Convolutional neural network with margin loss for fake news detection

The advent of online news platforms such as social media, news blogs, and online newspapers in recent years and their facilitated features such as swift information flow, easy access, and low costs encourage people to seek and raise their information by consuming their provided news. Furthermore, these platforms increase the opportunities for deceiver parties to influence public opinion and awareness by producing fake news, i.e., the news which consists of false and deceptive information and is published for achieving specific political and economic gains. Since the discerning of fake news through their contents by individuals is very difficult, the existence of an automatic fake news detection approach for preventing the spread of such false information is mandatory. In this paper, Convolutional Neural Networks (CNN) with margin loss and different embedding models proposed for detecting fake news. We compare static word embeddings with the non-static embeddings that provide the possibility of incrementally up-training and updating word embedding in the training phase. Our proposed architectures are evaluated on two recent well-known datasets in the field, namely ISOT and LIAR. Our results on the best architecture show encouraging performance, outperforming the state-of-the-art methods by 7.9% on ISOT and 2.1% on the test set of the LIAR dataset.