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

Ramamoorthy, Thilagavathi; Mappillairaju, Bagavandas

doi:10.1007/s42001-023-00222-x

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…This shows that the Twitter platform is highly used for sharing and explaining various diabetesrelated information. Similar exploration has been conducted for Indian populations regarding other non-communicable diseases such as Cancer, affirming the effectiveness of social media in this context (Ramamoorthy and Mappillairaju, 2023). Twitter's capacity to enable people to broadcast information and engage with their audiences has made it an excellent platform for sharing information.…”

Section: Discussionsupporting

confidence: 59%

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Ramamoorthy,

Kulothungan,

Mappillairaju

2024

Front. Artif. Intell.

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IntroductionThe utilization of social media presents a promising avenue for the prevention and management of diabetes. To effectively cater to the diabetes-related knowledge, support, and intervention needs of the community, it is imperative to attain a deeper understanding of the extent and content of discussions pertaining to this health issue. This study aims to assess and compare various topic modeling techniques to determine the most effective model for identifying the core themes in diabetes-related tweets, the sources responsible for disseminating this information, the reach of these themes, and the influential individuals within the Twitter community in India.MethodsTwitter messages from India, dated between 7 November 2022 and 28 February 2023, were collected using the Twitter API. The unsupervised machine learning topic models, namely, Latent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopic, and Top2Vec, were compared, and the best-performing model was used to identify common diabetes-related topics. Influential users were identified through social network analysis.ResultsThe NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around eight topics, namely, promotion, management, drug and personal story, consequences, risk factors and research, raising awareness and providing support, diet, and opinion and lifestyle changes. The influential nodes identified were mainly health professionals and healthcare organizations.DiscussionThe study identified important topics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public. Collaborations among influential healthcare organizations, health professionals, and the government can foster awareness and prevent noncommunicable diseases.

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Section: Discussionsupporting

confidence: 59%

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Ramamoorthy,

Kulothungan,

Mappillairaju

2024

Front. Artif. Intell.

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Exploring stroke discourse on Twitter through content and network analysis among Indian users

Ramamoorthy,

Kulothungan,

Mappillairaju

2024

Sci Rep

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The study aimed to understand stroke-related Twitter conversations in India, focusing on topics, message sources, reach, and influential users to provide insights to stakeholders regarding community needs for knowledge, support, and interventions. Geo-tagged Twitter posts focusing on stroke originating from India and, spanning from November 7, 2022, to February 28, 2023, were systematically obtained via the Twitter application programming interface, using keywords and hashtags sourced through Symplur Signals. Preprocessing involved the removal of hashtags, stop words, and URLs. The Latent Dirichlet Allocation (LDA) topic model was used to identify recurring stroke-related topics, while influential users were identified through social network analysis. About half of the tweets about stroke in India were about seeking support and post-stroke bereavement sharing and had the highest reachability. Four out of 10 tweets were from the individual twitter users. Tweets on the topic risk factors, awareness and prevention (14.6%) constituted the least proportion, whereas the topic management, research, and promotion had the least retweet ratio. Twitter demonstrates significant potential as a platform for both disseminating and acquiring stroke-related information within the Indian context. The identified topics and understanding of the content of discussion offer valuable resources to public health professionals and organizations to develop targeted educational and engagement strategies for the relevant audience.

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

Cited by 2 publications

References 31 publications

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Exploring stroke discourse on Twitter through content and network analysis among Indian users

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