To what extent homophily and influencer networks explain song popularity
Niklas Reisz, Vito D. P. Servedio, and Stefan Thurner

TL;DR
This study demonstrates that incorporating social network data based on musical homophily significantly improves the accuracy of predicting song popularity using machine learning models.
Contribution
It introduces a novel influence parameter derived from social links and musical tastes, enhancing prediction models for song popularity.
Findings
Influence parameter improves prediction precision by ~50%.
Social components are as significant as artist popularity.
Musical homophily effectively predicts song popularity.
Abstract
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…
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Taxonomy
TopicsMusic and Audio Processing · Opinion Dynamics and Social Influence · Complex Network Analysis Techniques
