Researchers at Ben-Gurion University of the Negev (BGU) in Israel and the University of Washington have created a generic method to detect fake accounts on most social networks.
The method is based on the assumption that fake accounts form improbable links to other users.
The team tested their algorithm on simulated and real-world data sets on 10 different social networks, with strong results on both, according to BGU’s Dima Kagan.
The algorithm consists of two main iterations based on machine learning, the first of which constructs a link prediction classifier that estimates the probability of an existing link between two users. The second iteration generates a new set of meta-features based on the features developed by the link prediction classifier.
The researchers used these meta-features to build a generic classifier that detects fake profiles. The results show the algorithm can identify people who have the strongest friendship ties on a social network, as well as malicious users, the researchers say.
From American Associates, Ben-Gurion University of the Negev
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Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA
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