DISCOVERING SPOTIFY SONG DURATION TRENDS OVER A DECADE USING K-MEANS CLUSTERING FOR INSIGHTFUL ANALYSIS - Kori Mahendra, I Putu Nanda; Setiawan, Mochammad Agus; Esa Mahendra, I Gusti Putu; Glaudio Jansson, Fidelis Arlando; Ngongo Dolu, Claransius; Sugiartawan , Putu (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
This study examines trends in song duration on Spotify from 2014 to 2024, employing K-Means clustering to uncover patterns. Song duration, an essential metric reflecting listener preferences and platform strategies, was preprocessed for normalization and categorized into three clusters: short-duration tracks (1–2 minutes), medium-duration tracks (3–4 minutes), and long-duration tracks (5+ minutes). Temporal analysis revealed a significant increase in shorter tracks after 2019, influenced by the rise of short-form video platforms such as TikTok, which favor concise and engaging content. Despite this shift, medium-duration songs maintained consistent popularity throughout the decade. The findings underscore the evolving dynamics of the music industry, particularly the impact of technological innovations and changing audience behavior on production and consumption trends. This research contributes to understanding how digital platforms shape musical attributes, such as duration, to align with user engagement strategies. Future studies could incorporate additional variables, such as genre, artist popularity, or lyrical content, to expand on these insights. Exploring regional differences in song duration trends or integrating machine learning models beyond clustering could provide further depth, offering valuable implications for artists, producers, and streaming platforms seeking to optimize content strategies in a competitive and rapidly changing industry.