Towards early identification of online rumors based on long short-term memory networks

doi:10.1016/j.ipm.2018.11.003

	Towards early identification of online rumors based on long short-term memory networks
	Liu, Yahui 1; Jin, Xiaolong 2,3; Shen, Huawei 2,3
	2019-07-01
发表期刊	INFORMATION PROCESSING & MANAGEMENT
ISSN	0306-4573
卷号	56 期号:4 页码:1457-1467
摘要	In the social media environment, rumors are constantly breeding and rapidly spreading, which has become a severe social problem, often leading to serious consequences (e.g., social panic and even chaos). Therefore, how to identify rumors quickly and accurately has become a key prerequisite for taking effective measures to curb the spread of rumors and reduce their influence. However, most existing studies employ machine learning based methods to carry out automatic rumor identification by extracting features of rumor contents, posters, and static spreading processes (e.g., follow-ups, thumb-ups, etc.) or by learning the presentation of forwarding contents. These studies fail to take into account the dynamic differences between the spreaders and diffusion structures of rumors and non-rumors. To fill this gap, this paper proposes Long Short-Term Memory (LSTM) network based models for identifying rumors by capturing the dynamic changes of forwarding contents, spreaders and diffusion structures of the whole (in the afterwards identification mode) or only the beginning part (in the halfway identification mode, i.e., early rumor identification) of the spreading process. Experiments conducted on a rumor and non-rumor dataset from Sina Weibo show that the proposed models perform better than existing baselines.
关键词	Rumor identification Long short-term memory network Forwarding content Spreader Diffusion structure
DOI	10.1016/j.ipm.2018.11.003
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB1000902] ; National Key Research and Development Program of China[2017YFC0820404] ; National Natural Science Foundation of China[61772501] ; National Natural Science Foundation of China[61572473] ; National Natural Science Foundation of China[61572469] ; National Natural Science Foundation of China[91646120] ; Youth Innovation Promotion Association CAS ; CCF-Tencent RAGR[20160107] ; Shihezi university[ZZZC2017508]
WOS研究方向	Computer Science ; Information Science & Library Science
WOS类目	Computer Science, Information Systems ; Information Science & Library Science
WOS记录号	WOS:000469907200018
出版者	ELSEVIER SCI LTD
引用统计	被引频次：49[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4210
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jin, Xiaolong
作者单位	1.Shihezi Univ, Comp Network Ctr, Shihezi, Peoples R China 2.Chinese Acad Sci, ICT, Key Lab Network Data Sci & Technol, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yahui,Jin, Xiaolong,Shen, Huawei. Towards early identification of online rumors based on long short-term memory networks[J]. INFORMATION PROCESSING & MANAGEMENT,2019,56(4):1457-1467.
APA	Liu, Yahui,Jin, Xiaolong,&Shen, Huawei.(2019).Towards early identification of online rumors based on long short-term memory networks.INFORMATION PROCESSING & MANAGEMENT,56(4),1457-1467.
MLA	Liu, Yahui,et al."Towards early identification of online rumors based on long short-term memory networks".INFORMATION PROCESSING & MANAGEMENT 56.4(2019):1457-1467.