文献类型：期刊文献

英文题名：Empower rumor events detection from Chinese microblogs with multi-type individual information

作者：Wang, Zhihong[1];Guo, Yi[1,2,3]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China;[2]Natl Engn Lab Big Data Distribut & Exchange Techn, Shanghai, Peoples R China;[3]Shanghai Engn Res Ctr Big Data & Internet Audienc, Shanghai, Peoples R China

年份：2020

卷号：62

期号：9

起止页码：3585

外文期刊名：KNOWLEDGE AND INFORMATION SYSTEMS

收录：;EI(收录号：20211810284661);WOS:【SCI-EXPANDED(收录号:WOS:000525174300001)】；

基金：This research is financially supported by National Natural Science Foundation of China (Grant Number 61462073) and Science and Technology Committee of Shanghai Municipality (STCSM) (Grant Numbers 17DZ1101003, 18511106602 and 18DZ2252300).

语种：英文

外文关键词：Rumor events detection; Dynamic time series; Individual information; Sentiment dictionary; GRU; Chinese microblogs

摘要：Online social media has become an ideal place in spreading rumor events with its convenience in communication and information dissemination, which raises the difficulty in debunking rumor events automatically. To deal with such a challenge, traditional classification approaches relying on manually labeled features have to face a daunting number of human efforts. With the consideration of the realness of a rumor event, it will be verified and authenticated with multi-type individual information, especially with individuals' emotional expressions to events and their own credibility. This paper presents a novel two-layer GRU model for rumor events detection based on multi-type individual information (MII) and a dynamic time-series (DTS) algorithm, named as MII-DTS-GRU. Specifically, MII refers to adopt the sentiment dictionary to identify fine-grained human emotional expressions to events and fuse with the individual credibility. Besides, the DTS algorithm retains the time distribution of social events. Experimental results on Sina Weibo datasets show that our model achieves a high accuracy of 96.3% and demonstrate that our proposed MII-DTS-GRU model outperforms the state-of-the-art models on rumor events detection.