Scientometric analysis of metadata of articles on the topic "Yoga" - K.V. Tartynova (Векторы благополучия: экономика и социум)
Relevance. Scientometric analysis is widely developing today and is an effective tool for reconnaissance analysis in the area of interest. The conducted analysis combines network analysis methods, namely, building a co‐authorship network using the Gephi program, and text analytics in the Orange Data Mining program. The results of the study help to see: what aspects in the study of yoga practice have already been considered by the scientific community. Also, the results are useful for detecting "blank spots" in scientific knowledge for further research of yoga as a practice of social well‐being. Aim. To conduct a scientometric analysis of publications of Russian authors on the topic "Yoga" from the international bibliographic database OpenAlex. Methods. Metadata of scientific publications of authors of Russian universities on yoga practices were used for network analysis. The international open bibliographic database OpenAlex served as a data source. The period of publication of articles was from January 25, 1999 to March 11, 2022. As a result, metadata on 125 articles were downloaded (at the time of downloading October 21, 2024). Gephi software was used to visualize and study the co‐authorship network. Text analytics was used using Orange Data Mining software to identify the structure and content of the scientific field on the topic "Yoga". Results. At the first stage, an undirected graph of the co‐authorship network was compiled for 125 articles on the topic "Yoga". The graph consists of 335 nodes (authors) and 1387 edges (co‐authorship links). The second stage was text analytics. Articles without annotations and duplicates (repeated articles) were removed for analysis. The final dataset included metadata of 117 articles with the specified title and abstract. As a result, a co‐authorship network was built and network clusters were designated; a classification of articles was compiled based on the Ward minimum variance method, and keywords were identified and analyzed for each class of articles. Conclusions. Scientometric analysis showed that Russian authors do not strive for an interdisciplinary study of yoga practice. This is evidenced by the comparability of the results of the co‐ authorship network analysis and text analytics of article abstracts. Conducting such scientometric analysis allows us to clearly demonstrate how modern data analytics tools can be used to solve problems in sociology.