An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors

doi:10.1155/2020/7562567

	An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors
	Guo, Meicheng 1; Xu, Zhiwei 2,3; Liu, Limin 2; Guo, Mengjie 3; Zhang, Yujun 3
	2020-07-17
发表期刊	MATHEMATICAL PROBLEMS IN ENGINEERING
ISSN	1024-123X
卷号	2020 页码:12
摘要	With the extensive usage of social media platforms, spam information, especially rumors, has become a serious problem of social network platforms. The rumors make it difficult for people to get credible information from Internet and cause social panic. Existing detection methods always rely on a large amount of training data. However, the number of the identified rumors is always insufficient for developing a stable detection model. To handle this problem, we proposed a deep transfer model to achieve accurate rumor detection in social media platforms. In detail, an adaptive parameter tuning method is proposed to solve the negative transferring problem in the parameter transferring process. Experiments based on real-world datasets demonstrate that the proposed model achieves more accurate rumor detection and significantly outperforms state-of-the-art rumor detection models.
DOI	10.1155/2020/7562567
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1800403] ; National Key Research and Development Program of China[2016YFE0121500] ; National Natural Science Foundation of China[61902382] ; National Natural Science Foundation of China[61972381] ; National Natural Science Foundation of China[61672500] ; National Natural Science Foundation of China[61962045] ; National Natural Science Foundation of China[61502255] ; National Natural Science Foundation of China[61650205] ; Strategic Priority Research Program of Chinese Academy of Science[XDC0203500] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2017MS(LH)0601] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2018MS06003]
WOS研究方向	Engineering ; Mathematics
WOS类目	Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
WOS记录号	WOS:000556227500002
出版者	HINDAWI LTD
引用统计	被引频次：16[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/2XEOYT63/15835
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Zhiwei
作者单位	1.Inner Mongolia Univ Technol, Coll Informat Engn, Hohhot 100080, Peoples R China 2.Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 100080, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Guo, Meicheng,Xu, Zhiwei,Liu, Limin,et al. An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors[J]. MATHEMATICAL PROBLEMS IN ENGINEERING,2020,2020:12.
APA	Guo, Meicheng,Xu, Zhiwei,Liu, Limin,Guo, Mengjie,&Zhang, Yujun.(2020).An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors.MATHEMATICAL PROBLEMS IN ENGINEERING,2020,12.
MLA	Guo, Meicheng,et al."An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors".MATHEMATICAL PROBLEMS IN ENGINEERING 2020(2020):12.