Mining and Forecasting Career Trajectories of Music Artists

TL;DR

This paper explores how data from concert discovery platforms can be used to analyze and predict the career development of music artists, including milestones like signing with labels and venue performances.

Contribution

It introduces a new dataset from concert platforms and demonstrates methods to mine and forecast artists' career milestones and success indicators.

Findings

High centrality in artist-venue graphs correlates with artist success.

The dataset enables prediction of major career milestones.

Temporal analysis reveals patterns in artist career trajectories.

Abstract

Many musicians, from up-and-comers to established artists, rely heavily on performing live to promote and disseminate their music. To advertise live shows, artists often use concert discovery platforms that make it easier for their fans to track tour dates. In this paper, we ask whether digital traces of live performances generated on those platforms can be used to understand career trajectories of artists. First, we present a new dataset we constructed by cross-referencing data from such platforms. We then demonstrate how this dataset can be used to mine and predict important career milestones for the musicians, such as signing by a major music label, or performing at a certain venue. Finally, we perform a temporal analysis of the bipartite artist-venue graph, and demonstrate that high centrality on this graph is correlated with success.