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Overview of the Wikidata Vandalism Detection Task at WSDM Cup 2017

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arxiv 1712.05956 v1 pith:DPTDXFVR submitted 2017-12-16 cs.IR

Stefan Heindorf (1) , Martin Potthast (2) , Gregor Engels (1) , Benno Stein (3) ((1) Paderborn University , (2) Leipzig University , (3) Bauhaus-Universität Weimar) This is my paper · ORCID
classification cs.IR
keywords taskvandalismdetectionsoftwarewikidatasourcewsdmachieves
verification ladder T0 review T1 audit T2 compute T3 formal

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We report on the Wikidata vandalism detection task at the WSDM Cup 2017. The task received five submissions for which this paper describes their evaluation and a comparison to state of the art baselines. Unlike previous work, we recast Wikidata vandalism detection as an online learning problem, requiring participant software to predict vandalism in near real-time. The best-performing approach achieves a ROC-AUC of 0.947 at a PR-AUC of 0.458. In particular, this task was organized as a software submission task: to maximize reproducibility as well as to foster future research and development on this task, the participants were asked to submit their working software to the TIRA experimentation platform along with the source code for open source release.

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Cited by 2 Pith papers

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  1. Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detection

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A single multilingual language model serializes all Wikidata edits into text and outperforms the production ORES system in vandalism detection and fairness.

  2. Wikidata from a Research Perspective -- A Systematic Mapping Study of Wikidata

    cs.DL 2019-08 conditional novelty 4.0 of 10

    A systematic mapping study classifies 67 peer-reviewed Wikidata research papers into five topic areas and identifies gaps such as multilingualism and usability.

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