Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata

Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata


Author(s)	Andrew G. West, Sampath Kannan, Insup Lee
Published in	EUROSEC
Date	2010-04
Volume	Unknown [+]
Issue	Unknown [+]
Page(s)	22-28
Keyword(s)	Wikipedia, spatio-temporal reputation, vandalism, collaborative software, content-based access control (Extra: vandalism)
Peer-reviewed?	Yes
Language(s)	English
License(s)	Unknown [+]
Identifiers
ISBN	978-1-4503-0059-9
DOI	10.1145/1752046.1752050
OCLC Number	Unknown [+]
CiteULike	Unknown [+]
arXiv	Unknown [+]
PubMed	Unknown [+]
Related material
Concept(s)	Unknown [+]
Tool(s)	STiki
Dataset(s)	Wikipedia Vandalism Corpus (Andrew G. West)
Slides	www.cis.upenn.edu
Presentation	Not available [+]
Search
Article	BASE, CiteSeerX, Google Scholar
Web	Ask, Bing, Google (PDF), Yahoo!
Download and mirrors
Local copy	Not available [+]
Remote mirror(s)	repository.upenn.edu
Archive(s)	Not available [+]
Paywall(s)	Not available [+]
Export and share
BibTeX, CSV, RDF, JSON

Browse properties · List of conference papers

Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata is a 2010 conference paper written in English by Andrew G. West, Sampath Kannan, Insup Lee and published in EUROSEC.

[edit] 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.