Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis

Abstract

Wearing a mask by the general public has been a controversial issue from the beginning of the COVID-19 pandemic as the public authorities have had mixed messages, either advising people not to wear masks if uninfected, to wear as a protective measure, to wear them only when inside a building/room with insufficient air flow or to wear them in all the public places. To date, the governments have had different policies regarding mask-wearing by the general public depending on the COVID-19 pandemic evolution. In this context, the paper analyzes the general public’s opinion regarding mask-wearing for the one-year period starting from 9 January 2020, when the first tweet regarding mask-wearing in the COVID-19 context has been posted. Classical machine learning and deep learning algorithms have been considered in analyzing the 8,795,633 tweets extracted. A random sample of 29,613 tweets has been extracted and annotated. The tweets containing news and information related to mask-wearing have been included in the neutral category, while the ones containing people’s opinions (for or against) have been marked using a symmetrical approach into in favor and against categories. Based on the analysis, it has been determined that most of the mask tweets are in the area of in favor or neutral, while a smaller percentage of tweets and retweets are in the against category. The evolution of the opinions expressed through tweets can be further monitored for extracting the public perspective on mask-wearing in times of COVID-19.

Keywords

• opinion mining
• social media
• COVID-19
• face mask
• stance classification