基于标记信息级联传播树特征的谣言检测新方法

近年来新浪微博已成为国内重要的社交媒体平台之一，然而该类平台开放的匿名环境给谣言提供了滋生、传播的温床，造谣传谣给社会带来的消极影响不容忽视。传统的基于特征的谣言检测研究主要关注消息文本、发布用户、传播等方面的静态扁平特征，忽略了对消息传播演化结构和传播群体反应等方面的研究。针对此问题，首先将消息传播的级联模型引入标记传播树（LPT）模型中，提出改进的标记信息级联传播树模型（CA-LPT）；在此模型下提出一种动态度量用户影响力的方法；然后提出10个新特征以扩充已有的静态扁平特征集，再利用基于随机通路图核和RBF核的混合核支持向量机（SVM）进行谣言检测；最后通过基于新浪微博真实数据集的实验分析，验证了所提方法能提升谣言检测的性能。

A novel rumor detection method based on features of labeled cascade propagation tree

Nowadays SinaWeibo has become one of the most popular social media platforms both at home and abroad. However, the open and anonymous environment of this type of platforms provides rumors a perfect hotbed to breed and spread, and the negative influence on society from rumors cannot be ignored. Traditional rumor detection methods based on features often focus on static flat features of message contents, users, propagation and so on, but the information propagation structure and the reaction of the propagation group are not fully studied. Aiming at this problem, first we introduce the cascade model of information propagation into the labeled propagation tree (LPT) and propose an improved model Labeled Cascade Propagation Tree (CA LPT). Secondly, we investigate users’ influence assessment by a dynamic method. Finally, we predict whether a microblog post is a rumor by applying 10 flat features with new features and hybrid kernel SVM based on random walk graph kernel and RBF kernel. Extensive experiments on real world data from SinaWeibo demonstrate that the proposed method can improve the performance of rumor detection.