Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals - Lab Report for Pan at Clef 2010

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Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals - Lab Report for Pan at Clef 2010
Authors
Santiago Moisés Mola-Velasco
Publication date
2010
Links
Original Preprint

Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals - Lab Report for Pan at Clef 2010 - scientific work related to Wikipedia quality published in 2010, written by Santiago Moisés Mola-Velasco.

Overview

Wikipedia is an online encyclopedia that anyone can edit. In this open model, some people edits with the intent of harming the integrity of Wikipedia. This is known as vandalism. Authors extend the framework presented in (Potthast, Stein, and Gerling, 2008) for Wikipedia vandalism detection. In this approach, several vandalism indicating features are extracted from edits in a vandalism corpus and are fed to a supervised learning algorithm. The best performing classifiers were LogitBoost and Random Forest. Authors classifier, a Random Forest, obtained an AUC of 0.92236, ranking in the first place of the PAN'10 Wikipedia vandalism detection task.

Embed

Wikipedia Quality

Mola-Velasco, Santiago Moisés. (2010). "[[Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals - Lab Report for Pan at Clef 2010]]".

English Wikipedia

{{cite journal |last1=Mola-Velasco |first1=Santiago Moisés |title=Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals - Lab Report for Pan at Clef 2010 |date=2010 |url=https://wikipediaquality.com/wiki/Wikipedia_Vandalism_Detection_Through_Machine_Learning:_Feature_Review_and_New_Proposals_-_Lab_Report_for_Pan_at_Clef_2010}}

HTML

Mola-Velasco, Santiago Moisés. (2010). &quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Vandalism_Detection_Through_Machine_Learning:_Feature_Review_and_New_Proposals_-_Lab_Report_for_Pan_at_Clef_2010">Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals - Lab Report for Pan at Clef 2010</a>&quot;.