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“PeerIndex generates some nice results,” says Daniel Tunkelang, who has developed his own algorithm for ranking Twitter users that considers both a person’s follower count and the follower counts of their followers. His ranking is available through the site TunkRank. “The immediate application of such lists is suggesting followers for a given topic,” Tunkelang says. Authority measures could eventually be used to filter and organize the information people see on a social network, he says.

PeerIndex has a webpage for each person it has rated the authority of and invites people to connect theirs to their LinkedIn or Facebook profiles. It may be possible to apply similar authority measures to these social networks, says Azhar.

Tunkelang agrees that authority measures could add value to other networks. “I’d love to see LinkedIn quantify the expertise of the people in its network,” he says, adding that authority ranking is likely to become an important feature of question-answering communities like Quora.

An issue that any ranking system will need to cope with is spam–a growing problem on Twitter and other networks. Computer science professor Daniel Gayo-Avello at the University of Oviedo, in Spain, recently published a study showing how different Twitter rankings are skewed by spam accounts. “One of the lessons is that follower count is not a robust measure of authority, since it is easily manipulated,” says Tunkelang. His algorithm fared the best out of those tested by Gayo-Avello.

A content-centric approach like PeerIndex’s sidesteps the issue of inflated follower counts, but any ranking that becomes popular will likely attract attention from people hoping to game the system. “I think we have to expect an arms race the moment that any ranking algorithm becomes popular enough to be worth manipulating,” says Tunkelang.

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