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

Author

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  • Ebtesam Alomari

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Iyad Katib

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Aiiad Albeshri

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Rashid Mehmood

    (High Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

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.

Suggested Citation

  • Ebtesam Alomari & Iyad Katib & Aiiad Albeshri & Rashid Mehmood, 2021. "COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning," IJERPH, MDPI, vol. 18(1), pages 1-34, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:1:p:282-:d:473735
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    References listed on IDEAS

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    1. Diya Li & Harshita Chaudhary & Zhe Zhang, 2020. "Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
    2. Tan Yigitcanlar & Nayomi Kankanamge & Karen Vella, 2021. "How Are Smart City Concepts and Technologies Perceived and Utilized? A Systematic Geo-Twitter Analysis of Smart Cities in Australia," Journal of Urban Technology, Taylor & Francis Journals, vol. 28(1-2), pages 135-154, April.
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    Cited by:

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    2. Shweta Agrawal & Sanjiv Kumar Jain & Shruti Sharma & Ajay Khatri, 2022. "COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
    3. Tim Hulsen, 2022. "Data Science in Healthcare: COVID-19 and Beyond," IJERPH, MDPI, vol. 19(6), pages 1-4, March.
    4. Nala Alahmari & Rashid Mehmood & Ahmed Alzahrani & Tan Yigitcanlar & Juan M. Corchado, 2023. "Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters," Sustainability, MDPI, vol. 15(22), pages 1-44, November.
    5. Raniah Alsahafi & Ahmed Alzahrani & Rashid Mehmood, 2023. "Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations," Sustainability, MDPI, vol. 15(5), pages 1-64, February.

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