Authors: Sara Javanmardi, David W. McDonald and Cristina V. Lopes.

Abstract: User generated content (UGC) constitutes a significant fraction of the Web. However, some wiiki–based sites, such as Wikipedia, are so popular that they have become a favorite target of spammers and other vandals. In such popular sites, human vigilance is not enough to combat vandalism, and tools that detect possible vandalism and poor-quality contributions become a necessity. The application of machine learning techniques holds promise for developing efficient online algorithms for better tools to assist users in vandalism detection. We describe an efficient and accurate classifier that performs vandalism detection in UGC sites. We show the results of our classifier in the PAN Wikipedia dataset. We explore the effectiveness of a combination of 66 individual features that produce an AUC of 0.9553 on a test dataset – the best result to our knowledge. Using Lasso optimization we then reduce our feature–rich model to a much smaller and more efficient model of 28 features that performs almost as well – the drop in AUC being only 0.005. We describe how this approach can be generalized to other user generated content systems and describe several applications of this classifier to help users identify potential vandalism.

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