This indicated that the Spotify metrics we studied - including acousticness, danceability, duration, energy, explicitness, instrumentalness, liveness, speechiness (a measure of the presence of spoken words in a song), tempo and release year - were not strong predictors of the song’s popularity. Preliminary results concluded that features are not linearly correlated, with some expected exceptions including songs’ energy. We also used the songs’ features to generate machine learning models to predict Spotify song popularity. In a related study, researchers collected data for Billboard’s Hot 100 from 1958 to 2013 and found that songs with a higher tempo and danceability often get a higher peak position on the Billboard charts. Our analysis determined that newer songs tend to last longer on the charts and that a song’s popularity affects how long it stays on the charts. Interestingly, we found no substantial correlations between the number of weeks a song remained on the charts, as a measure of popularity, and the acoustic features included in the study. To perform this study, we used two different data sets pertaining to songs that were Billboard hits from the early 1940s to 2020 and Spotify data related to over 600,000 tracks and over one million artists. The analysis we performed by looking at Spotify and Billboard revealed insights that are useful for the music industry. The rankings on the weekly Billboard Hot-100 are based on sales, online streams and radio plays in the United States. We sought to find trends and analyze the relationship between songs’ descriptive features and their popularity. Spotify’s metrics capture descriptive features such asacousticness, energy, danceability and instrumentalness (the collection of instruments and voices in a given piece). These features have been derived from a dataset which yielded categories for measuring and analyzing qualities of songs. We linked the datasets from the different platforms with Spotify’s acoustic descriptive metric or “descriptive features” for songs. With collaborators Laura Colley, Andrew Dybka, Adam Gauthier, Jacob Laboissonniere, Alexandre Mougeot and Nayeeb Mowla, we produced a systematic study that collected data from YouTube, Twitter, TikTok, Spotify and Billboard (Billboard Hot-100, sometimes also denoted by data researchers as “Billboard hot top” or in our work and others’ work, “Billboard Top-100”). The popularity of a song on digital platforms is considered a measure of the revenue the song may generate.Īs such, producers seek to answer questions like “How can we make the song more popular?” and “What are the characteristics of songs that make it the top charts?” How and whether this revenue reaches singers and songwriters at large is another matter. During the pandemic, although live music income dropped due to the cancellation of in-person performances, the income from streaming rose.Īs digital platforms like Spotify and TikTok have grown, the majority of music revenue has come to be contributed by digital media, mostly music streaming. Revenue in the music industry is derived from two sources that are affected by different factors: live music and recorded music. Perhaps the lyrics speak to an experience? Perhaps the energy makes it appealing? These questions are important to answer for music industry professionals, and analyzing data is a key part of this.Īt Carleton University, a group of data science researchers sought to answer the question: “What descriptive features of a song make it popular on music/online platforms?” While we might understand the genres or songs we appreciate, it’s not clear precisely why a certain song is more appealing or popular. Some of us seek live music at concerts, festivals and shows or rely on music to set the tone and mood of our days. We listen to it on our commutes and it resounds through shopping centres. Music is part of our lives in different ways. Can big data really predict what makes a song popular?
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