A novel ensemble model for detecting fake news

Abstract

<div>Due the growing proliferation of fake news over the past couple of years, our</div><div>objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt</div><div>for a blending technique that combines three models, namely bidirectional long</div><div>short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge</div><div>classifier. The implementation of the proposed model (i.e. BI-LSR) on real</div><div>world datasets, has shown outstanding results. In fact, it achieved an accuracy</div><div>score of 99.16%. Accordingly, this ensemble learning has proven to do perform</div><div>better than individual conventional machine learning and deep learning models</div><div>as well as many ensemble learning approaches cited in the literature.</div>