Deceptive narrative ml: unveiling fake news

Abstract

The problem of fake news is a big issue nowadays, with difficulties being caused by false information. This study examines the use of machine learning (ML) to detect fake information. Fake news, which often appears real, makes people believe things that aren't true and causes problems in politics, society, and health. Regular fact-checking struggles to keep up with the quick spread of online misinformation, The traditional method of factchecking faces limitations in keeping pace with the rapid dissemination of misinformation online, highlighting the need for more advanced and efficient approaches. So, using Machine Learning (ML) seems like a better way to deal with this problem. In this study, we focus on making different ML programs better at finding fake news. These programs include the 98.9% accurate Bi-Directional Long Short-Term Memory (Bi-Directional LSTM) and the 99.2% accurate LSTM with Word Embedding Model, Gated Recurrent Unit (GRU) Model with 98% accuracy, and Recurrent Neural Network (RNN) with an accuracy of 99.03%, are chosen because they are good at understanding the order of words, catching language details, and figuring out the context—important for telling if news is real or fake, make it little it long from the last sentence.