To help combat bogus eBay (NASDAQ:EBAY) seller ratings, which can mislead customers by giving a stamp of approval to fraudulent vendors, researchers at Carnegie Mellon University have developed a technique to help validate the scores.

EBay fraudsters have found ways to artificially elevate their seller ratings, by conducting transactions with friends or using alternate online identities to post positive feedback about themselves — much to the frustration of legitimate vendors who work hard to earn genuinely positive ratings.

“We want to help people detect potential fraud before the fraud occurs,” research associate Duen Horng “Polo” Chau, one of the scientists behind the fraud detection technique, said in a statement.

Chau, along with professor Christos Faloutsos and two other students, analyzed roughly one million transactions between almost 66,000 eBay users. They plotted the transactions as a graph to identify the distinctive pattern created by sales between fraudsters and their accomplices, then designed an algorithm to detect unnaturally close-knit groups of people that traded mainly among themselves.

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