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ZOT! To W IKIPEDI A V ANDALIS M PAN-10 @ CLEF 2010 Shared Task #2 Rebecca Maessen and James White

ZOT! To W IKIPEDI A V ANDALIS M PAN-10 @ CLEF 2010 Shared Task #2 Rebecca Maessen and James White. Data Set Low number of vandalism edits in PAN@CLEF data gave little information on patterns distinguishing vandalism from regular edits. Added 5,276 manually classified vandalism edits from

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ZOT! To W IKIPEDI A V ANDALIS M PAN-10 @ CLEF 2010 Shared Task #2 Rebecca Maessen and James White

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  1. ZOT! To WIKIPEDIAVANDALISMPAN-10 @ CLEF 2010 Shared Task #2Rebecca Maessen and James White Data Set Low number of vandalism edits in PAN@CLEF data gave little information on patterns distinguishing vandalism from regular edits. Added 5,276 manually classified vandalism edits from West et all research Combined data set 20,283 edits • 31% “ill-intentioned” edits compared to 6% before

  2. Algorithms • Logistic regression on word vector and W-J48 decision trees on metadata features • Logistic regression and W-J48 decision trees after combining the features • Ensemble methods: Bagging, Boosting and Random Forest

  3. Combining word vector and metadata attributes

  4. Ensemble Methods

  5. 10-val cross validation on bagging with W-J48graft

  6. ROC and AUC plot

  7. Conclusion • Results better than expected beforehand • Achieve an f-measure that is just as good as results from previous works • Decision trees is a favorable approach to the Wikipedia vandalism problem • Top down feature analysis and statistical information from word vectors are both relevant to classifying vandalism

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