IDEAS home Printed from https://ideas.repec.org/a/ids/ijsoma/v52y2025i1p1-24.html
   My bibliography  Save this article

Analysis of community engagement on social media during disasters: a machine learning approach

Author

Listed:
  • Arun Thomas
  • Sumit Kumar
  • Vinay V. Panicker

Abstract

Recently social networking sites have emerged as an imperative source for providing information to identify and track disasters. The utilisation of social networking data streams to extract meaningful data is possible. A proper data gathering and an evaluation protocol is required for a predictive model. With this purpose, the features of tweets during the Kerala floods of 2018 were examined in this research. About 154,524 tweets were collected using the API during the period between July 2018 and February 2019. Data visualisation has been done with the help of Tableau, and the most commonly used words were identified using a dataset of 10,704 tweets. A topic modelling approach was adopted for exploring and recognising the unlabelled topics in the dataset. A typical machine learning technique is designed for the sentiment analysis of tweets. The long short-term memory (LSTM) model has been developed to find the accuracy of the data.

Suggested Citation

  • Arun Thomas & Sumit Kumar & Vinay V. Panicker, 2025. "Analysis of community engagement on social media during disasters: a machine learning approach," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 52(1), pages 1-24.
    • Handle: RePEc:ids:ijsoma:v:52:y:2025:i:1:p:1-24
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=148832
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:ids:ijsoma:v:52:y:2025:i:1:p:1-24. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=150 .

    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.