Multiple identities detection in online social media
Zaher Yamak · HAL (Le Centre pour la Communication Scientifique Directe) · 2018
Since 2004, online social medias have grown hugely. This fast development had interesting effects to increase the connection and information exchange between users, but some negative effects also appeared, including fake accounts number growing day after day. Sockpuppets are multiple fake accounts created by a same user. They are the source of several types of manipulation such as those created to praise, defend or support a person or an organization, or to manipulate public opinion. In this thesis, we present SocksCatch, a complete process to detect and group sockpuppets, which is composed of three main phases: the first phase objective is the process preparation and data pre-processing; the second phase objective is the detection of the sockpuppet accounts using machine learning algorithms; the third phase objective is the grouping of sockpuppet accounts created by a same user using community detection algorithms. These phases are declined in three stages: a model stage to represent online social medias, where we propose a general model of social media dedicated to the detection and grouping of sockpuppets; an adaptation stage to adjust the process to a particular social media, where we instantiate and evaluate the SocksCatch model on a selected social media; and a real-time stage to detect and group the sockpuppets online, where SocksCatch is deployed online on a selected social media. Experiments have been performed on the adaptation stage using real data crawled from English Wikipedia. In order to find the best machine learning algorithm for sockpuppet's detection phase, the results of six machine learning algorithms are compared. In addition, they are compared with the literature, and the results show that our proposition improves the accuracy of the detection of sockpuppets. Furthermore, the results of five community detection algorithms are compared for sockpuppet's grouping phase, in order to find the best community detecton algorithm that will be used in real-time stage.