Fake News Detection Using Machine Learning and BERT Model

In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent research, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analysed in a unidirectional way. Two new fake news datasets are introduced, one obtained through crowdsourcing and covering six news domains, and another one obtained from the web covering celebrities. Our best-performing models achieved accuracies that are comparable to human ability to spot fake content. A bidirectional training approach is a priority for modelling the relevant information of fake news that is capable of improving the classification performance with the ability to capture semantic and long-distance dependencies in sentences. In this paper, we propose a BERT-based (Bidirectional Encoder Representations from Transformers) deep learning approach (FakeBERT) by combining different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT.