From CMU. Tested against some very large set of Twitter data. Uses a form of Graph Analytics. Python code is available as Open Source. Uses a form of Faloutsos' NetProbe (technical paper) for the analysis.
New algorithm detects online fraudsters: Method sees through camouflage to reveal fake followers, reviewers.
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 (Python) code at http://www.andrew.cmu.edu/user/bhooi/camo.zip. A research paper describing the algorithm won the Best Paper Award last month at the Association for Computing Machinery's Conference on Knowledge Discovery and Data Mining (KDD2016) in San Francisco. ..... "