In the past years, Spotify and YouTube have been dominating the music production scene, thus storing valuable information about how songs achieve popularity and become a successful hit. Past researches tried to determine the hit-potentiality focusing on the audio characteristics and showed the limitation of this approach. In this work, we studied popularity dynamics of songs including, together with audio characteristics, features from artist and release context, and processed these using machine learning techniques. As metrics for popularity, we employed the cumulative views on YouTube achieved by a song's video in this streaming platform. First, we investigated the relationship between songs with similar audio feature and their release date to identify sub-genres with concomitant popularity.