Research on Microblog Rumor Events Detection via Dynamic Time Series Based GRU Model

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.