IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i1p282-d473735.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/1/282/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/1/282/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eman Alqahtani & Nourah Janbi & Sanaa Sharaf & Rashid Mehmood, 2022. "Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling," Sustainability, MDPI, vol. 14(20), pages 1-65, October.
    2. 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.
    3. 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.
    4. Tim Hulsen, 2022. "Data Science in Healthcare: COVID-19 and Beyond," IJERPH, MDPI, vol. 19(6), pages 1-4, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michał Błaszczyk & Milan Popović & Karolina Zajdel & Radosław Zajdel, 2022. "The Impact of the COVID-19 Pandemic on the Organisation of Remote Work in IT Companies," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
    2. Seng Boon Lim & Jalaluddin Abdul Malek & Md Farabi Yussoff Md Yussoff & Tan Yigitcanlar, 2021. "Understanding and Acceptance of Smart City Policies: Practitioners’ Perspectives on the Malaysian Smart City Framework," Sustainability, MDPI, vol. 13(17), pages 1-31, August.
    3. Turgut Acikara & Bo Xia & Tan Yigitcanlar & Carol Hon, 2023. "Contribution of Social Media Analytics to Disaster Response Effectiveness: A Systematic Review of the Literature," Sustainability, MDPI, vol. 15(11), pages 1-50, May.
    4. Tatiana Tucunduva Philippi Cortese & Jairo Filho Sousa de Almeida & Giseli Quirino Batista & José Eduardo Storopoli & Aaron Liu & Tan Yigitcanlar, 2022. "Understanding Sustainable Energy in the Context of Smart Cities: A PRISMA Review," Energies, MDPI, vol. 15(7), pages 1-38, March.
    5. Thiago Poleto & Thyago Celso Cavalcante Nepomuceno & Victor Diogho Heuer de Carvalho & Ligiane Cristina Braga de Oliveira Friaes & Rodrigo Cleiton Paiva de Oliveira & Ciro José Jardim Figueiredo, 2023. "Information Security Applications in Smart Cities: A Bibliometric Analysis of Emerging Research," Future Internet, MDPI, vol. 15(12), pages 1-36, December.
    6. Li, Wenda & Yigitcanlar, Tan & Liu, Aaron & Erol, Isil, 2022. "Mapping two decades of smart home research: A systematic scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    7. Jung-Jung Chang & Chia-Li Lin, 2022. "Establishing Urban Revitalization and Regional Development Strategies with Consideration of Urban Stakeholders Based on the ISA-NRM Approach," Sustainability, MDPI, vol. 14(12), pages 1-28, June.
    8. Nancy Micozzi & Tan Yigitcanlar, 2022. "Understanding Smart City Policy: Insights from the Strategy Documents of 52 Local Governments," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
    9. Clement, Dr. Jessica & Crutzen, Prof. Nathalie, 2021. "How Local Policy Priorities Set the Smart City Agenda," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    10. D’Amico, Gaspare & Arbolino, Roberta & Shi, Lei & Yigitcanlar, Tan & Ioppolo, Giuseppe, 2022. "Digitalisation driven urban metabolism circularity: A review and analysis of circular city initiatives," Land Use Policy, Elsevier, vol. 112(C).
    11. Song Yang & Yinfeng Su & Qin Yu, 2024. "Smart-City Policy in China: Opportunities for Innovation and Challenges to Sustainable Development," Sustainability, MDPI, vol. 16(16), pages 1-32, August.
    12. Rocco Mazza & Emma Zavarrone & Mirko Olivieri & Daniela Corsaro, 2022. "A text mining approach for CSR communication: an explorative analysis of energy firms on Twitter in the post-pandemic era," Italian Journal of Marketing, Springer, vol. 2022(3), pages 317-340, September.
    13. Butler, Luke & Yigitcanlar, Tan & Paz, Alexander & Areed, Wala, 2022. "How can smart mobility bridge the first/last mile gap? Empirical evidence on public attitudes from Australia," Journal of Transport Geography, Elsevier, vol. 104(C).
    14. Sesil Koutra & Christos S. Ioakimidis, 2022. "Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges," Land, MDPI, vol. 12(1), pages 1-19, December.
    15. Ana Cristina Fachinelli & Tan Yigitcanlar & Jamile Sabatini-Marques & Tatiana Tucunduva Philippi Cortese & Debora Sotto & Bianca Libardi, 2023. "Urban Smartness and City Performance: Identifying Brazilian Smart Cities through a Novel Approach," Sustainability, MDPI, vol. 15(13), pages 1-24, June.
    16. Dorota Kamrowska-Załuska, 2021. "Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities," Land, MDPI, vol. 10(11), pages 1-19, November.
    17. Bernd W. Wirtz & Marcel Becker & Florian W. Schmidt, 2022. "Smart city services: an empirical analysis of citizen preferences," Public Organization Review, Springer, vol. 22(4), pages 1063-1080, December.
    18. Yanmin Xu & Wengang Li & Jianjiang Tai & Chunjiong Zhang, 2022. "A Bibliometric-Based Analytical Framework for the Study of Smart City Lifeforms in China," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    19. Seng Boon Lim & Jalaluddin Abdul Malek & Tan Yigitcanlar, 2021. "Post-Materialist Values of Smart City Societies: International Comparison of Public Values for Good Enough Governance," Future Internet, MDPI, vol. 13(8), pages 1-13, August.
    20. Stylianos Ioannidis & Christos Karelakis & Zacharias Papanikolaou & George Theodossiou, 2022. "Exploring Digitalisation Adaptation of Agro-food Firms: Evidence from Greece," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 15(1), pages 94-104, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:1:p:282-:d:473735. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.