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

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

Data_Sheet_1_Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India.docx

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.</p

Trait Valuation in Genetically Modified Crops: An ex-ante Analysis of GM Cassava against Cassava Mosaic Disease

Cassava is a widely distributed crop known for food security and industrial applications. Nonetheless, it is highly prone to attacks of pests and diseases. Cassava mosaic disease (CMD) is an important cause of loss across the globe. In this context, research is focused on developing cassava mosaic disease resistant varieties through transgenic and conventional approaches. In this paper, the trait of CMD resistance is valued ex-ante using partial budget approach and economic surplus model. The trait value of CMD resistance at farm level varies from ` 38658 per hectare in drip-irrigated production system to ` 15562 per hectare in rainfed production system. At the macro level, the value of the improved trait is worth 1781.70 million rupees. The results clearly indicate attractive rate of returns on investment in research for CMD resistance in cassava

Clinical presentation and 2-year mortality outcomes in acute heart failure in a tertiary care hospital in South India: A retrospective cohort study

Background: Heart failure (HF) is one of the leading causes of mortality and morbidity worldwide. We sought to describe the clinical epidemiology of HF from a representative sample in a tertiary care setting and to evaluate the factors which could increase the mortality risk in the study patients. Methods: This retrospective cohort study was carried out among patients who had been admitted with a diagnosis of acute HF from 2013 to 2017. Demographic data, history, laboratory investigations, data on medication clinical variables, and in-hospital outcomes were obtained from the patient's hospital records. The patients were assessed through the telephonic interview for mortality outcomes. Data were analyzed using SPSS software version 16.0 (SPSS Inc., Chicago, IL) and all values of P < 0.05 was considered as statistically significant. Results: A total of 355 acute heart failure (AHF) patients were included in the study with a mean age of 57.78 ± 12.78 years. The most common etiologies among the study patients were ischemic heart disease (58%) and Dilated Cardiomyopathy (24.79%). The in-hospital and 2-year mortality was found to be 104 (29.3%) and 179 (50.4%), respectively. The 2-year mortality was significantly higher in patients with ischemic HF than that of nonischemic HF (119 [57.8%] vs. 58 [39.5%], P = 0.003). Multivariate Cox proportional hazard analysis demonstrated that elderly age, the presence of diastolic dysfunction and higher levels of total leukocyte count (TLC) were independent predictors of mortality. Conclusion: The mortality rate in AHF is higher among ischemic HF than nonischemic HF. The major factors contributing to the 2-year mortality rate among AHF were elderly age, diastolic dysfunction, and high-TLC