Deep spatial-temporal structure learning for rumor detection on Twitter

doi:10.1007/s00521-020-05236-4

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	Deep spatial-temporal structure learning for rumor detection on Twitter
	Huang, Qi 4,5; Zhou, Chuan 2,5; Wu, Jia 1; Liu, Luchen 4,5; Wang, Bin 3
刊名	NEURAL COMPUTING & APPLICATIONS
	2020-08-08
页码	11
关键词	Rumor detection Spatial-temporal structure learning
ISSN号	0941-0643
DOI	10.1007/s00521-020-05236-4
英文摘要	The widespread of rumors on social media, carrying unreal or even malicious information, brings negative effects on society and individuals, which makes the automatic detection of rumors become particularly important. Most of the previous studies focused on text mining using supervised models based on feature engineering or deep learning models. In recent years, another parallel line of works, which focuses on the spatial structure of message propagation, provides an alternative and promising solution. However, these existing methods in this parallel line largely overlooked the temporal structure information associated with the spatial structure in message propagation. Actually the addition of temporal structure information can make the message propagations be classified from the perspective of spatial-temporal structure, a more fine-grained perspective. Under these observations, this paper proposes a spatial-temporal structure neural network for rumor detection, termed as STS-NN, which treats the spatial structure and the temporal structure as a whole to model the message propagation. All the STS-NN units are parameter shared and consist of three components, including spatial capturer, temporal capturer and integrator, to capture the spatial-temporal information for the message propagation. The results show that our approach obtains better performance than baselines in both rumor classification and early detection.
资助项目	NSFC[11688101] ; NSFC[61872360] ; ARC DECRA[DE200100964] ; Youth Innovation Promotion Association CAS[2017210]
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER LONDON LTD
WOS记录号	WOS:000557856900002
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/51961]
专题	应用数学研究所
通讯作者	Zhou, Chuan
作者单位	1.Macquarie Univ, Dept Comp, Fac Sci & Engn, Sydney, NSW, Australia 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Xiaomi AI Lab, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Huang, Qi,Zhou, Chuan,Wu, Jia,et al. Deep spatial-temporal structure learning for rumor detection on Twitter[J]. NEURAL COMPUTING & APPLICATIONS,2020:11.
APA	Huang, Qi,Zhou, Chuan,Wu, Jia,Liu, Luchen,&Wang, Bin.(2020).Deep spatial-temporal structure learning for rumor detection on Twitter.NEURAL COMPUTING & APPLICATIONS,11.
MLA	Huang, Qi,et al."Deep spatial-temporal structure learning for rumor detection on Twitter".NEURAL COMPUTING & APPLICATIONS (2020):11.