A Hybrid LSTM-BERT and Glove-based Deep Learning Approach for the Detection of Fake News

Abstract

Since the growth of the internet, there has been an increase in the circulation offalse information. The very network that keeps us informed about what's going on in the world also provides the ideal environment for the spread of bad content and fake news. Fighting against this fake news is vital since information is what shapes people's perspectives around the world. People don't just establish their own beliefs, but also make significant judgments based on the information that they gather. Should this information turn out to be wrong, the repercussions might be catastrophic. It is entirely impossible for a person to verify each and every piece of news individually. This article has proposed a hybrid deep learning model based on LS TM and BERT with Glove followed by a feature extraction method using TFIDF vectorizer, implement machine learning methods like naive Bayes, ensemble learning, and XG-boost, and evaluate the performance using accuracy and loss, the BERT model outperform with accuracy 99% and 3% loss.