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Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata
Technical Report MS-CIS-10-05, University of Pennsylvania, Dept. of Computer and Information Science (2010)
  • Andrew G. West, University of Pennsylvania
  • Sampath Kannan, University of Pennsylvania
  • Insup Lee, University of Pennsylvania
Abstract

Blatantly unproductive edits undermine the quality of the collaboratively-edited encyclopedia, Wikipedia. They not only disseminate dishonest and offensive content, but force editors to waste time undoing such acts of vandalism. Language- processing has been applied to combat these malicious edits, but as with email spam, these filters are evadable and computationally complex. Meanwhile, recent research has shown spatial and temporal features effective in mitigating email spam, while being lightweight and robust.

In this paper, we leverage the spatio-temporal properties of revision metadata to detect vandalism on Wikipedia. An administrative form of reversion called rollback enables the tagging of malicious edits, which are contrasted with nonoffending edits in numerous dimensions. Crucially, none of these features require inspection of the article or revision text. Ultimately, a classifier is produced which flags vandalism at performance comparable to the natural-language efforts we intend to complement (85% accuracy at 50% recall). The classifier is scalable (processing 100+ edits a second) and has been used to locate over 5,000 manually-confirmed incidents of vandalism outside our labeled set.

Disciplines
Publication Date
January 1, 2010
Citation Information
Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata. Andrew G. West, Sampath Kannan, and Insup Lee. Technical Report MS-CIS-10-05, University of Pennsylvania, Dept. of Computer and Information Science, February 2010.