@inproceedings{a2b474c8735741fd868402831562754d,
title = "Automated Detection of Sockpuppet Accounts in Wikipedia",
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.",
keywords = "early prediction, malicious activity, Sockpuppetry",
author = "Sakib, {Mostofa Najmus} and Francesca Spezzano",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022 ; Conference date: 10-11-2022 Through 13-11-2022",
year = "2022",
doi = "10.1109/ASONAM55673.2022.10068604",
language = "English",
series = "Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "155--158",
editor = "Jisun An and Chelmis Charalampos and Walid Magdy",
booktitle = "Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022",
}