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

文献类型：期刊论文

作者Liu, Yahui3; Jin, Xiaolong1,2; Shen, Huawei1,2
刊名INFORMATION PROCESSING & MANAGEMENT
出版日期2019-07-01
卷号56期号:4页码:1457-1467
关键词Rumor identification Long short-term memory network Forwarding content Spreader Diffusion structure
ISSN号0306-4573
DOI10.1016/j.ipm.2018.11.003
英文摘要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.
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
语种英语
WOS记录号WOS:000469907200018
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/4209]  
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.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
2.Chinese Acad Sci, ICT, Key Lab Network Data Sci & Technol, Beijing, Peoples R China
3.Shihezi Univ, Comp Network Ctr, Shihezi, 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.

入库方式： OAI收割

来源：计算技术研究所

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