We consider the problem of automatically assessing Wikipedia article quality. We develop several models to rank articles by using the editing relations between articles and editors. First, we create a basic model by modeling the article-editor network. Then we design measures of an editor’s contribution and build weighted models that improve the ranking performance. Finally, we use a combination of featured article information and the weighted models to obtain the best performance. We find that using manual evaluation to assist automatic evaluation is a viable solution for the article quality assessment task on Wikipedia.
CITATION STYLE
Li, X., Tang, J., Wang, T., Luo, Z., & de Rijke, M. (2015). Automatically assessing wikipedia article quality by exploiting article–editor networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9022, pp. 574–580). Springer Verlag. https://doi.org/10.1007/978-3-319-16354-3_64
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