Covid-19

Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning

Bibliographic Data

ID15491165
AuthorsEbtesam Alomari (0000-0003-4781-1145, King Abdulaziz University), Iyad Katib (0000-0003-3376-3218, King Abdulaziz University), Aiiad Albeshri (0000-0003-3796-0294, King Abdulaziz University), Rashid Mehmood (0000-0002-4997-5322, King Abdulaziz University, corresponding author)
Year2021
Volume18
Issue1
Pages282-282
Publication date2021-01-01
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph18010282
PMID33401512
OpenAlexW3117955110
LanguageEN
Citations received6
References cited42

Today's societies are connected to a level that has never been seen before. The COVID-19 pandemic has exposed the vulnerabilities of such an unprecedently connected world. As of 19 November 2020, over 56 million people have been infected with nearly 1.35 million deaths, and the numbers are growing. The state-of-the-art social media analytics for COVID-19-related studies to understand the various phenomena happening in our environment are limited and require many more studies. This paper proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) machine learning and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. The tool is described in detail, including its architecture, five software components, and algorithms. Using the tool, we collect a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February 2020 to 1 June 2020. We detect 15 government pandemic measures and public concerns and six macro-concerns (economic sustainability, social sustainability, etc.), and formulate their information-structural, temporal, and spatio-temporal relationships. For example, we are able to detect the timewise progression of events from the public discussions on COVID-19 cases in mid-March to the first curfew on 22 March, financial loan incentives on 22 March, the increased quarantine discussions during March-April, the discussions on the reduced mobility levels from 24 March onwards, the blood donation shortfall late March onwards, the government's 9 billion SAR (Saudi Riyal) salary incentives on 3 April, lifting the ban on five daily prayers in mosques on 26 May, and finally the return to normal government measures on 29 May 2020. These findings show the effectiveness of the Twitter media in detecting important events, government measures, public concerns, and other information in both time and space with no earlier knowledge about them

Business · Coronavirus disease 2019 (COVID-19 · Data science · Geography · Government (linguistics · Latent Dirichlet allocation · Pandemic · Political science · Public relations · Sentiment analysis · Social media · Sustainability · Topic model · World Wide Web · Computer Science · Data-Driven Disease Surveillance · Medicine · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining · Artificial Intelligence

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