Automated Detection of Sockpuppet Accounts in Wikipedia

Mostofa Najmus Sakib, Francesca Spezzano

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
EditorsJisun An, Chelmis Charalampos, Walid Magdy
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages155-158
Number of pages4
ISBN (Electronic)9781665456616
DOIs
StatePublished - 2022
Event14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022 - Virtual, Online, Turkey
Duration: 10 Nov 202213 Nov 2022

Publication series

NameProceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

Conference

Conference14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Country/TerritoryTurkey
CityVirtual, Online
Period10/11/2213/11/22

Keywords

  • early prediction
  • malicious activity
  • Sockpuppetry

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