Decoding Soft: Gen Z Soft Masculine Menswear Trends Through Semantic Network Analysis

With the sprouting of social media in recent years, new menswear tribes have emerged – e-bois, sad boys & softboys among Generation Z. This study examines menswear trends over 10 years through semantic network analysis on Orange3 machine learning. We aim to debunk soft masculinity in high-end fashion popularised by Gen Z cultures which differ from dated concepts such as androgyny and metrosexual. Through semantic network analysis, we quantified i) dominant design trends, ii) examined the connective power of trends under the degree of centrality and iii) studied the correlation between concepts. Our results revealed dominant trends in menswear with their corresponding design features and how soft trends have evolved through time-series analysis. The methods of this study overcome human-based forecasting’s predispositions by analysing 3,047 menswear collection reports from Vogue US via machine learning combining technological efficiency with human ingenuity. Our research points to contextualising menswear trends into semantic structures reinforced by quantitative and qualitative analysis. Our study demonstrates the feasibility of adopting this methodology into organising design trend concepts, recognising patterns, fashion forecasting, academic research, and potentials as an ideation tool for the creative industry.