Using Language Models to Detect Wikipedia Vandalism

This paper explores a statistical language modeling approach for detecting Wikipedia vandalism. Wikipedia is a popular and influential collaborative information system. The collaborative nature of authoring, as well as the high visibility of its content, have exposed Wikipedia articles to vandalism, defined as malicious editing intended to compromise the integrity of the content of articles. Extensive manual efforts are being made to combat vandalism and an automated approach to alleviate the laborious process is essential. This paper offers first a categorization of Wikipedia vandalism types and identifies technical challenges associated with detecting each category. In addition, this paper builds statistical language models, constructing distributions of words from the revision history of Wikipedia articles. As vandalism often involves the use of unexpected words to draw attention, the fitness (or lack thereof) of a new edit when compared with language models built from previous versions may well indicate that an edit is a vandalism instance. The Wikipedia domain with its revision histories offers a novel context in which to explore the potential of language models in characterizing author intention. As the experimental results presented in the paper demonstrate, these models hold promise for vandalism detection.