Memory-Guided Multi-View Multi-Domain Fake News Detection

doi:10.1109/TKDE.2022.3185151

	Memory-Guided Multi-View Multi-Domain Fake News Detection
	Zhu, Yongchun 1,2; Sheng, Qiang 1,2; Cao, Juan 1,2; Nan, Qiong 1,2; Shu, Kai 3; Wu, Minghui 4; Wang, Jindong 5; Zhuang, Fuzhen 6
	2023-07-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	35 期号:7 页码:7178-7191
摘要	The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a single domain (e.g., politics). However, correlations exist commonly across multiple news domains, and thus it is promising to simultaneously detect fake news of multiple domains. Based on our analysis, we pose two challenges in multi-domain fake news detection: 1) domain shift, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2) domain labeling incompleteness, stemming from the real-world categorization that only outputs one single domain label, regardless of topic diversity of a news piece. In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M3 FEND) to address these two challenges. We model news pieces from a multi-view perspective, including semantics, emotion, and style. Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics. Then, with enriched domain information as input, a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains. Extensive offline experiments on English and Chinese datasets demonstrate the effectiveness of M3 FEND, and online tests verify its superiority in practice. Our code is available at https://github.com/ICTMCG/M3FEND.
关键词	Fake news detection multi-domain learning multi-view learning memory bank
DOI	10.1109/TKDE.2022.3185151
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021AAA0140203] ; Zhejiang Provincial Key Research and Development Program of China[2021C01164] ; Project of Chinese Academy of Sciences[E141020] ; National Natural Science Foundation of China[62176014]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001004293600047
出版者	IEEE COMPUTER SOC
引用统计	被引频次：20[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/2XEOYT63/21252
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cao, Juan
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.IIT, Chicago, IL 60616 USA 4.Zhejiang Univ City Coll, Sch Comp & Comp Sci, Hangzhou 310015, Peoples R China 5.Microsoft Res Asia, Beijing 100080, Peoples R China 6.Beihang Univ, Sch Comp Sci, Inst Artificial Intelligence, SKLSDE, Beijing 100191, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Yongchun,Sheng, Qiang,Cao, Juan,et al. Memory-Guided Multi-View Multi-Domain Fake News Detection[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(7):7178-7191.
APA	Zhu, Yongchun.,Sheng, Qiang.,Cao, Juan.,Nan, Qiong.,Shu, Kai.,...&Zhuang, Fuzhen.(2023).Memory-Guided Multi-View Multi-Domain Fake News Detection.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(7),7178-7191.
MLA	Zhu, Yongchun,et al."Memory-Guided Multi-View Multi-Domain Fake News Detection".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.7(2023):7178-7191.