Fake News Classification using transformer based enhanced LSTM and BERT

Fake News has been a concern all over the world and social media has only amplified this phenomenon. Fake News has been affecting the world on a large scale as these are targeted to sway the decisions of the crowd in a particular direction. Since manually verifying the legitimacy of news is very hard and costly, there has been a great interest of researchers in this field. Different approaches to identifying fake news were examined, such as content-based classification, social context-based classification, image-based classification, sentiment-based classification, and hybrid context-based classification. This paper aims to propose a model for fake news classification based on news titles, following the content-based classification approach. The model uses a BERT model with its outputs connected to an LSTM layer. Training and evaluation of the model were done on the FakeNewsNet dataset which contains two sub-datasets, PolitiFact and GossipCop. A comparison of the model with base classification models has been done. A vanilla BERT model has also been trained on the dataset under similar constraints as the proposed model has to evaluate the impact same using an LSTM layer. The results obtained showed a 2.50% and 1.10% increase in accuracy on PolitiFact and GossipCop datasets respectively over the vanilla pre-trained BERT model.