Competition Produces Vandalism Detection For Wikis

marpot writes "Recently, the 1st International Competition on Wikipedia Vandalism Detection (PDF) finished: 9 groups (5 from the USA, 1 affiliated with Google) tried their best in detecting all vandalism cases from a large-scale evaluation corpus. The winning approach (PDF) detects 20% of all vandalism cases without misclassifying regular edits; moreover, it can be adjusted to detect 95% of the vandalism edits while misclassifying only 30% of all regular edits. Thus, by applying both settings, manual double-checking would only be required on 34% of all edits. Nothing is known, yet, whether the rule-based bots on Wikipedia can compete with this machine learning-based strategy. Anyway, there is still a lot potential for improvements since the top 2 detectors use entirely different detection paradigms: the first analyzes an edit's content, whereas the second (PDF) analyzes an edit's context using WikiTrust."

Comments

100% effective method

[robably]

Thus, by applying both settings, manual double-checking would only be required on 34% of all edits.

Or, you know, just keep applying the first setting that always correctly detects 20% of vandalism on the 80% that's left over, until there's nothing left. Problem solved.

Re:100% effective method

[Zocalo]

Otherwise known as Zeno's Dichotomy Paradox (often shorted to just "Xeno's Paradox", although he in fact suggested three).

I suppose I should now go and vandalise the article to keep in the spirit of things. Hang on, I'm half way there...