Using Supervised Learning Models for Creating a New Fake News Analysis and Classification of a COVID-19 Dataset: A case study on Covid-19 in Iran

Abstract

Today, the growth of the coronavirus as a pandemic and its global expansion is a significant concern in our society and the international community. However, in recent years, many individuals have shifted their major source of news and information to social networks. Consequently, the widespread dissemination of false and misleading information on social media is significant for most politicians. Our effort is not only against COVID-19 but against an “infodemic” as well. To address this, on COVID-19, we have collected and released a labeled dataset of 7,000 social media postings Persian data, and articles of authentic and false news. Covid 19 fake news has been detected in other languages such as Arabic, English, Chinese, and Hindi. We execute a multi-label task (actual vs. fictitious) on the labeled dataset and compare it to six machine learning baselines: Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. On the test set, the support vector machine gives us the best results, with an 89 percent accuracy rate.