This study aimed at examining fashion consumers’ awareness during the COVID-19 pandemic. Big data analysis methods, such as text mining, social network analysis, and regression analysis, were applied to user posts about fashion on Korean portal websites and social media during COVID-19. R 3.4.4, UCINET 6, and SPSS 25.0 software were used to analyze the data. The results were as follows. In researching the popular fashion-related topics during COVID-19, the prevention of infection and prophylaxis were significant concerns in the early stage (Jan 1 to Jan 31, 2020), and changed to online channels and online fashion platforms. Then, various topics and fashion keywords appeared with COVID-19-related keywords afterwards. Fashion-related subjects concerned prophylaxis, home life, digital and beauty products, online channels, and fashion consumption. In comparing fashion consumers’ awareness during COVID-19 with SARS and MERS, “face masks” was the common keyword for all three illnesses; yet, the prevention of infection was a major consumer concern in fashion-related subjects during COVD-19 only. As COVD-19 cases increased, the search volume for face masks, shoes, and home clothes also increased. Consumer awareness about face masks shifted from blocking yellow dust and micro-dust to the sociocultural significance and short supply. Keywords related to performance turned out to be the major awareness as to shoes, and home clothes were repurposed with an expanded range of use.

Abstract
I. Introduction
Ⅱ. Theoretical Background
    1. Change of fashion industry in COVID-19 situation
    2. Text mining and semantic network analysis
Ⅲ. Research Method
    1. Data collection
    2. Measurement and research process
Ⅳ. Result
    1. Fashion-related topic changes in COVID-19
    2. Comparison with fashion consumer perceptionin SARS and MERS situations
    3. Effect of COVID-19’s spread on fashion productinterest
Ⅴ. Conclusion
References

• Yeong-Hyeon Choi(한양대학교 의류학과) | 최영현
• Kyu-Hye Lee(한양대학교 의류학과) | 이규혜 Corresponding author