文献类型：会议论文

英文题名：Research on Microblog Rumor Events Detection via Dynamic Time Series based GRU Model

作者：Wang, Zhihong[1];Guo, Yi[1,3,4];Li, Zhen[2];Tang, Minwei[2];Qi, Tianmei[1];Wang, Jixiang[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China;[2]China Telecom BestPay Co Ltd, Shanghai, Peoples R China;[3]Natl Engn Lab Big Data Distribut & Exchange Techn, Shanghai, Peoples R China;[4]Shihezi Univ, Xinjiang, Peoples R China

会议论文集：IEEE International Conference on Communications (IEEE ICC)

会议日期：MAY 20-24, 2019

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：rumor events detection; dynamic time series; GRU; microblogs

摘要：The convenience of online social media in communication and information dissemination has made it an ideal place for spreading rumor events and automatically debunking rumor events is a crucial problem. However, it is a challenging task to employ traditional classification approaches to rumor events detection since they rely on hand-crafted features which require daunting manual efforts. Besides, the various posts on a rumor event will debate its realness over time, and the distribution of the posts is special in time dimension. Thus, this paper presents a novel method for rumor event detection based on a dynamic time series (DTS) algorithm and a two layer Gated Recurrent Unit (GRU) model, named 2-GRU-DTS. The proposed model uses the DTS algorithm to retain the distribution information of social events over time and uses the two layers GRU model to learn the hidden event representations. Experimental results on real datasets from Sina Weibo demonstrate that our proposed 2-GRU-DTS model outperforms latest rumor event detection algorithms.