A Comprehensive Approach to AI-Based Fake News Prediction in Digital Platforms by Applying Supervised Machine Learning Techniques

Abstract

In the modern era, the quantity of data, specifically the text data, has increased rapidly. Recently, fake news has gained a lot of attention worldwide. Generally, fake news is propagated through different social media. The effects of fake news may be economic, political, organizational, or personal. To save the life of community from fake news propagation, identification of fake news at an initial point is crucial. The fake news propagators target innocent people for spreading the fake news and they become a part of fake news propagation unknowingly. To prevent this kind of activity, fake news detection and its blueprint of propagation becomes crucial to community and government. This chapter makes an analysis related to the prediction of fake news through the help of supervised ML algorithms. The ML algorithms are adopted for the categorization of fake news as true or false with the help of NLP textual analysis and feature extraction. However, during the testing phase, the XGB algorithm gives the best result over other ML algorithms with an accuracy of 99%.