Detection of fake news using deep learning CNN–RNN based methods

Fake news is inaccurate information that is intentionally disseminated for a specific purpose. If allowed to spread, fake news can harm the political and social spheres, so several studies are conducted to detect fake news. This study uses a deep learning method with several architectures such as CNN, Bidirectional LSTM, and ResNet, combined with pre-trained word embedding, trained using four different datasets. Each data goes through a data augmentation process using the back-translation method to reduce data imbalances between classes. The results showed that the Bidirectional LSTM architecture outperformed CNN and ResNet on all tested datasets.