COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning

Abstract

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.

A Longitudinal Analysis of the Creation of Environmental Identity and Attitudes towards Energy Sustainability Using the Framework of Identity Theory and Big Data Analysis

“…Integrated environmental identity resists the changes taking place in the social context. Even a crisis situation, such as the COVID-19 pandemic and national lockdown and the resultant fear, anger, and anxiety over the health and economic situation [34,48,49] has not resulted in a decrease in public interest in environmental issues. This finding is in line with other studies [23][24][25] that focused on the integrity of sustainable identity with 'green behavior'.…”

“…A large and growing body of research has investigated the use of big data to increase customer loyalty [29,30], improve the quality of healthcare [31][32][33], and monitor public concerns [34]. Much of the current literature on social media pays particular attention to the sustainability of smart cities [35,36] and social media-mediated disaster communication [37].…”

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

“…A massive amount of real-time data is posted by millions of users on various topics including transportation and real-time road traffic [ 4 , 6 , 7 , 8 ]. In the recent decade, the use of Twitter and other social media by researchers and practitioners to study different issues in many application domains and sectors has steadily increased [ 9 , 10 , 11 , 12 , 13 , 14 ]. Transportation is no exception where social media has been used to study various aspects such as for analysing travel behaviours [ 15 ], recognizing mobility patterns [ 16 ], congestion detection [ 17 ], and event detection [ 4 , 6 , 9 , 18 , 19 ].…”

“…Many more studies are needed to improve the breadth and depth of the research on the subject in several aspects to establish maturity in this area. The research gaps relate to the focus of the studies, the size and diversity of the data, the applicability and performance of the machine learning methods, the diversity in terms of the social media languages, the scalability of the computing platforms, and others [ 13 , 21 ]. The maturity of research in this area will allow the development, commercialization, and wide adoption of the tools for transportation planning and operations (for the literature review and research gap, see Section 2 ).…”

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

“…This is because of the prevalence of personal devices that allow real-time interaction with the stakeholders and collection of location, activity, preferences, opinion, and other data. Traditional methods of research and data collection using surveys and other means cannot capture timely, course-grained, dynamic, and large-scale data, in addition to having other disadvantages [177,180]. Therefore, it seems inevitable that digital and emerging technologies will become part of the existing research and data collection methodologies and will be included in research design and methodology curricula.…”