Automated Detection of Sockpuppet Accounts in Wikipedia

Mostofa Najmus Sakib, Francesca Spezzano

Abstract: This paper addresses the problem of identifying sockpuppet accounts on Wikipedia. We formulate the problem as a binary classification task and propose a set of features based on user activity and the semantics of their contributions to separate sockpuppets from benign users. We tested our system on a dataset we built (and released to the research community) containing 17K accounts validated as sockpuppets. Experimental results show that our approach achieves an F1-score of 0.82 and outperforms other systems proposed in the literature. Moreover, our proposed approach is able to achieve an F1-score of 0.73 at detecting sockpuppet accounts by just considering their first edit.