PITTSBURGH — An algorithm developed at Carnegie Mellon University makes it easier to determine if someone has faked an Amazon or Yelp review or if a politician with a suspiciously large number of Twitter followers might have bought and paid for that popularity
The method, called FRAUDAR, marks the latest escalation in the cat-and-mouse game played by online fraudsters and the social media platforms that try to out them. In particular, the new algorithm makes it possible to see through camouflage that fraudsters use to make themselves look legitimate, said Christos Faloutsos, professor of machine learning and computer science.
In real-world experiments using Twitter data for 41.7 million users and 1.47 billion followers, FRAUDAR fingered more than 4,000 accounts not previously identified as fraudulent, including many that used known follower-buying services such as TweepMe and TweeterGetter.
“We’re not identifying anything criminal here, but these sorts of frauds can undermine people’s faith in online reviews and behaviors,” Faloutsos said. He noted most social media platforms try to flush out such fakery, and FRAUDAR’s approach could be useful in keeping up with the latest practices of fraudsters.
The CMU algorithm is available as open-source code at http://www.
Faloutsos and his data analytics team specialize in graph mining, a method that looks for patterns in the data. In this case, social media interactions are plotted as a graph, with each user represented as a dot, or node, and transactions between users represented as lines, or edges.
The state-of-the-art for detecting fraudsters, with tools such as Faloutsos’ NetProbe, is to find a pattern known as a “bipartite core.” These are groups of users who have many …