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Multimodal Disentangled Domain Adaption for Social Media Event Rumor Detection

文献类型：期刊论文

作者Zhang, Huaiwen1,3; Qian, Shengsheng1,3; Fang, Quan1,3; Xu, Changsheng1,2,3
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2021
卷号23页码:4441-4454
关键词Social networking (online) Feature extraction Task analysis Adaptation models Writing Visualization Training Disentanglement representation learning domain adaptation event rumor detection social media
ISSN号1520-9210
DOI10.1109/TMM.2020.3042055
通讯作者Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要With the rapid development of social media and the increasing scale of social media data, the rumor detection on social media platforms has become vitally crucial. The key challenges for rumor detection on social media platforms are how to identify rumors deeply entangled with the specific content and how to detect rumors for the emerging social media events without labeled data. Unfortunately, most of the existing approaches can hardly handle these challenges since they tend to learn event-specific features and cannot transfer the learned features to newly emerged events. To tackle the above challenges, we propose a novel Multimodal Disentangled Domain Adaption (MDDA) method which can derive event-invariant features and thus benefit the detection of rumors on emerging social media events. The model consists of two components: the multimodal disentangled representation learning and the unsupervised domain adaptation. The multimodal disentangled representation learning is responsible for disentangling the multimedia posts into the content features and the rumor style features, and removing the content-specific features from post representation. The unsupervised domain adaptation aims to filter out the event-specific features and keep shared rumor style features among events. Based on the final event-invariant rumor style features, we train a robust social media rumor detector that can transfer knowledge from source events to the target events, which can perform well on the newly emerged events. Extensive experiments on two Twitter benchmark datasets demonstrate that our rumor detection model outperforms state-of-the-art methods.
资助项目National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61572503] ; National Natural Science Foundation of China[61802405] ; National Natural Science Foundation of China[61872424] ; National Natural Science Foundation of China[61702509] ; 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[QYZDJ-SSW-JSC039] ; K.C.Wong Education Foundation
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000728924800004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; K.C.Wong Education Foundation
源URL[http://ir.ia.ac.cn/handle/173211/46776]  
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Peng Cheng Lab, Shenzhen 518066, 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. Multimodal Disentangled Domain Adaption for Social Media Event Rumor Detection[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:4441-4454.
APA Zhang, Huaiwen,Qian, Shengsheng,Fang, Quan,&Xu, Changsheng.(2021).Multimodal Disentangled Domain Adaption for Social Media Event Rumor Detection.IEEE TRANSACTIONS ON MULTIMEDIA,23,4441-4454.
MLA Zhang, Huaiwen,et al."Multimodal Disentangled Domain Adaption for Social Media Event Rumor Detection".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):4441-4454.

入库方式： OAI收割

来源：自动化研究所

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