State of the Art Machine Learning Techniques for Detecting Fake News

Abstract

The social media has significantly changed how we communicate and exchange information throughout time. Along with it comes the issue of fake news' quick spread, which may have detrimental effects on both people and society. Fake news has been surfacing often and in enormous quantities online for a variety of political and economic goals. To increase the appeal of their publications, fake news publishers employ a number of stylistic strategies, one of which is stirring up readers' emotions. To increase the appeal of their publications, fake news publishers employ a number of stylistic strategies, one of which is stirring up the feelings of readers. As an outcome, it is now extremely difficult to analyses bogus news so that the creators may verify it through data processing channels without misleading the public. It is necessary to implement a system for fact-checking claims, especially those that receive thousands of views and likes before being disputed and disproved by reliable sources. Numerous machine learning algorithms have been applied to accurately identify and categories bogus news. A ML classifier was used in this investigation to determine if news was phony or authentic. On the dataset, the proposed model and other benchmark methods are assessed using the best characteristics. Results from the classification show that our suggested model (CNNs) performs better than the current models with a precision of 98.13%.