Apprehension of Spurious Information - A Hybrid Architecture to Detect COVID Fake News

Abstract Propagation of false information has an enduring effect on the economy and can lead to political tensions. In order to counterattack this unexpected repercussion, a system has to be modelled to distinguish the hoax from the real. This paper proposes a deep learning based hybrid model that can determine if the news is veracious. The performance of the model is tested on four datasets including COVID fake news to ensure that it is able to generalise across different domains and achieve best results. Bidirectional Gated Recurrent Units emerges to be the ideal decoder to work along with the Convolutional Neural Network in this hybrid architecture while the words being represented using the glove.6B.50D embeddings provided to be the best representation when juxtaposed with Word2Vec, BERT Embeddings, glove.6B.100D and keras embedding layer. Experiments on the datasets show promising results with an overall accuracy of 80.17% on Disaster Tweets, 99% on ISOT dataset, 59.86% on LIAR and 90.05% on COVID Fake News dataset.