An Automatic Fake News Identification System using Machine Learning Techniques

Abstract

With the evolvement in technology and social media, the prevalence of fake news is rapidly increasing. It has become a new research field that is gaining popularity and requires attention. However, due to a scarcity of resources such as insufficient and invalid datasets along with analysis techniques, there are various challenges such as the flourishment of fake news, that are faced. It has a considerable influence on everyday lives, as well as in almost every single field, especially politics, and education. Hence, this condition requires attention to detect fake news for reducing distrust in the government systems. This article introduces a solution to fake news detection by implementing a model using various classification techniques. This work has been implemented with Decision Tree, Random Forest, Logistic Regression, and Passive Aggressive Classifier for identifying fake news. However, the outcome of the passive-aggressive classifier has resulted in the highest accuracy of 93.05%. Furthermore, this work can help in the real-time identification of fake news leading to maintaining people’s trust on social media and government systems.