Comparative Analysis of Machine Learning Models for Detection of Fake News: A Case Study of Covid-19

Abisola Olayiwola (Department of Computer Engineering, Olabisi Onabanjo University)
Ajibola Oluwafemi Oyedeji (Department of Computer Engineering, Olabisi Onabanjo University)
Oluwakemi Omoyeni (Department of Computer Engineering, Olabisi Onabanjo University)
Oluwafemi Ayemimowa (Department of Computer Engineering, Olabisi Onabanjo University)
Mubarak Olaoluwa (Telecom Physique Strasbourg, University of Strasbourg)

Journal of Information Technology and Computer Engineering
Vol 7 No 01 (2023): Journal of Information Technology and Computer Engineering

During and after the Covid-19 pandemic, people rely heavily on the internet for information because of its easy accessibility. However, the spread of fake information through this medium has been fast-growing, especially during and after the pandemic. This study, therefore, aims to evaluate the performance of 5 machine learning models used in detecting Covid-19 fake news. The models were trained using the Covid-19 dataset gathered online. The dataset contains 7,262 real news and 9,727 fake news, totalling 16,989 news altogether. 80% of this dataset was used for training the models while 20% was used for testing them. The support vector machine (SVM) with 95%, 95%, 97% and 96% for the accuracy, precision, recall and F1-score respectively was the best classifier for detecting Covid-19 fake news and has shown a better performance than the other algorithms.