키워드 네트워크 분석을 활용한 항노화 분야 연구동향 분석

Abstract

This study attempted to investigate recent research trends in anti-aging through keyword network analysis with the data from the academic journal database of the NRF. For this, keywords were extracted from a total of 118 anti-aging-related papers from 2013 to 2022 and cleansed with Textom. Then, the network analysis was performed, using UCINET 6, and the results found the followings: First, in terms of keyword frequency, ‘anti-oxidation’ was the highest with 34 times, followed by ‘cosmetics (30 times)’, ‘wrinkles (23 times)’, ‘skin (19 times)’, ‘extract (17 times)’, ‘collagen (17 times)’, ‘improvement (16 times)’, ‘anti-inflammation (14 times)’ and ‘whitening (11 times)’. Second, it was able to examine inter-keyword relations by visualizing a total network structure on the ‘anti-aging’ keyword, using NetDraw. Third, according to analysis of network centrality on the ‘anti-aging’ keyword, ‘cosmetics’ was the highest in terms of degree centrality. In addition, ‘cosmetics’, ‘extract’ and ‘improvement’ revealed high network strength. In closeness centrality, it was ‘cosmetics’ which maintained the shortest distance with other keywords. According to analysis of betweenness centrality, ‘skin’ was the most frequent keyword. In addition, ‘skin’, ‘collagen’, ‘extract’, ‘melanin’ and ‘cell’ were higher in frequency ranking, showing relatively high mediating effects, compared to other keywords.