Detecting the risk of COVID-19 spread in near real-time using social media

COVID-19 is a contagious disease caused by SARS-CoV-2, and WHO recommended preventive measures like social distancing, testing, lockdowns, face masks, etc. to limit its spread. Failure to implement and monitor these measures increases the risk of spread and mortality rates. In this paper, a near real-time system using Twitter for detecting the risk of COVID-19 spread is proposed. The system uses Apache Spark framework for text mining, machine learning, and near real-time processing of data from Twitter. Five base machine learning classifiers: support vector machine (SVM), logistic regression (LR), multilayer perceptron (MLP), decision tree (DT), and Naive Bayes (NB) are combined to form an ensemble majority voting classifier (EMVC). Results show that the EMVC achieved an accuracy of 94.76%. Then, the proposed system is tested in real-time for detecting tweets related to the risk of COVID-19 spread in London, Mumbai, and New York in June 2020.