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DC-CNN: Dual-channel Convolutional Neural Networks with attention-pooling for fake news detection

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Abstract

Fake news detection mainly relies on the extraction of article content features with neural networks. However, it has brought some challenges to reduce the noisy data and redundant features, and learn the long-distance dependencies. To solve the above problems, Dual-channel Convolutional Neural Networks with Attention-pooling for Fake News Detection (abbreviated as DC-CNN) is proposed. This model benefits from Skip-Gram and Fasttext. It can effectively reduce noisy data and improve the learning ability of the model for non-derived words. A parallel dual-channel pooling layer was proposed to replace the traditional CNN pooling layer in DC-CNN. The Max-pooling layer, as one of the channels, maintains the advantages in learning local information between adjacent words. The Attention-pooling layer with multi-head attention mechanism serves as another pooling channel to enhance the learning of context semantics and global dependencies. This model benefits from the learning advantages of the two channels and solves the problem that pooling layer is easy to lose local-global feature correlation. This model is tested on two different COVID-19 fake news datasets, and the experimental results show that our model has the optimal performance in dealing with noisy data and balancing the correlation between local features and global features.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61772231), the Shandong Provincial Natural Science Foundation (ZR2017MF025), the Project of Shandong Provincial Social Science Program (18CHLJ39), the Project of Independent Cultivated Innovation Team of Jinan City (2018GXRC002), and the Shandong Provincial Key R&D Program of China (2021CXGC010103).

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Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022, China

    Kun Ma, Changhao Tang, Benkuan Cui, Ke Ji & Zhenxiang Chen

  2. Department of Computer and Software Engineering, Shandong College of Electronic Technology, Jinan, 250200, China

    Weijuan Zhang

  3. Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, WA, USA

    Ajith Abraham

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  1. Kun Ma
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Correspondence to Kun Ma.

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The data can be available at https://github.com/SmallZzz/FakeNewsData

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Ma, K., Tang, C., Zhang, W. et al. DC-CNN: Dual-channel Convolutional Neural Networks with attention-pooling for fake news detection. Appl Intell 53, 8354–8369 (2023). https://doi.org/10.1007/s10489-022-03910-9

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