소셜 빅데이터로 살펴본 스트리트 패션 네트워크의 변화 -대중적 관심사와 클러스터 속성에 대한 시계열적 접근-

Abstract

Street fashion's influence on the fashion world is gradually expanding. Thus, we looked at the public attention of street fashion and the process of cluster attribute changes through social big data. Channels for unstructured data collection were limited to social media platforms, and data was collected in five-year intervals from 2010, 2015, and 2020. After the refining process, the 100 final keywords used for the analysis were extracted by period. Data analysis was conducted using TF-IDF value, network centrality value, and QAP correlation analyses. The networks in 2010 and 2015 showed similar relationships, and the correlation between the 2010 and 2020 periods was relatively weakened, indicating that changes in the network structure had taken place. 2010's network tended to explore street fashion and celebrity fashion overseas; however, the network's structure was diversified with the addition of various street fashion styles in 2015 and clusters related to marketing activities and consumer behavior in 2020. In particular, interest in brands and items sharply increased in 2020, highlighting the importance of brand value and product differentiation. In addition, new keywords, such as “eco-friendly” and “technology” began to appear, confirming that changes caused by the influence of millennials were impacting the fashion market. In addition to developing design-oriented fashion products, it is necessary for companies and designers to reconsider access to fashion through technological advancements, sustainable products, and marketing. This study is significant, as it provides basic data that can be used to establish a product and marketing strategy for street fashion brands.