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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
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    References listed on IDEAS

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    1. Md Shoaib Ahmed & Tanjim Taharat Aurpa & Md Musfique Anwar, 2021. "Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-20, August.
    2. Rui He & Yungeng Li, 2021. "Media Exposure, Cancer Beliefs, and Cancer-Related Information-Seeking or Avoidance Behavior Patterns in China," IJERPH, MDPI, vol. 18(6), pages 1-22, March.
    3. Koskan, A. & Klasko, L. & Davis, S.N. & Gwede, C.K. & Wells, K.J. & Kumar, A. & Lopez, N. & Meade, C.D., 2014. "Use and taxonomy of social media in cancer-related research: A systematic review," American Journal of Public Health, American Public Health Association, vol. 104(7), pages 20-37.
    4. Prateeksha Dawn Davidson & Thanujah Muniandy & Dhivya Karmegam, 2023. "Perception of COVID-19 vaccination among Indian Twitter users: computational approach," Journal of Computational Social Science, Springer, vol. 6(2), pages 541-560, October.
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