Predicting Music Popularity on Streaming Platforms - Araujo, Carlos Soares; Cristo, Marco; Giusti, Rafael (Anais do Simpósio Brasileiro de Computação Musical (SBCM); 2019: Anais do XVII Simpósio Brasileiro de Computação Musical, 2019)
Online streaming platforms have become one of the most important forms of music consumption. Most streaming platforms provide tools to assess the popularity of a song in the forms of scores and rankings. In this paper, we address two issues related to song popularity. First, we predict whether an already popular song may attract higher-than-average public interest and become “viral”. Second, we predict whether sudden spikes in public interest will translate into long-term popularity growth. We base our findings in data from the streaming platform Spotify and consider appearances in its “Most-Popular” list as indicative of popularity, and appearances in its “Virals” list as indicative of interest growth. We approach the problem as a classification task and employ a Support Vector Machine model built on popularity information to predict interest, and vice versa. We also verify if acoustic information can provide useful features for both tasks. Our results show that the popularity information alone is sufficient to predict future interest growth, achieving a F1-score above 90% at predicting whether a song will be featured in the “Virals” list after being observed in the “Most-Popular”.