首页|Data on Artificial Intelligence Reported by Vaitheeswaran Kulothungan and Collea gues (Topic modeling and social network analysis approach to explore diabetes di scourse on Twitter in India)
Data on Artificial Intelligence Reported by Vaitheeswaran Kulothungan and Collea gues (Topic modeling and social network analysis approach to explore diabetes di scourse on Twitter in India)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning - Art ificial Intelligence is the subject of a report. According to news reporting ori ginating from Bengaluru, India, by NewsRx correspondents, research stated, "The utilization of social media presents a promising avenue for the prevention and m anagement of diabetes. To effectively cater to the diabetes-related knowledge, s upport, and intervention needs of the community, it is imperative to attain a de eper understanding of the extent and content of discussions pertaining to this h ealth issue." Our news editors obtained a quote from the research, "This study aims to assess and compare various topic modeling techniques to determine the most effective mo del for identifying the core themes in diabetes-related tweets, the sources resp onsible for disseminating this information, the reach of these themes, and the i nfluential individuals within the Twitter community in India. Twitter messages f rom India, dated between 7 November 2022 and 28 February 2023, were collected us ing the Twitter API. The unsupervised machine learning topic models, namely, Lat ent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopi c, and Top2Vec, were compared, and the best-performing model was used to identif y common diabetes-related topics. Influential users were identified through soci al network analysis. The NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around ei ght 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 heal th professionals and healthcare organizations. The study identified important to pics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public."
BengaluruIndiaAsiaArtificial Intel ligenceHealth and MedicineIndiaMachine LearningRisk and Prevention
NETL
NSTL
