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http://dx.doi.org/10.5850/JKSCT.2022.46.6.1058

Quantitative Study of Soft Masculine Trends in Contemporary Menswear Using Semantic Network Analysis  

Tin Chun Cheung (School of Fashion and Textiles, The Hong Kong Polytechnic University)
Sun Young Choi (School of Fashion and Textiles, The Hong Kong Polytechnic University)
Publication Information
Journal of the Korean Society of Clothing and Textiles / v.46, no.6, 2022 , pp. 1058-1073 More about this Journal
Abstract
Big data analytics and social media have shifted the way fashion trends are dictated. Fashion as a medium for expressing gender has created new concepts of masculinity in popular culture, where men are increasingly depicted in a softer style. In this study, we analyzed 2,879 menswear collections over a 10-year period from Vogue US to uncover key menswear trends. Using Semantic Network Analysis (SNA) on Orange3, we were able to quantitatively analyze how contemporary menswear designers interpreted diversified trends of masculinity on the runway. Frequency and degree centrality were measured to weigh the significance of trend keywords. "Jacket (f = 3056; DC = 0.80), shirt (f = 1912; DC = 0.60) and pant (f = 1618; DC = 0.53)" were among the most prominent keywords. Our results showed that soft masculine keywords, e.g., "lace, floral, and pink" also appeared, but with the majority scoring DC = < 0.10. The findings provide an insight into key menswear trends through frequency, degree centrality measurements, time-series analysis, egocentric, and visual semantic networks. This also demonstrates the feasibility of using text analytics to visualize design trends, concepts, and patterns for application as an ideation tool for academic researchers, designers, and fashion retailers.
Keywords
Data mining; Degree centrality; Menswear trends; Semantic network analysis; Soft masculinity;
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Times Cited By KSCI : 6  (Citation Analysis)
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