Multimodal fake news detection through data augmentation-based contrastive learning
During the information exploding era, news can be created or edited purposely for promoting the spreading of social influence. However, unverified or fabricated news can also spread unscrupulously, leading to serious consequences, such as poor decisions or even health risk. Thus, in order to discrim...
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| Published in | Applied soft computing Vol. 136; p. 110125 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | During the information exploding era, news can be created or edited purposely for promoting the spreading of social influence. However, unverified or fabricated news can also spread unscrupulously, leading to serious consequences, such as poor decisions or even health risk. Thus, in order to discriminate the fake news, several fake news detection approaches have been proposed and the majority of these methods suffer from low efficacy of detection, due to the lack of multimodal information and the small data size. Hence, we develop a novel machine learning based framework, i.e., BERT-based back-Translation Text and Entire-image multimodal model with Contrastive learning (TTEC). In this framework, the text of news is first back-translated encouraging the framework to learn some general characteristics regarding a particular topic. Secondly, both textual and visual features are fed into a BERT-based model in order to produce multimodal features. Thirdly, the contrastive learning is utilized to derive more reasonable multimodal representations through utilizing similar news published in the past. Eventually, to demonstrate the effectiveness of the proposed framework, extensive experiments are conducted and the results show our method outperforms the state of art methods by 3.1% on Mac. F1 scores.
•We present a new multimodal fake news detection framework with contrastive learning.•The combination of back-translation and contrastive learning is proved effective.•The specific effects of contrastive learning and different image forms are discussed.•Several SOTA models are compared and extensive experiments are conducted. |
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| AbstractList | During the information exploding era, news can be created or edited purposely for promoting the spreading of social influence. However, unverified or fabricated news can also spread unscrupulously, leading to serious consequences, such as poor decisions or even health risk. Thus, in order to discriminate the fake news, several fake news detection approaches have been proposed and the majority of these methods suffer from low efficacy of detection, due to the lack of multimodal information and the small data size. Hence, we develop a novel machine learning based framework, i.e., BERT-based back-Translation Text and Entire-image multimodal model with Contrastive learning (TTEC). In this framework, the text of news is first back-translated encouraging the framework to learn some general characteristics regarding a particular topic. Secondly, both textual and visual features are fed into a BERT-based model in order to produce multimodal features. Thirdly, the contrastive learning is utilized to derive more reasonable multimodal representations through utilizing similar news published in the past. Eventually, to demonstrate the effectiveness of the proposed framework, extensive experiments are conducted and the results show our method outperforms the state of art methods by 3.1% on Mac. F1 scores.
•We present a new multimodal fake news detection framework with contrastive learning.•The combination of back-translation and contrastive learning is proved effective.•The specific effects of contrastive learning and different image forms are discussed.•Several SOTA models are compared and extensive experiments are conducted. |
| ArticleNumber | 110125 |
| Author | Tang, Keke Hua, Jiaheng Cui, Xiaodong Zhu, Peican Li, Xianghua |
| Author_xml | – sequence: 1 givenname: Jiaheng surname: Hua fullname: Hua, Jiaheng organization: School of Computer Science, Northwestern Polytechnical University (NWPU), Xi’an 710072, China – sequence: 2 givenname: Xiaodong surname: Cui fullname: Cui, Xiaodong email: xiaodong.cui@nwpu.edu.cn organization: School of Marine Science and Technology, NWPU, Xi’an 710072, China – sequence: 3 givenname: Xianghua surname: Li fullname: Li, Xianghua organization: School of Artificial Intelligence, Optics and Electronics (iOPEN), NWPU, Xi’an 710072, China – sequence: 4 givenname: Keke surname: Tang fullname: Tang, Keke organization: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China – sequence: 5 givenname: Peican orcidid: 0000-0002-8389-1093 surname: Zhu fullname: Zhu, Peican email: ericcan@nwpu.edu.cn organization: School of Artificial Intelligence, Optics and Electronics (iOPEN), NWPU, Xi’an 710072, China |
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| Keywords | Fake news detection Multimodal framework Machine learning Contrastive learning |
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