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.

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

“…We were able to demonstrate a process for identifying potential events related to peaks in the number of messages containing polarized opinions about the vaccination campaign against COVID-19 in Brazil, following premises we observed in previous studies [ 20 , 21 , 22 , 58 ] and opening space for continuities by applying other techniques capable of further filtering useful information by collecting messages from social network users. The analytical process we applied demonstrates that it is possible to generate mechanisms for automating searches for news of benign or harmful events to verify if they were the focus of misinformation or fake news [ 5 , 59 ].…”

“…An opinion analysis framework can describe the process applied in this work based on other analytical processes proposed in the literature (see, for instance, [ 20 , 21 , 22 , 23 ]). Essentially, a framework or analytical process for sentiment or opinion analysis has to deal with [ 24 , 25 ]: Data collection to assemble the corpora.…”

Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications

“…Stance detection is an approach to automatically detect an author's stance (e.g., support, against, neutral) towards a specific topic. It has been applied to study the public perception of COVID-19 vaccines [16][17][18] , mask-wearing 15,19 , and other COVID-19 topics. This study adopted a variant of BERT 11 to embed documents for clustering and topic generation, which has been tested and used in COVID-19 research [20][21][22][23] .…”

Classifying the Evolving Mask Debate: A Transferable Machine Learning Framework

“…Prior work has applied supervised machine learning on tweets related to masks. This includes distinguishing between tweets that are relevant to the wearing of masks, with further filtering of those that do not express any personal opinions (Ahmed et al 2020). Cotfas et.…”