Using text mining and forest plots to identify similarities and differences between two spine-related journals based on medical subject headings (MeSH terms) and author-specified keywords in 100 top-cited articles

Po-Hsin Chou (), Jui-Chung John Lin () and Tsair-Wei Chien ()
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Po-Hsin Chou: National Yang Ming Chiao Tung University
Jui-Chung John Lin: Taipei Medical University
Tsair-Wei Chien: Chi Mei Medical Center

Scientometrics, 2023, vol. 128, issue 1, No 1, 17 pages

Abstract: Abstract Literature research requires an understanding of the similarities and differences between different types of journals. It has not yet been possible to use text-mining to demonstrate the differences between the topics of articles by presenting features of article keywords using forest plots. It is important for authors to make a quick assessment of the similarities and differences between research types when submitting an article for publication in a journal. Our study uses text mining and forest plotting techniques to extract article features and compare the similarities and differences between the two journals' research types. There were a total of 100 top-cited articles selected from Spine (Phila Pa 1976) and The Spine Journal: official journals of the North American Spine Society with impact factors of 3.19 and 3.22 respectively, as reported by Journal Citation Reports (JCR) for 2018. XLSTAT software was used to extract features from author-made keywords and medical subject headings (e.g., MeSH terms in PubMed). These 200 top-cited articles were analyzed and clustered by performing factor analysis and social network analysis (SNA). The study presented three types of results: (1) descriptive statistics, (2) classification analysis, and (3) inferential statistics. The chi-square test was used to examine the frequency of clusters and journals, and forest plots were used to analyze differences between journals in terms of research topics. It was observed that (1) the United States dominated publications, accounting for 54% of 200 articles; the MeSH term of surgery was simultaneously highlighted in both journals using a word cloud generator; (2) five-term clusters were identified, namely, (i) Pain & Prognosis, (ii) Statistics & Data, (iii) Spine & Surgery, (iv) physiopathology, and (v) physiology; (4) there were no differences in distribution counts among categories between journals (Chi Square = 1.64, df = 4, p = 0.82), but differences in category(factor) scores between journals were found(Q-statistic = 484.94, df = 4, p

Keywords: Text mining; Social network analysis; Forest plot; MeSH term; SNA (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11192-022-04549-6

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