The illiterate editor: Metadata-driven revert detection in wikipedia

Jeffrey Segall, Rachel Greenstadt

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    As the community depends more heavily on Wikipedia as a source of reliable information, the ability to quickly detect and remove detrimental information becomes increasingly important. The longer incorrect or malicious information lingers in a source perceived as reputable, the more likely that information will be accepted as correct and the greater the loss to source reputation. We present The Illiterate Edi- Tor (IllEdit), a content-agnostic, metadata-driven classica- Tion approach to Wikipedia revert detection. Our primary contribution is in building a metadata-based feature set for detecting edit quality, which is then fed into a Support Vec- Tor Machine for edit classication. By analyzing edit histo- ries, the IllEdit system builds a prole of user behavior, es- Timates expertise and spheres of knowledge, and determines whether or not a given edit is likely to be eventually re- verted. The success of the system in revert detection (0.844 F-measure) as well as its disjoint feature set as compared to existing, content-analyzing vandalism detection systems, shows promise in the synergistic usage of IllEdit for increas- ing the reliability of community information.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 9th International Symposium on Open Collaboration, WikiSym + OpenSym 2013
    DOIs
    StatePublished - 2013
    Event9th International Symposium on Open Collaboration, WikiSym + OpenSym 2013 - Hong Kong, China
    Duration: Aug 5 2013Aug 7 2013

    Publication series

    NameProceedings of the 9th International Symposium on Open Collaboration, WikiSym + OpenSym 2013

    Other

    Other9th International Symposium on Open Collaboration, WikiSym + OpenSym 2013
    CountryChina
    CityHong Kong
    Period8/5/138/7/13

    ASJC Scopus subject areas

    • Software

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