Detection of Multiple Identity Manipulation in Collaborative Projects

TLDR: This article proposes a set of features that grows on previous literature to use in automatic data analysis in order to detect the Sockpuppets accounts created on EnWiki and compares several machine learning algorithms to show that the new features and training data enable to detect 99\% of fake accounts, improving previous results from the literature.

Abstract: Various techniques are used to manipulate users in OSN environments such as social spam, identity theft, spear phishing and Sybil attacks... In this article, we are interested in analyzing the behavior of multiple fake accounts that try to bypass the OSN regulation. In the context of social media manipulation detection, we focus on the special case of multiple Identity accounts (Sockpuppet) created on English Wikipedia (EnWiki). We set up a complete methodology spanning from the data extraction from EnWiki to the training and testing of our selected data using several machine learning algorithms. In our methodology we propose a set of features that grows on previous literature to use in automatic data analysis in order to detect the Sockpuppets accounts created on EnWiki. We apply them on a database of 10.000 user accounts. 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.