Wikipedia vandalism detection

Abstract

Wikipedia is an online encyclopedia that anyone can edit. The fact that there are almost no restrictions to contributing content is at the core of its success. However, it also attracts pranksters, lobbysts, spammers and other people who degradatesWikipedia's contents. One of the most frequent kind of damage is vandalism, which is defined as any bad faith attempt to damage Wikipedia's integrity. For some years, the Wikipedia community has been fighting vandalism using automatic detection systems. In this work, we develop one of such systems, which won the 1st International Competition on Wikipedia Vandalism Detection. This system consists of a feature set exploiting textual content of Wikipedia articles. We performed a study of different supervised classification algorithms for this task, concluding that ensemble methods such as Random Forest and LogitBoost are clearly superior. After that, we combine this system with two other leading approaches based on different kind of features: metadata analysis and reputation. This joint system obtains one of the best results reported in the literature. We also conclude that our approach is mostly language independent, so we can adapt it to languages other than English with minor changes.