Detection of fake news using Machine Learning Algorithms

Abstract: Social media platforms like Facebook, Whatsapp, Twitter, and Telegram are important sources of information diffusion in the modern period, and people believe it with- out questioning its authenticity or source. Social media has fascinated people worldwide in spreading fake news due to its easy availability, cost-effectiveness, and ease of information sharing. The website – pmssgovt online claims that the scheme is applicable to all Indian students studying from class 9 to graduation degree. The website was fake and was charging Rs 450 as the registration fee for students to participate in the said scheme. Fake news can be generated to mislead the community for personal or commercial gains. It can also be used for other personal benefits such as defaming eminent personalities, amendment of government policies, etc. Thus, to mitigate the awful consequences of fake news, several research types have beenconducted for its detection with high accuracy to prevent its fatal outcome. This article proposes a framework for detecting fake news based on feature extraction and feature selection algorithmsand a set of voting classifiers. The proposed system distinguishes fake news from real news. First, we preprocessed the data taking unnecessary characters and numbers and reducing the words in the dictionary (lemmatization). Second, we extracted some important features by using two types of feature extraction, the term frequency-inverse document frequency technique and bag ofwords. Third, the extracted characteristics were reduced with the help of the different machine learning algorithm and the analysisof the variance algorithm. At last, challenges and open issues along with future research directions are discussed to facilitate the research in this domain further