Mining and Forecasting Career Trajectories of Music Artists
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.
Keywords
Cite
@article{arxiv.1805.03324,
title = {Mining and Forecasting Career Trajectories of Music Artists},
author = {Shushan Arakelyan and Fred Morstatter and Margaret Martin and Emilio Ferrara and Aram Galstyan},
journal= {arXiv preprint arXiv:1805.03324},
year = {2018}
}
Comments
In HT'18: 29th ACM Conference on Hypertext and Social Media