IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v6y2023i2d10.1007_s42001-023-00222-x.html
   My bibliography  Save this article

Tweet topics on cancer among Indian Twitter users—computational approach using latent Dirichlet allocation topic modelling

Author

Listed:
  • Thilagavathi Ramamoorthy

    (SRM Institute of Science and Technology)

  • Bagavandas Mappillairaju

    (SRM Institute of Science and Technology)

Abstract

Understanding the extent and content of conversations on cancers inform the stakeholders regarding the needs of the community in terms of knowledge, support and interventions. This study identified the topics of tweet content shared regarding cancer, source of messages and the degree of reachability of identified topics among Twitter users in India. Twitter messages geocoded within India, related to cancer and posted between September 15, 2021 and October 15, 2021 were retrieved using the Twitter application programming interface based on keywords identified from Symplur Signals. The tweets were pre-processed to remove the stop words, hashtags and Uniform Resource Locators. Tweets were visualized using word clouds and correlations between word tokens. Latent Dirichlet allocation (LDA) topic model, an unsupervised machine learning technique was used to identify the commonly discussed cancer topics. A total of 6374 tweets from 3135 unique twitter users were analysed in the study. Majority of the tweets (60.8%) were from the individual twitter users. LDA model identified four topics: (1) prevention, early detection and promotion (36.1%), (2) seeking support and sharing personal experience (15.8%), (3) Human Papillomavirus vaccine and cancer research (13.4%), (4) risk factors, treatment and raising awareness (34.7%). Among the four identified topics, prevention, early detection and promotion had the highest reachability. Twitter is being used as a potential alternative communication platform for disseminating cancer-related information in India. The topics identified in the study provides useful insights for public health professionals and organizations for aligning cancer-related engagement and education for the target audience.

Suggested Citation

  • Thilagavathi Ramamoorthy & Bagavandas Mappillairaju, 2023. "Tweet topics on cancer among Indian Twitter users—computational approach using latent Dirichlet allocation topic modelling," Journal of Computational Social Science, Springer, vol. 6(2), pages 1033-1054, October.
    • Handle: RePEc:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-023-00222-x
    DOI: 10.1007/s42001-023-00222-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-023-00222-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-023-00222-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-023-00222-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.