Positive Unlabeled Fake News Detection via Multi-Modal Masked Transformer Network

doi:10.1109/TMM.2023.3263552

	Positive Unlabeled Fake News Detection via Multi-Modal Masked Transformer Network
	Wang, Jinguang 1,2; Qian, Shengsheng3 ; Hu, Jun 4; Hong, Richang 1,2
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
	2024
卷号	26 页码:234-244
通讯作者	Hong, Richang(hongrc.hfut@gmail.com)
摘要	Fake news detection has gotten continuous attention during these years with more and more people have been posting and reading news online. To enable fake news detection, existing researchers usually assume labeled posts are provided for two classes (true or false) so that the model can learn a discriminative classifier from the labeled data. However, this supposition may not hold true in reality, as most users may only label a small number of posts in a single category that they are interested in. Furthermore, most existing methods fail to mask the noise or irrelevant context (i.e., regions or words) between different modalities to assist in strengthening the correlations between relevant contexts. To tackle these issues, we present a curriculum-based multi-modal masked transformer network (CMMTN) for positive unlabeled multi-modal fake news detection by jointly modeling the inter-modality and intra-modality relationships of multi-modal information and masking the irrelevant context between modalities. In particular, we adopt BERT and ResNet to obtain better representations for texts and images, separately. Then, the extracted features of images and texts are fed into a multi-modal masked transformer network to fuse the multi-modal content and mask the irrelevant context between modalities by calculating the similarity between inter-modal contexts. Finally, we design a curriculum-based PU learning method to handle the positive and unlabeled data. Massive experiments on three public real datasets prove the effectiveness of the CMMTN.
关键词	Fake news detection multi-modal learning social media
DOI	10.1109/TMM.2023.3263552
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
项目资助者	National Key Research and Development Program of China
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:001140881500016
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55552
专题	多模态人工智能系统全国重点实验室
通讯作者	Hong, Richang
作者单位	1.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China 2.Hefei Comprehens Natl Sci Ctr, Inst Artificial Intelligence, Hefei 230009, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100049, Peoples R China 4.Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore
推荐引用方式 GB/T 7714	Wang, Jinguang,Qian, Shengsheng,Hu, Jun,et al. Positive Unlabeled Fake News Detection via Multi-Modal Masked Transformer Network[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2024,26:234-244.
APA	Wang, Jinguang,Qian, Shengsheng,Hu, Jun,&Hong, Richang.(2024).Positive Unlabeled Fake News Detection via Multi-Modal Masked Transformer Network.IEEE TRANSACTIONS ON MULTIMEDIA,26,234-244.
MLA	Wang, Jinguang,et al."Positive Unlabeled Fake News Detection via Multi-Modal Masked Transformer Network".IEEE TRANSACTIONS ON MULTIMEDIA 26(2024):234-244.