Approach is based on machine learning and gradually improves performance

Researchers at Ben-Gurion University, along with colleagues in Washington, developed a new generic method to identify fake accounts on most social networks, including the popular sites Facebook and Twitter. The algorithm is based on machine learning and can even increase its performance over time.

Better to expose fakes

“With recent disturbing news about Russia’s failure to protect user privacy and deliberate use of social media to influence elections, eliminating fake users has never been more important,”

emphasizes study author Dima Kagan. The researchers tested their newly developed algorithm on simulated and real data sets in ten different social networks. He achieved good results on all sides.

The algorithm consists of two runs based on machine learning. In the first pass, a “Link Prediction Classifier” is created that estimates the probability of a connection between two users with high accuracy. In the second pass, a new set of meta-features is generated based on the features previously created by the Link Prediction Classifier.

Create more security

Finally, the researchers used these meta-features and constructed a generic classifier that detects fake profiles on social networks.

“Our method outperforms other anomaly detection methods and we believe it has significant potential for a wide range of applications, particularly in the area of ​​cyber security,”

summarize the researchers. The team also recently developed a “Social Privacy Protector” to help users analyze their friends list in seconds and find fake profiles within them.


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