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.
Highlights
We propose a software tool comprising a collection of 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
We review here the works about COVID-19 analysis using social media that have used topic modelling as the modelling method
We review the works related to COVID-19 analysis that use Twitter data with a focus on the tweets in the Arabic language
Summary
There is a growing consensus that the post-pandemic societies and world may take a different course for living, work, education, and other spheres of life. There is a need to understand what is happening around the world during and after the pandemic, what measures are being taken to fight the pandemic by the government and authorities, what the needs of the people are, and what people’s concerns and priorities are, etc. This information can help us to understand the implications of various pandemic measures (e.g., social isolation), manage the pandemic, address people’s concerns, understand the impact of various policies for the post-pandemic future, and more. Many more studies are needed to improve the breadth and depth of the research on the subject in several aspects (Section 2 elaborates the research gap, novelty, and contributions of our work)
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