Multi-Modal Meta Multi-Task Learning for Social Media Rumor Detection

doi:10.1109/TMM.2021.3065498

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Multi-Modal Meta Multi-Task Learning for Social Media Rumor Detection
	Zhang, Huaiwen 2,3; Qian, Shengsheng 2,3; Fang, Quan 2,3; Xu, Changsheng 1,2,3
刊名	IEEE TRANSACTIONS ON MULTIMEDIA
	2022
卷号	24 页码:1449-1459
关键词	Task analysis Social networking (online) Feature extraction Learning systems Semantics Media Blogs Meta learning multi-modal multi-task learning rumor detection social media
ISSN号	1520-9210
DOI	10.1109/TMM.2021.3065498
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要	With the rapid development of social media platforms and the increasing scale of the social media data, the rumor detection task has become vitally important since the authenticity of posts cannot be guaranteed. To date, Many approaches have been proposed to facilitate the rumor detection process by utilizing the multi-task learning mechanism, which aims to improve the performance of rumor detection task by leveraging the useful information in the stance detection task. However, most of the existing approaches suffer from three limitations: (1) only focus on the textual content and ignore the multi-modal information which is key component contained in social media data; (2) ignore the difference of feature space between the stance detection task and rumor detection task, resulting in the unsatisfactory usage of stance information; (3) largely neglect the semantic information hidden in the fine-grained stance labels. Therefore, in this paper, we design a Multi-modal Meta Multi-Task Learning (MM-MTL) framework for social media rumor detection. To make use of multiple modalities, we design a multi-modal post embedding layer which considers both textual and visual content. To overcome the feature-sharing problem of the stance detection task and rumor detection task, we propose a meta knowledge-sharing scheme to share some higher meta network-layers and capture the meta knowledge behind the multi-modal post. To better utilize the semantic information hidden in the fine-grained stance labels, we employ the attention mechanism to estimate the weight of each reply. Extensive experiments on two Twitter benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance.
资助项目	National Key Research, and Development Program of China[2017YFB1002804] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61802405] ; National Natural Science Foundation of China[62072456] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61936005] ; National Natural Science Foundation of China[U1705262] ; Key Research Program of Frontier Sciences, CAS[QYZDJSSWJSC039] ; Open Research Projects of Zhejiang Laboratory[2021KE0AB05] ; K.C. Wong Education Foundation ; CCF-Tencent Open Fund
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000776227200017
资助机构	National Key Research, and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; Open Research Projects of Zhejiang Laboratory ; K.C. Wong Education Foundation ; CCF-Tencent Open Fund
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48216]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者	Xu, Changsheng
作者单位	1.Peng Cheng Lab, Shenzhen 518055, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Huaiwen,Qian, Shengsheng,Fang, Quan,et al. Multi-Modal Meta Multi-Task Learning for Social Media Rumor Detection[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2022,24:1449-1459.
APA	Zhang, Huaiwen,Qian, Shengsheng,Fang, Quan,&Xu, Changsheng.(2022).Multi-Modal Meta Multi-Task Learning for Social Media Rumor Detection.IEEE TRANSACTIONS ON MULTIMEDIA,24,1449-1459.
MLA	Zhang, Huaiwen,et al."Multi-Modal Meta Multi-Task Learning for Social Media Rumor Detection".IEEE TRANSACTIONS ON MULTIMEDIA 24(2022):1449-1459.