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

文献类型：期刊论文

作者Zhu, Yongchun4,5; Sheng, Qiang4,5; Cao, Juan4,5; Nan, Qiong4,5; Shu, Kai1; Wu, Minghui6; Wang, Jindong2; Zhuang, Fuzhen3
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2023-07-01
卷号35期号:7页码:7178-7191
ISSN号1041-4347
关键词Fake news detection multi-domain learning multi-view learning memory bank
DOI10.1109/TKDE.2022.3185151
英文摘要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.
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
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:001004293600047
源URL[http://119.78.100.204/handle/2XEOYT63/21251]  
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.IIT, Chicago, IL 60616 USA
2.Microsoft Res Asia, Beijing 100080, Peoples R China
3.Beihang Univ, Sch Comp Sci, Inst Artificial Intelligence, SKLSDE, Beijing 100191, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
6.Zhejiang Univ City Coll, Sch Comp & Comp Sci, Hangzhou 310015, 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.

入库方式： OAI收割

来源：计算技术研究所

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