New algorithm identifies fake users on social networks

Fraudulent user profiles – bots – are a serious and growing concern on social media. By some estimates, as many as 48 million Twitter accounts and 270 million Facebook accounts are phony, designed for nefarious purposes from ruining reputations to influencing shoppers and voters.

Now, researchers from Israel’s Ben-Gurion University (BGU) of the Negev and from the University of Washington in Seattle say they have developed a generic method to detect fake accounts on most types of social networks, including Facebook and Twitter. According to their study published in the journal Social Network Analysis and Mining, the new method is based on the assumption that fake accounts tend to establish improbable links to other users in the networks. “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 Dima Kagan, lead researcher and a PhD student in BGU’s department of software and information systems engineering. 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. These meta-features are used to construct a generic classifier that can detect fake profiles in a variety of online social networks. -- More...

ISRAEL21c——Bio and Archives

ISRAEL21c was founded in 2001, in the wake of the Second Intifada, to broaden public understanding of Israel beyond typical portrayals in the mainstream media.

The organization’s founders – Israeli-American technology executives – understood the great power of the Internet and developed a first-of-its kind online product with global appeal and reach.