Abstract
Scientific literature data is now more available than ever before. Specifically, this paper focuses on measures of centrality and dispersion of keywords and author network analysis of research papers published in the 2012–2016 period, in five top journals (in the first and second quartiles of SCImago Journal Rank, in 2016, in the field of Tourism, Leisure and Hospitality Management) within the scope of sport. Based on Social Network Theory and using content analysis for data mining purposes, this paper aims to identify trends in sports literature and authors behaviors during the analyzed period. Also, using ANOVA links between author network characteristics and ranking information are analyzed, such as: self-citations, total citations, and percentage of international collaboration. In the period studied, some topics evolution and social activities patterns are discovered. The most predominant topics in sport literature for analyzed years were sport policy, social capital, and sport participation while predominant topics in other disciplines were not found. Furthermore, according to social patterns, results suggest the predominance of two-author teams and that group of authors tend to vary from one research to another. Furthermore, there are links between author network characteristics and citations received, particularly, between Average Degree, Weighted Average Degree, and Graph Density and total citations and, also, between Graph Density and the percentage of international collaboration.
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Perez-Aranda, J., Pelaez-Verdet, A. An application of social network mining to scientific data: identifying networks structures and detecting partnerships in metrics and citation patterns. Soc. Netw. Anal. Min. 11, 4 (2021). https://doi.org/10.1007/s13278-020-00710-2
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DOI: https://doi.org/10.1007/s13278-020-00710-2