An algorithm able to identify people's tribe "membership" would have a wide range of applications on the Internet from generating more relevant search results and ads to allowing social networks to provide better recommendations and content, computer scientists at the University of California, San Diego reported Tuesday.
So far, the algorithm is 48 percent accurate on average, which may seem low but is better than chance, which gets answers right only 9 percent of the time, they said.
While humans can recognize urban tribes at a glance, computers cannot, the researchers said.
"This is a first step," university computer researcher Serge Belongie said. "We are scratching the surface to figure out what the signals are."
The algorithm was better when it looked at group pictures rather than pictures of individuals, the researchers said, because it was easier to pick up social cues, such as clothing and hairdos, to determine people's tribes.
The researchers designed the algorithm to analyze the picture as the sum of its parts and attributes -- in this case haircuts, hair color, make up, jewelry and tattoos, for example.
To define urban tribes in the study, computer scientists turned to Wikipedia and selected the eight most popular categories in the encyclopedia's list of subcultures: biker, country, Goth, heavy metal, hip hop, hipster, raver and surfer.