which is up by 1.3 %compared with the best value in other literatures.