패션 중고거래에 대한 소비자 인식 변화 탐색: 소셜 빅데이터를 활용한 텍스트 마이닝 분석

Abstract

This study examines recent shifts in consumer perceptions of fashion secondhand transactions by leveraging big data-driven text mining analysis of social media over the last three years (2021–2024). As the circular economy paradigm expands, the fashion secondhand market has become a crucial sector, offering not only economic benefits but also promoting sustainable, value-driven consumption. The study analyzes 28,127 documents from major Korean online platforms—including blogs, cafes, and news sites—using methods such as word frequency analysis, TF-IDF for the top 30 keywords, LDA topic modeling to identify six major topics, and CONCOR network analysis of the top 100 keywords per year. This approach uncovers core keywords, thematic clusters, and evolving discourse structures related to fashion secondhand trading. The analysis of the six major topics and 30 core keywords indicates that consumer discussions have evolved from simple consumer-to-consumer exchanges to encompass distinct submarkets, including luxury resale, limited-edition items, and vintage fashion. There is a notable focus on transaction risks and the necessity for authenticity verification. Additionally, the findings highlight that digital platforms play a pivotal role in market expansion and consumer engagement, while concerns regarding transaction safety and product authenticity remain prevalent throughout the study period. These insights offer strategic guidance for platform operators and industry stakeholders on trust-building mechanisms and targeted marketing strategies within the evolving secondhand fashion market.