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

Thilagavathi Ramamoorthy () and Bagavandas Mappillairaju
Additional contact information
Thilagavathi Ramamoorthy: SRM Institute of Science and Technology
Bagavandas Mappillairaju: SRM Institute of Science and Technology

Journal of Computational Social Science, 2023, vol. 6, issue 2, No 22, 1033-1054

Abstract: 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.

Keywords: Twitter; Cancer; Latent Dirichlet allocation; Machine learning; Natural language processing; Social media; Topic modelling (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-023-00222-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-023-00222-x

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-023-00222-x

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().