Positive Unlabeled Fake News Detection via Multi-Modal Masked Transformer Network
文献类型：期刊论文
| 作者 | Wang, Jinguang3,4; Qian, Shengsheng2 ; Hu, Jun1; Hong, Richang3,4
|
| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| 出版日期 | 2024 |
| 卷号 | 26页码:234-244 |
| ISSN号 | 1520-9210 |
| 关键词 | Fake news detection multi-modal learning social media |
| DOI | 10.1109/TMM.2023.3263552 |
| 通讯作者 | Hong, Richang(hongrc.hfut@gmail.com) |
| 英文摘要 | 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. |
| 资助项目 | National Key Research and Development Program of China |
| 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:001140881500016 |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55551]  |
| 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.Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100049, Peoples R China 3.Hefei Comprehens Natl Sci Ctr, Inst Artificial Intelligence, Hefei 230009, Peoples R China 4.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。