Spotting out emerging artists using geo-aware analysis of P2P query strings

Abstract

Record label companies would like to identify potential artists as early as possible in their careers, before other companies approach the artists with competing contracts. The vast number of candidates makes the process of identifying the ones with high success potential time consuming and laborious. This paper demonstrates how datamining of P2P query strings can be used in order to mechanize most of this detection process. Using a unique intercepting system over the Gnutella network, we were able to capture an unprecedented amount of geographically identified (geo-aware) queries, allowing us to investigate the diffusion of music related queries in time and space. Our solution is based on the observation that emerging artists, especially rappers, have a discernible stronghold of fans in their hometown area, where they are able to perform and market their music. In a file sharing network, this is reflected as a delta function spatial distribution of content queries. Using this observation, we devised a detection algorithm for emerging artists, that looks for performers with sharp increase in popularity in a small geographic region though still unnoticable nation wide. The algorithm can suggest a short list of artists with breakthrough potential, from which we showed that about 30% translate the potential to national success.

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