Wikipedia Vandalism Corpus (Andrew G. West)
From WikiPapers
| Wikipedia Vandalism Corpus (Andrew G. West) (Alternative names for this dataset) | |
| Keyword(s) | vandalism |
| Size | 25.5 MB |
| Language(s) | English |
| Author(s) | Andrew G. West |
| License(s) | Unknown [+] |
| Website | http://www.andrew-g-west.com |
| Related material | |
| Related dataset(s) | Unknown [+] |
| Related tool(s) | Unknown [+] |
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Wikipedia Vandalism Corpus (Andrew G. West) is a corpus of 5.7 million automatically tagged and 5,000 manually-confirmed incidents of vandalism in English Wikipedia.
- ZIP: http://www.andrew-g-west.com/docs/wiki_corpus_2010_03_05.zip
- README: http://www.andrew-g-west.com/docs/wiki_corpus_readme.txt
Publications
| Title | Author(s) | Keyword(s) | Published in | Language | DateThis property is a special property in this wiki. | Abstract | R | C |
|---|---|---|---|---|---|---|---|---|
| Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata | Andrew G. West Sampath Kannan Insup Lee |
Wikipedia Spatio-temporal reputation Vandalism Collaboration software Content-based access control |
EUROSEC | English | April 2010 | 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. | 9 | 3 |
