Name
Abstract | ||
|---|---|---|
We identify collaboration profiles in a musical network composed of successful artists. We argue that the way in which artists professionally connect with each other can significantly impact their success. Using data from Billboard and Spotify, we construct a collaborative success-based network to identify different profiles, as well as to analyze their impact on artists' popularity. Through topological metrics and clustering algorithms, we identify three well-defined communities with distinct collaboration patterns and notable discrepancies in levels of musical success. We have found that successful artists are more likely to have profiles with a high degree of interaction and high diversification. These findings offer a new perspective on success in the music industry, unraveling how collaboration profiles can contribute to an artist's popularity.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3297280.3297483 | SAC |
Keywords | Field | DocType |
collaboration profiles, creative collaboration, music success, social networks | Data science,Social network,Music industry,Sociology,Musical,Popularity,Diversification (marketing strategy),Cluster analysis | Conference |
ISBN | Citations | PageRank |
978-1-4503-5933-7 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariana O. Silva | 1 | 0 | 1.01 |
| Lais M. A. Rocha | 2 | 3 | 2.46 |
| Mirella Moura Moro | 3 | 64 | 8.37 |