  Forecasting the popularity of new songs has become a standard practice in the
music industry and provides a comparative advantage for those that do it well.
Considerable efforts were put into machine learning prediction models for that
purpose. It is known that in these models, relevant predictive parameters
include intrinsic lyrical and acoustic characteristics, extrinsic factors
(e.g., publisher influence and support), and the previous popularity of the
artists. Much less attention was given to the social components of the
spreading of song popularity. Recently, evidence for musical homophily - the
tendency that people who are socially linked also share musical tastes - was
reported. Here we determine how musical homophily can be used to predict song
popularity. The study is based on an extensive dataset from the last.fm online
music platform from which we can extract social links between listeners and
their listening patterns. To quantify the importance of networks in the
spreading of songs that eventually determines their popularity, we use musical
homophily to design a predictive influence parameter and show that its
inclusion in state-of-the-art machine learning models enhances predictions of
song popularity. The influence parameter improves the prediction precision
(TP/(TP+FN)) by about 50% from 0.14 to 0.21, indicating that the social
component in the spreading of music plays at least as significant a role as the
artist's popularity or the impact of the genre.
