The next ‘American Idol’? Ask your computer

Bryn Nelson Columnist

Could a computer pick the next “American Idol”? The next Ludacris or Madonna?

New software by Israeli researchers promises to take much of the guesswork (and endless cover songs) out of figuring out which hip-hop or R & B artist will be the next "it" star in the United States. Using a mathematical formula to sort music requests logged by the massive Gnutella peer-to-peer file-sharing network, the researchers have boasted an enviable 15 percent to 30 percent success rate in automatically choosing artists or bands with breakout potential.

The solution, according to Tel Aviv University electrical engineer Yuval Shavitt and his colleagues, “is based on the observation that emerging artists, especially rappers, have a discernable stronghold of fans in their hometown area, where they are able to perform and market their music.”

Crucially, the researchers were able to extract geographic information from many of the 10 million to 40 million requests logged by Gnutella users every day. In some cases, the locations could be pinpointed to the level of a city borough, as in New York.

“What’s happening on the Internet is that it’s a whole collection of individual actions,” Shavitt said. “Such a huge collection of actions should have a very strong signal about many things, and in this case, it was so obvious that it should be a way to pick up trends — music, for example.”

Measuring popularity
But how do you mathematically model the concept of local popularity for a song or band? For up-and-coming artists, Shavitt said, the route to national success usually begins in their neighborhood or hometown.

“They’re performing in local clubs and attracting attention, and people will start looking for their music,” he said. “If your music is good, you will create local buzz. And this will be detected by a local rise in the number of queries in the system.”

For the research, presented in August at the Knowledge Discovery and Data Mining conference in Las Vegas, the team collected location-tagged Gnutella data from mid-October 2006 through July 2007. The computer algorithm monitored metropolitan regions around the country and checked for any query streams that were fast becoming local floods, but were relatively unnoticeable elsewhere.

Jamie Mccarthy / WireImage Hip-hop group Shop Boyz performs on MTV's "TRL" on Aug. 14, 2007. The group's hit single, "Party Like a Rockstar," eventually hit No. 2 on the charts.

In the case of the Grammy-nominated hip-hop group Shop Boyz, the algorithm flagged the band as a potential breakthrough act after detecting a rapid rise in queries from fans in the band’s hometown of Atlanta six weeks into 2007. Nine weeks later, the band signed with Universal Republic, and entered the Billboard rap charts three weeks after that, eventually peaking at No. 2 with the single, “Party Like a Rockstar.”

“With Shop Boyz, we managed to get a nine-week lead on the industry,” Shavitt said, even though the group didn’t even crack the 10,000 most popular queries nationwide at the time. With other artists, he said, the algorithm’s advance notice has been more on the order of 6 to 8 weeks — still plenty of time to get a jump on the competition.

For St. Louis-based rapper Huey, Shavitt said the system spotted a local spike in requests for his “Pop, Lock & Drop It” before mid-January 2007. In early March, the song debuted on the Billboard Hot 100 at No. 98.

Requests for songs by an established artist like Madonna, on the other hand, would show a relatively uniform geographical distribution and steady numbers over time.

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