Fake news detection on Bengali linguistic using deep learning approach

Abstract

A vast quantity of data and information are available on the internet. Because the internet is so widely available and has resulted in a tremendous growth in the number of online news, people are interested in reading news from online news portals. Online news portals include things like Facebook, Twitter, WhatsApp, Telegram, Instagram, blogs, and more. Both the quantity of news-on-news websites and the number of readers is increasing. But how real is online news today is a matter of thought. A huge amount of fake news is being spread in newspapers and online due to various yellow journalists. Which is having an adverse effect on society. As a result, there are many kinds of instability, bad politics, etc. problems are being created in the country. If this situation continues, our country and society will go to hell. The only solution is to ensure that yellow journalists do not spread fake news. But despite all the vigilance, fake news will spread. We can solve this by using artificial intelligence, for example, by employing various machine learning and deep learning algorithms, we can identify bogus news and take precautions against it. In this paper, fake news is detected using 4 deep learning algorithms like RNN, LSTM, BiLSTM, GRU model and 1 machine learning algorithm BERT model. RNN has an accuracy of 94.58%, LSTM has an accuracy of 92.84%, BiLSTM has an accuracy of 94.29%, GRU has an accuracy of 93.22% and BERT has an accuracy of 95%. The BERT model has the highest accuracy of 95% among all models.