Enhancing Fake News Detection by Multi-Feature Classification

Abstract

The proliferation of social media platforms has significantly accelerated our access to news, but it has also facilitated the rapid dissemination of fake news. Automatic fake news detection systems can help solve this problem. Although there is much research in this area, getting an accurate detection system is still a challenge. This article proposes a novel model to increase the accuracy of fake news detection. The theory behind the proposed model is to extract and combine global, spatial, and temporal features of text to use in a new fast classifier. The proposed model consists of two phases: first, global features are extracted by TF-IDF, spatial features by a convolutional neural network (CNN), and temporal features by bi-directional long short-term memory (BiLSTM) simultaneously. Then a fast learning network (FLN) is used to efficiently classify the features. Extensive experiments were conducted using two publicly available fake news datasets: ISOT and FA-KES. These two have different sizes; therefore, the proposed architecture (CNN+BiLSTM+FLN) can be evaluated much better. Results demonstrate the proposed model's superiority in comparison with previous works.