A Time-Series Analysis of Fast Fashion and Sustainability Discourse Using Text Mining

Fast fashion is leading the fashion market. However, fast fashion has been identified as a major contributor to environmental pollution through overproduction, inventory surplus and the disposal of waste, while sustainable fashion has emerged as a solution to those problems. Fast fashion, with its short fashion cycle and numerous purchases, clashes with the sustainability of long-term use and reduced consumption in the concept. It is a situation where there are a lot of concerns about how to solve the sustainability issue in fast fashion. To address the problem, it is necessary to understand the situation and evaluate how to deal with it. Therefore, the objectives of this study are to identify how to change and to converge the discourse according to time, analyzing textual data collected from Naver(2014~2024) in a time series depending on the combination of ‘fast fashion’ and ‘sustainability’. Data analysis was conducted using term frequency-inverse document frequency and clustering by TEXTOM. The results were as follows. Firstly, the fast fashion discourse excluding sustainability was changed gradually: from a discourse of industrial characteristics where trend and profit-driven logic takes precedence into a discourse of worrying about environmental effect and social responsibility. Secondly, fast fashion discourse including sustainability evolved discourse of sustainability as ‘value’ or ’ideal’ into discourse of sustainable strategics, technology and practical methods. Thirdly, the discourse including sustainability was changed from a corporate-led discourse to a consumer practice-oriented discourse. Fourthly, fast fashion discourse including sustainability was being strengthened linkages with global policies or agendas.