Network analysis as a method of assessing terms in a dataset comprising eating disorders

Abstract

Eating disorders are central reason of physical and psycho-social morbidity. Several factors have been identified as being associated with the prevalence and progression of eating disorders in humans. Scientific investigation was carried out to assess the usage of terms in manuscript titles of nearly 900 published articles followed by network analysis and network centralities using R programming. The tm package, term document matrix function was utilized to create a term document matrix (TDM) from the corpus. A binary word matrix comprising 17 terms was created based on higher probability of occurring a term in a column. An agglomerative hierarchical clustering technique using ward clustering algorithm was presented. A data frame from the TDM was created to store data and used to plot word cloud based on word frequencies. An undirected network graph was plotted based on terms that appeared in the term matrix. Centralization measures such as Degree centrality, Closeness, Eigenvector and betweenness Centrality were reported.

40 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modeling

“…Previous bibliometric studies about ED have focused on: identifying the distribution by language, region and country, as well as topics and their trends [1], productivity trends and collaboration patterns [2], most cited works in Anorexia Nervosa research [8], cross-cultural aspects of ED [3], comparison of citations between types of journals [9], female authorship [10], secular trends in the scientific terminology [11,12], the gap between scientific research and clinical practice [13], the use of keywords [14], and network analyses of common terms used in the field [15]. In particular, the current study complements the work by He et al [1].…”