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

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 wi...

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Autores principales:	Liviu-Adrian Cotfas, Camelia Delcea, Rareș Gherai, Ioan Roxin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	opinion mining social media COVID-19 face mask stance classification Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/6bc9bbd28cc64010a1e7e0e6afc81caa
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spelling	oai:doaj.org-article:6bc9bbd28cc64010a1e7e0e6afc81caa2021-11-25T19:05:52ZUnmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis10.3390/sym131119952073-8994https://doaj.org/article/6bc9bbd28cc64010a1e7e0e6afc81caa2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/1995https://doaj.org/toc/2073-8994Wearing 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 <i>neutral</i> category, while the ones containing people’s opinions (for or against) have been marked using a symmetrical approach into <i>in favor</i> and <i>against</i> categories. Based on the analysis, it has been determined that most of the mask tweets are in the area of <i>in favor</i> or <i>neutral</i>, while a smaller percentage of tweets and retweets are in the <i>against</i> 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.Liviu-Adrian CotfasCamelia DelceaRareș GheraiIoan RoxinMDPI AGarticleopinion miningsocial mediaCOVID-19face maskstance classificationMathematicsQA1-939ENSymmetry, Vol 13, Iss 1995, p 1995 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	opinion mining social media COVID-19 face mask stance classification Mathematics QA1-939
spellingShingle	opinion mining social media COVID-19 face mask stance classification Mathematics QA1-939 Liviu-Adrian Cotfas Camelia Delcea Rareș Gherai Ioan Roxin Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis
description	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 <i>neutral</i> category, while the ones containing people’s opinions (for or against) have been marked using a symmetrical approach into <i>in favor</i> and <i>against</i> categories. Based on the analysis, it has been determined that most of the mask tweets are in the area of <i>in favor</i> or <i>neutral</i>, while a smaller percentage of tweets and retweets are in the <i>against</i> 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.
format	article
author	Liviu-Adrian Cotfas Camelia Delcea Rareș Gherai Ioan Roxin
author_facet	Liviu-Adrian Cotfas Camelia Delcea Rareș Gherai Ioan Roxin
author_sort	Liviu-Adrian Cotfas
title	Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis
title_short	Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis
title_full	Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis
title_fullStr	Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis
title_full_unstemmed	Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis
title_sort	unmasking people’s opinions behind mask-wearing during covid-19 pandemic—a twitter stance analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6bc9bbd28cc64010a1e7e0e6afc81caa
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Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis

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