SocksCatch: Automatic detection and grouping of sockpuppets in social media

摘要

Since 2004, online social media (OSN) have evolved hugely. This fast development had interesting effects to increase the connection and information exchange of users, but some negative effects also appeared, including fake accounts number growing day after day.The sockpuppets are the multiple fake accounts created by the same user. They are the source of several types of manipulation such as those created to praise, defend or support a person or organization or to manipulate public opinion.In this article, we present SocksCatch, a complete process to detect and group sockpuppets which is composed of three main phases: first phase is the data collection and selection; second phase is the detection of the sockpuppet accounts using machine learning algorithms; third phase is the grouping of sockpuppet accounts created by the same user using graph theory.Experiments have been performed for the three phases using real data crawled from english Wikipedia. The results compare six machine learning algorithms for the detection phase and show that SocksCatch detects between 89% and 95% of the selected sockpuppets depending on the algorithms. We also compare five community detection algorithms for the grouping phase, and show that SocksCatch’s grouped sockpuppets and the real sockpuppet’s groups are similar between 80% and 88%, according to the cluster’s comparison measures: normalized variation of information (NVI) and normalized mutual information (NMI).