文献类型：期刊文献

英文题名：Rumor Events Detection From Chinese Microblogs via Sentiments Enhancement

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

机构：[1]East China Univ Sci & Technol, Shanghai 200237, Peoples R China;[2]Natl Engn Lab Big Data Distribut & Exchange Techn, Shanghai 200436, Peoples R China;[3]Shanghai Engn Res Ctr Big Data & Internet Audienc, Shanghai 200072, Peoples R China;[4]China Telecom BestPay Co Ltd, Shanghai 200085, Peoples R China

年份：2019

卷号：7

起止页码：103000

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20205009614803);WOS:【SCI-EXPANDED(收录号:WOS:000481688500211)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0807105, in part by the National Natural Science Foundation of China under Grant 61462073, and in part by the Science and Technology Committee of Shanghai Municipality (STCSM) under Grant 17DZ1101003, Grant 18511106602, and Grant 18DZ2252300.

语种：英文

外文关键词：Rumor events detection; sentiment dictionary; dynamic time series; GRU; online social networks

摘要：The convenience of social media in communication and information dissemination has made it an ideal place for spreading rumor events, which raises a higher requirement for automatic debunking of rumor events. Meanwhile, the traditional rumor classification approaches relying on manual labeled features have to face a daunting number of manual efforts. In general, when facing a dubious claim, people can authenticate and verify the realness of an event with the contents of continuous posts, such as source credibility, public sentiments, propagation structures, and so on. In this paper, we pay more attention to the emotional expressions of posts host, especially the fine-grained sentiments, which are effective for rumor events detection. Thus, this paper presents a novel two-layer GRU model for rumor events detection based on a Sentiment Dictionary (SD) and a dynamic time series (DTS) algorithm, named as SD-DTS-GRU. The model learns continuous representations of microblog events in a better manner by making use of the SD to identify fine-grained human emotional expressions of each event and retaining the time distribution of social events by the DTS algorithm. The experimental results on Sina Weibo datasets show that our model achieves a high accuracy of 95.2% and demonstrate that our proposed SD-DTS-GRU model outperforms latest explorations on rumor events detection.