BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection
Nowadays, with the rapid growth of microblogging networks for news propagation, there are increasingly more people accessing news through such emerging social media. In the meantime, fake news now spreads at a faster pace and affects a larger population than ever before. Compared with traditional te...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/144285 |
| _version_ | 1811684004850565120 |
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| author | Zhang, Tong Wang, Di Chen, Huanhuan Zeng, Zhiwei Guo, Wei Miao, Chunyan Cui, Lizhen |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhang, Tong Wang, Di Chen, Huanhuan Zeng, Zhiwei Guo, Wei Miao, Chunyan Cui, Lizhen |
| author_sort | Zhang, Tong |
| collection | NTU |
| description | Nowadays, with the rapid growth of microblogging networks for news propagation, there are increasingly more people accessing news through such emerging social media. In the meantime, fake news now spreads at a faster pace and affects a larger population than ever before. Compared with traditional text news, the news posted on microblog often has attached images in the context. So how to correctly and autonomously detect fakes news in a multi-modal manner becomes a prominent challenge to be addressed. In this paper, we propose an end-to-end model, named BERT-based domain adaptation neural network for multi-modal fake news detection (BDANN). BDANN comprises three main modules: a multi-modal feature extractor, a domain classifier and a fake news detector. Specifically, the multi-modal feature extractor employs the pretrained BERT model to extract text features and the pretrained VGG-19 model to extract image features. The extracted features are then concatenated and fed to the detector to distinguish fake news. The role of the domain classifier is mainly to map the multi-modal features of different events to the same feature space. To assess the performance of BDANN, we conduct extensive experiments on two multimedia datasets: Twitter and Weibo. The experimental results show that BDANN outperforms the state-of-the-art models. Moreover, we further discuss the existence of noisy images in the Weibo dataset that may affect the results. |
| first_indexed | 2024-10-01T04:21:44Z |
| format | Conference Paper |
| id | ntu-10356/144285 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:21:44Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1442852020-10-27T01:46:23Z BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection Zhang, Tong Wang, Di Chen, Huanhuan Zeng, Zhiwei Guo, Wei Miao, Chunyan Cui, Lizhen School of Computer Science and Engineering 2020 International Joint Conference on Neural Networks (IJCNN) Engineering::Computer science and engineering Adaptation Models Fake News Detection Nowadays, with the rapid growth of microblogging networks for news propagation, there are increasingly more people accessing news through such emerging social media. In the meantime, fake news now spreads at a faster pace and affects a larger population than ever before. Compared with traditional text news, the news posted on microblog often has attached images in the context. So how to correctly and autonomously detect fakes news in a multi-modal manner becomes a prominent challenge to be addressed. In this paper, we propose an end-to-end model, named BERT-based domain adaptation neural network for multi-modal fake news detection (BDANN). BDANN comprises three main modules: a multi-modal feature extractor, a domain classifier and a fake news detector. Specifically, the multi-modal feature extractor employs the pretrained BERT model to extract text features and the pretrained VGG-19 model to extract image features. The extracted features are then concatenated and fed to the detector to distinguish fake news. The role of the domain classifier is mainly to map the multi-modal features of different events to the same feature space. To assess the performance of BDANN, we conduct extensive experiments on two multimedia datasets: Twitter and Weibo. The experimental results show that BDANN outperforms the state-of-the-art models. Moreover, we further discuss the existence of noisy images in the Weibo dataset that may affect the results. Accepted version 2020-10-27T01:46:23Z 2020-10-27T01:46:23Z 2020 Conference Paper Zhang, T., Wang, D., Chen, H., Zeng, Z., Guo, W., Miao, C., & Cui, L. (2020). BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), 1-8. doi:10.1109/IJCNN48605.2020.9206973 978-1-7281-6926-2 https://hdl.handle.net/10356/144285 10.1109/IJCNN48605.2020.9206973 1 8 en © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/IJCNN48605.2020.9206973 application/pdf |
| spellingShingle | Engineering::Computer science and engineering Adaptation Models Fake News Detection Zhang, Tong Wang, Di Chen, Huanhuan Zeng, Zhiwei Guo, Wei Miao, Chunyan Cui, Lizhen BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection |
| title | BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection |
| title_full | BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection |
| title_fullStr | BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection |
| title_full_unstemmed | BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection |
| title_short | BDANN : BERT-based domain adaptation neural network for multi-modal fake news detection |
| title_sort | bdann bert based domain adaptation neural network for multi modal fake news detection |
| topic | Engineering::Computer science and engineering Adaptation Models Fake News Detection |
| url | https://hdl.handle.net/10356/144285 |
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