Machine and Deep Learning based Fake News Detection Approaches using Natural Language Processing: A Review

Abstract

In this Era of digital science and social media, social media plays a vital role in everyone's life. Social media and world wide web provides a fast and easy platform to their user for sharing their thoughts, views experience etc. These platforms can also be misused to scatter rumors such as fake news articles. The spread of fake news articles has become a serious problem to deal with. Fake news can be defined as some sort of falsity information, that is scattered by someone for his own interest. Fake news may have many side effects like manipulating public opinion about something, defame of a genuine personality, monetary loss etc. Keeping in mind these side effects, there is a need of fake news detection methods which can work efficiently. Manual methods were used in old days to identify this type of falsity information but these manual methods were tedious and time consuming so automatic detection methods are required for the same. In recent past  many researchers are working to develop various auto-models to detect this kind of misinformation. Different models which are based on Machine learning have been developed for the identification of fake news articles spread through social media platforms. Deep learning solutions also caught Ears and Eyes of everyone in this field. In this paper we are going to present an analysis of various machine learning and deep learning based fake news detection techniques as well as the comparative study of these techniques in terms of used models.