基于突发话题和领域专家的微博谣言检测方法

针对现有谣言检测方法中存在的数据采集困难和谣言检测滞后的问题，提出一种基于动量模型的突发话题检测和领域专家发现的谣言检测方法。该方法借鉴物理学中的动力学理论对话题特征进行建模，使用特征的动力学物理量描述特征的突发特性和发展趋势，并在对突发特征进行特征聚合之后提取得到突发话题；然后，依据话题与用户个人信息的领域相关性在候选专家池中发现领域相关的微博用户来甄别话题信息的真实性。基于新浪微博数据的实验结果表明，相对于仅基于有监督机器学习的微博谣言识别方法，该方法谣言识别准确率提高了13个百分点；相对于主流人工识别方法，将最长谣言检测用时缩短至20h，能够较好地应用于实际的微博谣言检测环境。

Rumor detection method based on burst topic detection and domain expert discovery

It is difficult for existing rumor detection methods to overcome the disadvantage of data collection and detection delay. To resolve this problem, a rumor detection method based on burst topic detection inspired by the momentum model and domain expert discovery was proposed. The dynamics theory in physics was introduced to model the topic features spreading among the Weibo platform, and dynamic physical quantities of the topic features were used to describe the burst characteristics and tendency of topic development. Then, emergent topics were extracted after feature clustering. Next, according to the domain relativity between the topic and the expert, domain experts for each emergent topic were selected within experts pool, which is responsible for identifying the credibility of the emergent topic. The experimental results show that the proposed method gets 13 percentage points improvement on accuracy comparing with the Weibo rumor identification method based merely on supervised machine learning, and the detection time is reduced to 20 hours compared with dominating manual methods, which means that the proposed method is applicable for real rumor detection situation.