Reality Check AI : Harnessing AI to Forecast and Unmask False Reporting

Abstract

The internet has revolutionized the way people consume information, but it has also led to a rise in fake news, which is concerning because of the possible effects it may have on society. This study investigates whether it is possible to detect fake news only by looking at text using deep learning algorithms. The ability of three neural network architectures to identify false information on the internet is suggested and assessed: DistilBERT, Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs). This dataset, called ISOT (In-Store Orders and Transactions), was first created for retail analytics but is now used as a standard for assessing false news detection algorithms. The goal of this research is to support continued initiatives to promote information integrity and fight false information