earticle

영어

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.

중국어

本研究以韩国研究财团提供的登载学术杂志D/B提取的资料为对象􎨳利用关键词网络分析􎨳观 察了抗老化相关研究动向􀕶以2013年至2022年的美容领域抗老化相关论文118篇为对象􎨳提取 关键词(主题词)􎨳利用Textom提纯数据􎨳用UCINET 6进行了网络分析􀕶研究结果显示􎨳首先􎨳 “抗老化(34次)”出现最多􎨳“化妆品(30次)”􀕵“皱纹(23次)”􀕵“皮肤(19次)”􀕵“提取物(17次)”􀕵 “胶原蛋白(17次)”􀕵“改善(16次)”􀕵“抗炎(14次)”􀕵“美白(11次)”等主题词的出现频率较高􀕶第 二􎨳利用NetDraw􎨳将抗老化相关关键词的整个网络结构进行可视化􎨳可以同时掌握了各个 关键词之间的关系􀕶第三􎨳对抗老化相关关键词进行网络中心性分析结果显示􎨳“化妆品”的 连接中心性最高􎨳“化妆品”􀕵“提取物”􀕵“改善”等是连接强度最高的主题词􀕶接近中心性分析 结果显示􎨳“化妆品”与其他关键词保持最近的距离􎨳中介中心性分析结果显示􎨳“皮肤”出现最 多􎨳“皮肤”􀕵“胶原蛋白”􀕵“提取物”􀕵“黑色素”􀕵“细胞”等是高于频率排名的关键词􎨳与网络 中的其他关键词相比􎨳出现相对较高的中介效果􀕶