• Title/Summary/Keyword: 유튜브 쇼츠

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A Study on the Snack Culture Phenomenon in YouTube Shorts : Focused on Users' Perceived Value (유튜브 쇼츠(Youtube Shorts)의 스낵컬처(Snack Culture)현상 요인 분석: 사용자의 인지된 가치를 중심으로)

  • Won Jin Hong;Seung In Kim
    • Industry Promotion Research
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    • v.9 no.3
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    • pp.193-203
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    • 2024
  • This study analyzes user behavior on YouTube Shorts within snack culture and proposes short-form video production strategies by identifying key value factors. Prior research identified seven characteristics (playfulness, time killing, information provision, social presence, interactivity, escapism, conciseness) and criteria based on the 5W1H principles. Surveys and interviews revealed that key user values are playfulness, time killing, conciseness, and interactivity. Users engage without specific purposes, watch 10-20 consecutive pieces selectively, and use it in comfortable environments. This research provides insights for understanding user behavior and short-form video production strategies.

Analyzing K-POP idol popularity factors using music charts and new media data using machine learning (머신러닝을 활용한 음원 차트와 뉴미디어 데이터를 활용한 K-POP 아이돌 인기 요인 분석)

  • Jiwon Choi;Dayeon Jung;Kangkyu Choi;Taein Lim;Daehoon Kim;Jongkyn Jung;Seunmin Rho
    • Journal of Platform Technology
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    • v.12 no.1
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    • pp.55-66
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    • 2024
  • The K-POP market has become influential not only in culture but also in society as a whole, including diplomacy and environmental movements. As a result, various papers have been conducted based on machine learning to identify the success factors of idols by utilizing traditional data such as music and recordings. However, there is a limitation that previous studies have not reflected the influence of new media platforms such as Instagram releases, YouTube shorts, TikTok, Twitter, etc. on the popularity of idols. Therefore, it is difficult to clarify the causal relationship of recent idol success factors because the existing studies do not consider the daily changing media trends. To solve these problems, this paper proposes a data collection system and analysis methodology for idol-related data. By developing a container-based real-time data collection automation system that reflects the specificity of idol data, we secure the stability and scalability of idol data collection and compare and analyze the clusters of successful idols through a K-Means clustering-based outlier detection model. As a result, we were able to identify commonalities among successful idols such as gender, time of success after album release, and association with new media. Through this, it is expected that we can finally plan optimal comeback promotions for each idol, album type, and comeback period to improve the chances of idol success.

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