Automatically Assessing Wikipedia Article Quality by Exploiting Article-Editor Networks

Xinyi Li, Jintao Tang, Ting Wang, Zhunchen Luo, Maarten de Rijke

Abstract: 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.