A novel ensemble model for detecting fake news

Bensouda, Nissrine
El Fkihi, Sanaa
Faizi, Rdouan

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 13, No 1: March 2024

Due the growing proliferation of fake news over the past couple of years, ourobjective 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 optfor a blending technique that combines three models, namely bidirectional longshort-term memory (Bi-LSTM), stochastic gradient descent classifier and ridgeclassifier. The implementation of the proposed model (i.e. BI-LSR) on realworld datasets, has shown outstanding results. In fact, it achieved an accuracyscore of 99.16%. Accordingly, this ensemble learning has proven to do performbetter than individual conventional machine learning and deep learning modelsas well as many ensemble learning approaches cited in the literature.