Abstract
We present results of a new approach to detect destructive article revisions, so-called vandalism, in Wikipedia. Vandalism detection is a one-class classification problem, where vandalism edits are the target to be identified among all revisions. Interestingly, vandalism detection has not been addressed in the Information Retrieval literature by now. In this paper we discuss the characteristics of vandalism as humans recognize it and develop features to render vandalism detection as a machine learning task. We compiled a large number of vandalism edits in a corpus, which allows for the comparison of existing and new detection approaches. Using logistic regression we achieve 83% precision at 77% recall with our model. Compared to the rule-based methods that are currently applied in Wikipedia, our approach increases the F-Measure performance by 49% while being faster at the same time.
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Potthast, M., Stein, B., Gerling, R. (2008). Automatic Vandalism Detection in Wikipedia. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_75
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DOI: https://doi.org/10.1007/978-3-540-78646-7_75
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