Abstract: Nowadays, social media sites are growing day after day and they are gathering the most important number of visitors on the internet. But among these visitors there is also a big number of manipulators that try to benefit from the others for many reasons, which leads to increase the need to detect the fake accounts that try to manipulate over the online social media (OSN). A malicious account on OSNs can be created for different objectives such as social spam, identity theft, spear phishing and sybil attacks. In this article, we study and analyze the multiple fake accounts created by one user in order to manipulate and bypass the OSN regulation. We present a new methodology to detect the multiple identity fake accounts and validate it on a set of 10000 accounts extracted from English Wikipedia (EnWiki). In this methodology, we propose a set of features that differentiate between fake and legitimate accounts and grows on previous literature, then we train and test them using machine learning algorithms. The results compare several machine learning algorithms to show that our new features and training data enable to detect 99% of fake accounts, improving previous results from the literature.
