IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i10p254-d647342.html
   My bibliography  Save this article

A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

Author

Listed:
  • Ashwag Alasmari

    (Computer Science Department, King Khalid University, Abha 62529, Saudi Arabia
    These authors contributed equally to this work.)

  • Aseel Addawood

    (Information System Department, Imam Mohammad Bin Saud University, Riyadh 11564, Saudi Arabia
    These authors contributed equally to this work.)

  • Mariam Nouh

    (Center for Complex Engineering Systems (CCES) at KACST and MIT, King Abdulaziz City for Science and Technology, Riyadh 12354, Saudi Arabia)

  • Wajanat Rayes

    (Department of Information Science, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

  • Areej Al-Wabil

    (College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia)

Abstract

COVID-19 has had broad disruptive effects on economies, healthcare systems, governments, societies, and individuals. Uncertainty concerning the scale of this crisis has given rise to countless rumors, hoaxes, and misinformation. Much of this type of conversation and misinformation about the pandemic now occurs online and in particular on social media platforms like Twitter. This study analysis incorporated a data-driven approach to map the contours of misinformation and contextualize the COVID-19 pandemic with regards to socio-religious-political information. This work consists of a combined system bridging quantitative and qualitative methodologies to assess how information-exchanging behaviors can be used to minimize the effects of emergent misinformation. The study revealed that the social media platforms detected the most significant source of rumors in transmitting information rapidly in the community. It showed that WhatsApp users made up about 46% of the source of rumors in online platforms, while, through Twitter, it demonstrated a declining trend of rumors by 41%. Moreover, the results indicate the second-most common type of misinformation was provided by pharmaceutical companies; however, a prevalent type of misinformation spreading in the world during this pandemic has to do with the biological war. In this combined retrospective analysis of the study, social media with varying approaches in public discourse contributes to efficient public health responses.

Suggested Citation

  • Ashwag Alasmari & Aseel Addawood & Mariam Nouh & Wajanat Rayes & Areej Al-Wabil, 2021. "A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia," Future Internet, MDPI, vol. 13(10), pages 1-15, September.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:10:p:254-:d:647342
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/10/254/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/13/10/254/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    2. Jari Jussila & Anu Helena Suominen & Atte Partanen & Tapani Honkanen, 2021. "Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter," Future Internet, MDPI, vol. 13(6), pages 1-16, June.
    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. Viral Tolia & Rajkumar Renin Singh & Sameer Deshpande & Anupama Dave & Raju M. Rathod, 2022. "Understanding Factors to COVID-19 Vaccine Adoption in Gujarat, India," IJERPH, MDPI, vol. 19(5), pages 1-21, 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. Nisar, Sobia & Shafiq, Muhammad, 2019. "Framework for efficient utilisation of social media in Pakistan's healthcare sector," Technology in Society, Elsevier, vol. 56(C), pages 31-43.
    2. Luis-Millán González & José Devís-Devís & Maite Pellicer-Chenoll & Miquel Pans & Alberto Pardo-Ibañez & Xavier García-Massó & Fernanda Peset & Fernanda Garzón-Farinós & Víctor Pérez-Samaniego, 2021. "The Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis," IJERPH, MDPI, vol. 18(9), pages 1-20, April.
    3. Han, Chunjia & Yang, Mu & Piterou, Athena, 2021. "Do news media and citizens have the same agenda on COVID-19? an empirical comparison of twitter posts," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    4. Boonyanit Mathayomchan & Viriya Taecharungroj & Walanchalee Wattanacharoensil, 2023. "Evolution of COVID-19 tweets about Southeast Asian Countries: topic modelling and sentiment analyses," Place Branding and Public Diplomacy, Palgrave Macmillan, vol. 19(3), pages 317-334, September.
    5. Elanor Colleoni & Nuccio Ludovico & Illia Laura & Ravindran Kiron, 2021. "Does Sharing Economy Have a Moral Capital? Comparing Semantic Networks in Social Media and News Media," Journal of Management and Sustainability, Canadian Center of Science and Education, vol. 11(2), pages 1-1, December.
    6. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    7. Cindy Cheng & Joan Barceló & Allison Spencer Hartnett & Robert Kubinec & Luca Messerschmidt, 2020. "COVID-19 Government Response Event Dataset (CoronaNet v.1.0)," Nature Human Behaviour, Nature, vol. 4(7), pages 756-768, July.
    8. Bruce Forrester, 2020. "Authentic chatter," Computational and Mathematical Organization Theory, Springer, vol. 26(4), pages 382-411, December.
    9. Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020. "Happiness-lost: Did Governments make the right decisions to combat Covid-19?," GLO Discussion Paper Series 556, Global Labor Organization (GLO).
    10. Hongzhou Shen & Yue Ju & Zhijing Zhu, 2023. "Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    11. Gaspar, Rui & Yan, Zheng & Domingos, Samuel, 2019. "Extreme natural and man-made events and human adaptive responses mediated by information and communication technologies' use: A systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 125-135.
    12. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    13. Jiayin Pei & Guang Yu & Xianyun Tian & Maureen Renee Donnelley, 2017. "A new method for early detection of mass concern about public health issues," Journal of Risk Research, Taylor & Francis Journals, vol. 20(4), pages 516-532, April.
    14. Zeynep Ertem & Dorrie Raymond & Lauren Ancel Meyers, 2018. "Optimal multi-source forecasting of seasonal influenza," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    15. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    16. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    17. 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.
    18. Rodrigo Carrillo-Larco, 2012. "Social networks and public health: use of Twitter by ministries of health," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(4), pages 755-756, August.
    19. Esther Oliver & María Carmen Llasat & Montserrat Llasat-Botija & Javier Díez-Palomar, 2021. "Twitter’s Messages about Hydrometeorological Events. A Study on the Social Impact of Climate Change," Sustainability, MDPI, vol. 13(6), pages 1-24, March.
    20. Xiaodong Yang & Lai Wei & Zhiyue Liu, 2022. "Promoting COVID-19 Vaccination Using the Health Belief Model: Does Information Acquisition from Divergent Sources Make a Difference?," IJERPH, MDPI, vol. 19(7), pages 1-15, March.

    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:jftint:v:13:y:2021:i:10:p:254-:d:647342. 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.