Social media analytics and reachability evaluation - #Diabetes

Karmegam, Dhivya; Mappillairaju, Bagavandas

doi:10.1016/j.dsx.2021.102359

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…Dissemination of accurate and updated information about stroke through social media is important for prevention, awareness, and early diagnosis. Our study found that messages shared by individuals had a higher retweet ratio, consistent with prior studies on non-communicable diseases 13 , 28 – 30 . Nevertheless, there's a potential risk of disseminating unauthorized or inaccurate information unintentionally, which could negatively affect those seeking reliable social media information.…”

Section: Discussionsupporting

confidence: 92%

“…Globally, discussions related to stroke in social media has been used to understand the conversations on heat waves, quality and trustworthiness of information, treatment, recovery, and survivorship 11 , 12 . The usage of twitter and other social media in India for health information and communication has been previously studied for Polycystic ovary syndrome, diabetes, cancer and COVID-19 in context of vaccine as well as its psychological impacts 13 – 17 . A study conducted in the Indian context examined the marketing approaches employed by e-cigarette marketers on social media, as well as the level of user engagement.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

Ramamoorthy,

Kulothungan,

Mappillairaju

2024

Sci Rep

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“…As health information-seeking behavior is different across the globe, it is essential to understand social media usage for health-related information, its content, types of users, sentiments, and social networks for developing strategic interventions for the community (Raamkumar et al, 2016 ). Despite the growing importance of social media in health communication, research on how it is utilized for diabetes-related discourse in India is notably scarce (Diviya Prabha and Rathipriya, 2022 ; Karmegam, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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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Sentiment Classification of Diabetes-Related Tweets Using Transformer-Based Deep Learning Approach

Anoop¹

2023

Communications in Computer and Information Science

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Social media analytics and reachability evaluation - #Diabetes

Cited by 5 publications

References 40 publications

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

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

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

Sentiment Classification of Diabetes-Related Tweets Using Transformer-Based Deep Learning Approach

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