Open AccessEmpirical Evaluation of Machine Learning Classification Algorithms for Detecting COVID19 Fake News
Author(s) -
Hiba Alsaidi,
Wael Etaiwi
Publication year - 2022
Publication title -
international journal of advances in soft computing and its applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.15
H-Index - 18
eISSN - 2710-1274
pISSN - 2074-8523
DOI - 10.15849/ijasca.220328.04
Subject(s) - naive bayes classifier , machine learning , artificial intelligence , support vector machine , computer science , fake news , decision tree , recall , logistic regression , algorithm , precision and recall , statistical classification , psychology , internet privacy , cognitive psychology
Humans have been fighting the Covid19 pandemic since it started, not just to protect their wellbeing but also to counteract the news and rumors that have been spreading about it. Rumors and false allegations can be almost as dangerous as the virus, as they affect people's mental health and increase their stress levels. To address this problem, several machine learning techniques could be used to detect fake news. In this paper, four different machine learning algorithms are compared according to their ability to detect fake news, including Naive Bayes, Decision Tree, Support Vector Machines, and Logistic Regression. A dataset of annotated news is used in the experiments. The experimental results show that Naïve Bayes outperforms other algorithms in terms of accuracy, precision, recall, and F1 score. Keywords: COVID-19, Machine Learning, Fake news detection.