Exploratory Data Analysis and Implications for Detecting Fake News Related to COVID-19

Abstract

False information and fake news about infectious diseases can be a huge threat, as they involve human lives and can cause a lot of social confusion. In fact, more than 800 people died around the world from January to March 2020 due to fake news related to COVID-19. In a sea of information, identifying fake news becomes increasingly important, and machine learning methods can be used as an effective tool for detecting fake news. This study employed exploratory data analysis to detect fake news related to COVID-19. Additionally, it looks at the potential difficulties and implications of employing machine learning to identify fake news.