Fake news and COVID-19 vaccination: a comparative study

Abstract

COVID-19 pandemic has changed almost every aspect of people's lives around the world. Along with non-pharmaceutical interventions such as physical distancing, vaccination is one of the proposed solutions to control the spread of this pandemic. However, so much fake information is spread on social media websites about the vaccination. In this paper, we study the problem of fake news detection on Twitter network. After collecting a dataset and pre-processing, a set of features are extracted from the tweets. This includes the tweet's length and its keywords, number of followers, sentiment, and readability scores. In the next phase, six well-known classifiers are executed on this data, and the best result with the highest accuracy is chosen for the community detection process to study and track the evolution of fake news campaigns. For the analysis, we considered multiple criteria such as the number of communities, their sizes, leaders, and topics. The results of this research can help decision-makers to understand the underlying and formation of fake news campaigns.