Mining Coronavirus (COVID-19) Posts in Social Media

TL;DR

This paper presents a study on automatically detecting COVID-19 positive reports from social media posts using machine learning, aiming to understand the outbreak's impact through linguistic analysis.

Contribution

It introduces a machine learning approach to identify COVID-19 related posts on social media, providing early insights into public reports during the outbreak.

Findings

Early social media posts can be used to detect COVID-19 reports

Machine learning models show promise in classifying COVID-19 related content

Preliminary results indicate potential for real-time outbreak monitoring

Abstract

World Health Organization (WHO) characterized the novel coronavirus (COVID-19) as a global pandemic on March 11th, 2020. Before this and in late January, more specifically on January 27th, while the majority of the infection cases were still reported in China and a few cruise ships, we began crawling social media user postings using the Twitter search API. Our goal was to leverage machine learning and linguistic tools to better understand the impact of the outbreak in China. Unlike our initial expectation to monitor a local outbreak, COVID-19 rapidly spread across the globe. In this short article we report the preliminary results of our study on automatically detecting the positive reports of COVID-19 from social media user postings using state-of-the-art machine learning models.