Learning to Predict the Quality of Contributions to Wikipedia

Gregory Druck, Gerome Miklau, Andrew McCallum

Although some have argued that Wikipedia's open edit policy is one of the primary reasons for its success, it also raises concerns about quality --- vandalism, bias, and errors can be problems. Despite these challenges, Wikipedia articles are often (perhaps surprisingly) of high quality, which many attribute to both the dedicated Wikipedia community and ``good Samaritan" users. As Wikipedia continues to grow, however, it becomes more difficult for these users to keep up with the increasing number of articles and edits. This motivates the development of tools to assist users in creating and maintaining quality. In this paper, we propose metrics that quantify the quality of contributions to Wikipedia through implicit feedback from the community. We then learn discriminative probabilistic models that predict the quality of a new edit using features of the changes made, the author of the edit, and the article being edited. Through estimating parameters for these models, we also gain an understanding of factors that influence quality. We advocate using edit quality predictions and information gleaned from model analysis not to place restrictions on editing, but to instead alert users to potential quality problems, and to facilitate the development of additional incentives for contributors. We evaluate the edit quality prediction models on the Spanish Wikipedia. Experiments demonstrate that the models perform better when given access to content-based features of the edit, rather than only features of contributing user. This suggests that a user-based solution to the Wikipedia quality problem may not be sufficient.

Subjects: 1. Applications; 12. Machine Learning and Discovery

Submitted: May 5, 2008


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