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

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.

Long text feature extraction network with data augmentation.

The spread of COVID-19 has had a serious impact on either work or the lives of people. With the decrease in physical social contacts and the rise of anxiety on the pandemic, social media has become the primary approach for people to access information related to COVID-19. Social media is rife with rumors and fake news, causing great damage to the Society. Facing shortages, imbalance, and nosiness, the current Chinese data set related to the epidemic has not helped the detection of fake news. Besides, the accuracy of classification was also affected by the easy loss of edge characteristics in long text data. In this paper, long text feature extraction network with data augmentation (LTFE) was proposed, which improves the learning performance of the classifier by optimizing the data feature structure. In the stage of encoding, Twice-Masked Language Modeling for Fine-tuning (TMLM-F) and Data Alignment that Preserves Edge Characteristics (DA-PEC) was proposed to extract the classification features of the Chinese Dataset. Between the TMLM-F and DA-PEC processes, we use Attention to capture the dependencies between words and generate corresponding vector representations. The experimental results illustrate that this method is effective for the detection of Chinese fake news pertinent to the pandemic.