Design Of Hybrid Expert Framework For Fake News Prediction Using Machine Learning Techniques

Abstract

Fake News Detection has been identified as one of the important predictions in recent scenarios with the inception of various profiles in social media with an intent not only to popularise negative and false news in social media but also to indulge in malicious activities that could result in loss of data or illegal activities through the utilization of social media profiles. Fake news had been predicted from time to time with technologies that were predominant at that time like Data Mining, Machine Learning, and Artificial Intelligence, and in the current scenario, even deep learning models were used. The major objective of this research paper is to design a Hybrid Expert Fake News Predictive Framework (HEFNPF) using machine learning models to predict fake news in social media using numerical factors and test them using classifiers. The model comprised three major phases including linguistic conversion with pre-processing, Feature extraction with model building, and finally prediction with classifier test and ruleset generations.