Researchers from Ben-Gurion University of the Negev and the University of Washington have developed an algorithm to locate fake users on many social media sites, including Facebook and Twitter.
The algorithm consists of two main iterations based on machine-learning algorithms. The first constructs a link prediction classifier that can estimate, with high accuracy, the probability of a link existing between two users. The second iteration generates a new set of meta-features based on the features created by the link prediction classifier. The researchers used these meta-features and constructed a generic classifier that can detect fake profiles in a variety of online social networks.
“Overall, the results demonstrated that in a real-life friendship scenario we can detect people who have the strongest friendship ties as well as malicious users, even on Twitter,” said the researchers. “Our method outperforms other anomaly detection methods and we believe that it has considerable potential for a wide range of applications particularly in the cyber-security arena.”
Researchers at Ben-Gurion University previously developed the Social Privacy Protector to help users evaluate their friends list in seconds and identify which have few or no mutual links and might be “fake” profiles.
“With recent disturbing news about failures to safeguard user privacy, and targeted use of social media by Russia to influence elections, rooting out fake users has never been of greater importance,” said lead researcher Dima Kagan. “We tested our algorithm on simulated and real-world data sets on 10 different social networks and it performed well on both.”
Other researchers who contributed are Dr. Michael Fire of the University of Washington (former Ben-Gurion U. doctoral student) and Prof. Yuval Elovici, director of the Telekom Innovation Labs@BGU, director of Cyber@BGU and a member of the BGU Department of Software and Information Systems Engineering.
