Research Article
COVID-19 Fake News Detection Using Machine Learning Techniques: A Comparative Study
@ARTICLE{10.4108/eai.24-6-2022.174229, author={Amor Ben El Khettab Lalmi and Abderrahim Djaballah and Mohamed Gharzouli}, title={COVID-19 Fake News Detection Using Machine Learning Techniques: A Comparative Study}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={7}, number={22}, publisher={EAI}, journal_a={CS}, year={2022}, month={6}, keywords={Fake News Detection, COVID-19, Machine Learning, Artificial Intelligence, Natural Language Processing}, doi={10.4108/eai.24-6-2022.174229} }- Amor Ben El Khettab Lalmi
Abderrahim Djaballah
Mohamed Gharzouli
Year: 2022
COVID-19 Fake News Detection Using Machine Learning Techniques: A Comparative Study
CS
EAI
DOI: 10.4108/eai.24-6-2022.174229
Abstract
Fake news has become one of the most serious issues in recent years, especially on social media. For example, during the covid-19 pandemic, a great deal of false information about the virus spread easily and quickly through the internet. In this area, researchers have given substantial answers to this problem utilizing various machine learning techniques. However, there are some gaps that need to be clarified. In the context of COVID-19 fake news detection, in this study, we present a comparison of four major machine learning algorithms: SVM, Nave Bayes, Logistic Regression, and Random Forest. We proposed four new machine learning models by combining these algorithms with two feature extraction techniques (TF-IDF and CountVectorizer). On three datasets, we tested the suggested models and analyzed their performance. According to the obtained results, we concluded that some properties of the used datasets can affect the obtained results. In addition, we find the best model overall.
Copyright © 2022 A.B.K Lalmi et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
