Automatic Vandalism Detection in Wikipedia
Authors | Martin Potthast Benno Stein Robert Gerling |
|---|---|
Publication date | 2008 |
DOI | 10.1007/978-3-540-78646-7_75 |
Links | Original |
Automatic Vandalism Detection in Wikipedia - scientific work related to Wikipedia quality published in 2008, written by Martin Potthast, Benno Stein and Robert Gerling.
Overview
Authors present results of a new approach to detect destructive article revisions, so-called vandalism, inWikipedia. 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 authors discuss the characteristics of vandalism as humans recognize it and develop features to render vandalism detection as a machine learning task. Authors compiled a large number of vandalism edits in a corpus, which allows for the comparison of existing and new detection approaches. Using logistic regression authors achieve 83% precision at 77% recall with model. Compared to the rule-based methods that are currently applied in Wikipedia, approach increases the F-Measure performance by 49% while being faster at the same time.
Embed
Wikipedia Quality
Potthast, Martin; Stein, Benno; Gerling, Robert. (2008). "[[Automatic Vandalism Detection in Wikipedia]]". Springer Berlin Heidelberg. DOI: 10.1007/978-3-540-78646-7_75.
English Wikipedia
{{cite journal |last1=Potthast |first1=Martin |last2=Stein |first2=Benno |last3=Gerling |first3=Robert |title=Automatic Vandalism Detection in Wikipedia |date=2008 |doi=10.1007/978-3-540-78646-7_75 |url=https://wikipediaquality.com/wiki/Automatic_Vandalism_Detection_in_Wikipedia |journal=Springer Berlin Heidelberg}}
HTML
Potthast, Martin; Stein, Benno; Gerling, Robert. (2008). "<a href="https://wikipediaquality.com/wiki/Automatic_Vandalism_Detection_in_Wikipedia">Automatic Vandalism Detection in Wikipedia</a>". Springer Berlin Heidelberg. DOI: 10.1007/978-3-540-78646-7_75.
