Tensor Decomposition and Graph Convolutional Network for Fake News Classification

Abstract

With the widespread of fake news on social media, its impact has become a major concern of the public, so accurate detection methods are urgently needed. However, these methods rarely investigate the sentence interaction patterns of different news articles, and most of them do not consider fine-grained fake news classification. To overcome these issues, We proposes a method that constructs a graph representation for news articles and employs a graph neural network (GNN) to classify fake news. The method uses the local word co-occurrence information of sentences to obtain the interaction relationship between sentences, which is abstracted by the weight matrix of the graph representation. A third-order co occurrence tensor is built, and the weight matrix is calculated based on the canonical polyadic (CP) decomposition of this tensor. The computed representations can capture more accurate contextual information of news articles. The results on two real-world datasets demonstrate that the proposed method outperforms the competing methods in both binary and multiclass classification tasks.In particular, for multiclass classification on the selected dataset with 70% of the training set for training, the improvements got good accuracy and F1-score.