To rate a person’s level of expertise–as “good,” “average,” or “novice”–Noll’s team integrated a second factor into their algorithm: temporal information. “The idea is that the early bird gets the worm,” says Ching-man Au Yeung, a researcher in electronics and computer science at the University of Southampton in the U.K., who collaborated with Noll on the development of the algorithm. Those people who first discover content that subsequently receives a lot of tagging can be identified as trend setters in a community. “They are finding the usefulness of a document before others do,” says Au Yeung, who compares their acquisition of influence to the way a knowledgeable academic builds a reputation.

In contrast, followers find useful content later and tag it because it is already popular. These are more likely to be spammers, “people who identify a topic that grows in importance and use it to point to their own stuff,” says Scott Golder, formerly a research scientist at Hewlett Packard and currently a graduate student at Cornell. Golder adds that the SPEAR algorithm employs “a very smart set of criteria that has not been used before in computer science.”

The researchers tested their algorithm using data from Delicious, analyzing over 71,000 Web documents, 0.5 million users, and 2 million shared bookmarks. “We set the algorithm to find JavaScript experts, for example, and it produced a list of users; the top two were professional software developers,” says Noll. “None of the spammers ranked in the top 200.”

Noll says that the algorithm can be adjusted for any online community, including Twitter and music-sharing sites. The work was presented last week at the SIGIR Conference in Boston. Noll says that companies including Microsoft were interested in using the algorithm for social Web search, where documents are ranked based on users’ bookmarks.

“I’d expect … this combination of mutual reinforcement with the distinction between discoverers and followers to be useful in many domains,” says Kleinberg.