DC-CNN: Dual-channel Convolutional Neural Networks with Attention-pooling for Fake News Detection
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.
Contributions
Dynamic word embedding for Chinese text (abbreviated as DWtext). DWtext is the dynamic word embedding method proposed by DC-CNN. It performs word segmentation on social media data according to Chinese syntactic characteristics. By filtering the redundant parts of word segmentation, it cannot only retain the features useful for classification, but also reduce the noise data. The adjacent word segmentation is expressed in the form of "N-gram" and related to each other, allowing the model to learn the order information between words. The conditional probability of intermediate word vectors is utilized to predict contextual word vectors, so that word vectors with the same context have similar semantics. Compared with traditional static word embedding, DWtext can dynamically generate word vectors with different contexts, which can reduce semantic ambiguity in a more effective way. Therefore, corresponding word vector can be generated by the model even confronted with words exclusive the training vocabulary.
Dual-channel CNN with Attention Pooling (abbreviated as DC-CNN). DC-CNN replaces the traditional pooling layer with a Dual-channel pooling layer, which incorporates with a Max-pooling layer and an Attention-pooling layer. The Max-pooling layer can remarkably decrease redundant features by allowing neurons in one layer to solely focus on active features while ignoring inactive ones. However, it is also followed by the easy loss of dependencies between local features. In that case, attention-pooling is adopted as a remedy. It has a great advantage in capturing long-distance dependencies, which provides a natural advantage of our model to learn global semantics. Thus, DC-CNN, combining the benefits of the two channels, can effectively compensate for the lack of correlation between local features and global features.
