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Decoding Korean men’s fashion trends: a text mining analysis of YouTube content

Author

Listed:
  • Nahyun Lee

    (Gachon University)

  • Sungeun Suh

    (Gachon University)

Abstract

YouTube immensely influences modern fashion. This study identified the key types of Korean male fashion YouTube video content as a large corpus of text data, examined the features of each type using text mining, and derived the characteristics of fashion content targeting Korean men in their 20s and 30s. We selected 10 leading Youtubers who focus on Korean men’s fashion and converted their video content into text using the ClovaNote program. Thereafter, we performed text mining analysis using the Textom program and a content analysis based on the results. We categorised the key types of Korean male fashion YouTube content as ‘fashion curation and review’, ‘outfit styling guide’, ‘fashion advice and tips’, ‘fashion feedback talk’, and ‘brand sponsorship and collaboration’. The study found the characteristics of Korean male fashion content as follows: casual and street trends in men’s fashion, guidance for fashion beginners and the significance of female perspectives, complement to body image, and smart fashion choice balancing price and quality. YouTube content provides guidance for fashion beginners and offers tips to Korean men to overcome their physical shortcomings. It also guides men to adopt attractive fashion styles from a female perspective, while encouraging rational consumption by balancing brand, price, and quality. This study aimed to overcome the limitations of previous qualitative research on Korean men’s fashion and YouTube by integrating big data text mining with qualitative analysis, endeavouring to balance objective and qualitative indicators. Additionally, this study is significant in that, unlike previous big data analyses based on textual data, it converts video data into text and applies big data and content analyses to examine Korean men’s fashion. Finally, as this study analyses Korean male consumers’ fashion needs and consumption characteristics, the findings can provide valuable information on the male fashion market.

Suggested Citation

  • Nahyun Lee & Sungeun Suh, 2025. "Decoding Korean men’s fashion trends: a text mining analysis of YouTube content," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-17, December.
    • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05051-z
    DOI: 10.1057/s41599-025-05051-z
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    References listed on IDEAS

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    1. Eun Joung Kim & Jung Yoon Kim, 2023. "Exploring the Online News Trends of the Metaverse in South Korea: A Data-Mining-Driven Semantic Network Analysis," Sustainability, MDPI, vol. 15(23), pages 1-19, November.
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