IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i11p152-d963284.html
   My bibliography  Save this article

Arabic Twitter Conversation Dataset about the COVID-19 Vaccine

Author

Listed:
  • Huda Alhazmi

    (Department of Computer Science, Umm Al-Qura University, Makkah 24236, Saudi Arabia)

Abstract

The development and rollout of COVID-19 vaccination around the world offers hope for controlling the pandemic. People turned to social media such as Twitter seeking information or to voice their opinion. Therefore, mining such conversation can provide a rich source of data for different applications related to the COVID-19 vaccine. In this data article, we developed an Arabic Twitter dataset of 1.1 M Arabic posts regarding the COVID-19 vaccine. The dataset was streamed over one year, covering the period from January to December 2021. We considered a set of crawling keywords in the Arabic language related to the conversation about the vaccine. The dataset consists of seven databases that can be analyzed separately or merged for further analysis. The initial analysis depicts the embedded features within the posts, including hashtags, media, and the dynamic of replies and retweets. Further, the textual analysis reveals the most frequent words that can capture the trends of the discussions. The dataset was designed to facilitate research across different fields, such as social network analysis, information retrieval, health informatics, and social science.

Suggested Citation

  • Huda Alhazmi, 2022. "Arabic Twitter Conversation Dataset about the COVID-19 Vaccine," Data, MDPI, vol. 7(11), pages 1-17, November.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:11:p:152-:d:963284
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/11/152/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/7/11/152/
    Download Restriction: no
    ---><---

    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:jdataj:v:7:y:2022:i:11:p:152-:d:963284. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.