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

Machine learning - right

[Animats]

Wikipedia already has programs which detect most of the blatant vandalism. Page blanking and big deletions are caught immediately. Deletions that delete references generate warnings. Incoming text that duplicates other content on the Web is caught. That gets rid of most of the blatant vandalism. It's not a serious problem on Wikipedia.

The current headaches are mostly advertising, fancruft, and pushing of some political point of view. That's hard to deal with using what is, after all, a rather dumb machine learning algorithm that has no model of the content or subject matter.