A Comparative Analysis of Seasonal Skincare Trends Using Social Network Analysis

This study aimed to examine the structural characteristics and seasonal variations of skincare trends using semantic network analysis. Online text data collected from August 1, 2023, to July 31, 2025, were divided into four seasons to explore seasonal differences in skincare-related keywords. Frequency analysis, network analysis, degree centrality analysis, CONCOR clustering, and QAP correlation analysis were conducted to identify relationships and structural patterns among keywords. Frequency analysis showed that in spring, keywords related to environmental factors such as fine dust and pollen were prominent, while in summer, ultraviolet exposure and sebum-related concerns appeared more frequently. In autumn, issues related to dryness and skin elasticity were emphasized, whereas in winter, greater attention was given to skin barrier function and protection. Across all seasons, skincare keyword networks exhibited high density and short average path lengths, indicating strong interconnections among keywords. Degree centrality analysis revealed that although a common structural pattern centered on hydration and moisturization was maintained, the composition and connection strength of central keywords varied depending on seasonal environmental conditions. CONCOR analysis identified distinct cluster patterns for each season, and QAP analysis indicated higher structural similarity between autumn and winter networks than between winter and spring networks. These findings suggest that skincare trends emerge within interconnected network structures influenced by seasonal environmental factors and provide practical implications for skincare strategy development and content planning.