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

There has been a surge in disseminating disreputable data since the advent of the internet. Even if the internet helps keep us updated on current events, it facilitates the rapid dissemination misleading information and propaganda. Since information influences people’s perceptions everywhere, fighting against this bogus news is crucial. People use the information they gather to do more than form their opinions; they also make essential value judgments. If this data turns out to be inaccurate, it might have catastrophic consequences. It is unrealistic to expect a single person to confirm every news item independently. This paper proposes a hybrid deep learning model that combines LSTM and BERT with Glove. We implement machine learning methods such as naive Bayes, ensemble learning, and XG-boost. Finally, we analyze performance in terms of accuracy and loss and discover that the BERT model produces the optimal results.