The best talent
scouts have a special instinct that tells them if a new band has the stuff to
make it big - and make the record label money. Now, researchers have developed
a software program that uses data from search queries on peer-to-peer
file-sharing networks to predict which new artists will have hit songs.
researchers, led by Yuval Shavitt of Tel Aviv University, hope that music producers and
record labels could use the tool to identify the next big music phenomenon. The
software might also interest young people who want to be up on the next big
computer algorithm has shown to be successful so far, achieving a success rate
of up to 50 percent (half of the algorithm's predictions turned out to be true
hits). For example, the software flagged Soulja Boy's "Crank That" and Sean
Kingston's "Temperature" in April 2007, and both songs became Billboard hits in
June 2007. The software also identified Shop Boyz' single "Party Like a
Rockstar" before the band signed to Universal, and several weeks before the
song became a national hit.
To do this,
the researchers analyze 30-40 million search queries entered on Gnutella, the
largest peer-to-peer file-sharing network in the US. In the beginning, queries almost always
come from a single geographic location (the artist's hometown), but if they
grow exponentially, the search queries prove to be a reliable predictor of a
future hit artist.
serving as a magic ball for record labels, the software could have other
applications. Another researcher on the project, grad student Noam Koenigstein,
hopes to use the software to examine new songs by already popular artists like
Madonna, and see if these songs sell well because they have the criteria of a
"hit," or just because they're sung by Madonna. In addition, the researchers
said that the same software can be used to predict the popularity of other
entertainment products such as TV shows and YouTube videos.
via: Tel Aviv University