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Multi-modal transformer using two-level visual features for fake news detection

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Abstract

Fake news with multimedia data is ubiquitous on the Internet nowadays, and it is difficult for users to distinguish them. Therefore, it is necessary to design automatic multi-modal fake news detectors. However, the existing works make poor utilization of visual information, and do not fully consider the semantic interaction of multi-modal data. In this paper, we propose the multi-modal transformer using two-level visual features (MTTV) for fake news detection. First, we model texts and images from news uniformly as sequences that can be processed by transformer, and two-level visual features, i.e. global feature and entity-level feature, are used to improve the utilization of news images. Second, we extend the transformer model for natural language processing to multi-modal transformer which can make multi-modal data interact fully and capture the semantic relationships between them. In addition, we propose a scalable classifier to improve the classification balance of fine-grained fake news detection with the problem of class imbalance. Extensive experiments on two public datasets demonstrate that our method achieved significant performance improvement compared to the state-of-the-art methods. The source code is available athttps://github.com/cqu-wb/MTTV.

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Acknowledgements

Supported by Zhejiang Lab (No. 2021KE0AB01), Open Fund of Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources (No. LMEE-KF2021008), Technology Innovation and Application Development Key Project of Chongqing (No. cstc2021jscx-gksbX0058), National Natural Science Foundation of China (No.62176029), and Guangxi Key Laboratory of Trusted Software (No. kx202006).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400030, China

    Bin Wang & Yong Feng

  2. Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, Chongqing, 401147, China

    Xian-cai Xiong

  3. Chongqing Institute of Planning and Natural Resources Investigation and Monitoring, Chongqing, 401121, China

    Xian-cai Xiong

  4. 8# of Zhejiang Lab, Yuhang District, Hangzhou, 311121, China

    Yong-heng Wang

  5. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Bao-hua Qiang

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  1. Bin Wang

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  2. Yong Feng

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  3. Xian-cai Xiong

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  4. Yong-heng Wang

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  5. Bao-hua Qiang

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Correspondence toYong Feng.

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Wang, B., Feng, Y., Xiong, Xc.et al. Multi-modal transformer using two-level visual features for fake news detection.Appl Intell53, 10429–10443 (2023). https://doi.org/10.1007/s10489-022-04055-5

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