Deep spatial-temporal structure learning for rumor detection on Twitter

doi:10.1007/s00521-020-05236-4

CSpace > 应用数学研究所

	Deep spatial-temporal structure learning for rumor detection on Twitter
	Huang, Qi 1,3; Zhou, Chuan 2,3; Wu, Jia 4; Liu, Luchen 1,3; Wang, Bin 5
	2020-08-08
发表期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
页码	11
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.
关键词	Rumor detection Spatial-temporal structure learning
DOI	10.1007/s00521-020-05236-4
收录类别	SCI
语种	英语
资助项目	NSFC[11688101] ; NSFC[61872360] ; ARC DECRA[DE200100964] ; Youth Innovation Promotion Association CAS[2017210]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000557856900002
出版者	SPRINGER LONDON LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/51962
专题	应用数学研究所
通讯作者	Zhou, Chuan
作者单位	1.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China 4.Macquarie Univ, Dept Comp, Fac Sci & Engn, Sydney, NSW, Australia 5.Xiaomi AI Lab, 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.