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Submission Summary: 0 pending, 2 declined, 3 accepted (5 total, 60.00% accepted)

Wikipedia

Submission + - Competition on Detecting Vandalism in Wikis (webis.de)

marpot writes: Recently, the 1st International Competition on Wikipedia Vandalism Detection 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 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 analyzes an edit's context using WikiTrust.
Announcements

Submission + - Developing a Vandalism Detector for Wikipedia (webis.de)

marpot writes: The title really says it all. In an effort to assist Wikipedia's editors in their struggle to keep articles clean, we conduct a public lab on vandalism detection. Goal is the development of a practical vandalism detector that is capable of telling apart ill-intentioned edits from well-intentioned edits. Such a tool, which will work not unlike a spam detector, will release the crowd's workforce currently occupied with manual and semi-automatic edit filtering. The performance of submitted detectors is evaluated based on a large collection of human-annotated edits, which has been crowdsourced using Amazon's Mechanical Turk. Everyone is welcome to participate.
Announcements

Submission + - Competition on Plagiarism Detection (webis.de)

marpot writes: "

Does your school/university check your homeworks/theses for plagiarism? Nowadays probably yes, but are they doing it properly? Little is known about plagiarism detection accuracy, which is why we conduct a competition on plagiarism detection, sponsored by Yahoo! We have set up a corpus of artificial plagiarism which contains plagiarism with varying degrees of obfuscation, and translation plagiarism from Spanish or German source documents. A random plagiarist was employed who attempts to obfuscate his plagiarism with random sequences of text operations, e.g., shuffling, deleting, inserting, or replacing a word. Translated plagiarism is created using machine translation.

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