Elucidation of the Relationship Between a Song’s Spotify Descriptive Metrics and its Popularity on Various Platforms


The music industry and personal music consumption have evolved dramatically with the advent of streaming plat-forms. In this evolving landscape, there is considerable interest in understanding what factors contribute to a song’s popularity. Extrinsic (i.e. non-acoustic) features of a given song, such as the record label, and/or intrinsic (i.e. acoustic) features such as its energy may contribute to popularity on a given digital platform. In this work, we, for the first time, sought to systematically study how a song’s Spotify acoustic descriptive features correlated with popularity metrics on various Internet platforms. Since each platform defines “popularity” according to platform-specific metrics, a large-scale correlation-based analysis was generated. The digital platforms considered in this article are Google Trends, WhoSampled, TikTok, Twitter, YouTube, and the Billboard Top-100. Platform-specific scrapers were created and all data was aggregated with the Spotify Echo Nest dataset of descriptive acoustic metrics. While the majority of correlations were unre-markable considering both Spearman and Pearson coefficients, a number of corroborating and contradictory findings resulted, with notable implications for acoustic features on various digital platforms. Notably, the YouTube view count was found to be positively correlated to the Spotify song popularity (p = 0.822), year (p = 0.600), and energy (p = 0.455) and moderately negatively correlated to accousticness (p = −0.542) and instrumentalness (p = −0.345). All reproducing code and aggregated data from this work are open-source for use by the broader research community.

2022 IEEE 46th Annual Computer Software and Applications Conference