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In the world we are living now, it&#x2019;s very difficult to find a people who access to Internet regularly but own no social network account. Social networks play a significant and day by day, more important role. People use Facebook or Twitter for communicating, news reading, information sharing, and advertising. However, these social networks lead to some issues. One of which is the need for a defense mechanism against fake accounts. To separate fake accounts from authentic ones is obviously a non trivial task. In this paper, we propose an empirical ranking scheme, comprising of both graph-based and feature-based approaches to aid the detection of fake Facebook profiles. Utilizing Support Vector Machine (SVM) [4] and Sybil- Walk [8], the model achieved high accuracy over the set of ten thousands Vietnamese Facebook accounts.

An Application of Random Walk on Fake Account Detection Problem: A Hybrid Approach