Fake News Detection Using Unsupervised and Deep Learning Algorithms

Abstract

A detection of fake news is difficult due to limited publicly available resources (Datasets). Fake news is a false information which present in news or stories, blog so on. Fake news easily spread and damage the reputation of person or an organisation, therefore, detection of fake news is important. This project work detects fake news using unsupervised and deep learning algorithms. In unsupervised learning method One Class SVM (Support Vector Machine) and in deep learning method Hybrid CNN-RNN is implemented. Experimental results with NEWS dataset showed an accuracy of 58% for One Class SVM and 96.4% for Hybrid CNN-RNN. The proposed method performs better in terms of application performance compared to already existing Machine learning algorithms. This project can be further extended by exploiting high dimensional datasets in future.