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Automatically Assessing Wikipedia Article Quality by Exploiting Article–Editor Networks

  • Xinyi Li
  • Jintao Tang
  • Ting Wang
  • Zhunchen Luo
  • Maarten de Rijke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)

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.

Keywords

Weighted Model Ranking Performance Feature Article Editing Action Simple Weighted 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xinyi Li
    • 1
    • 2
  • Jintao Tang
    • 1
  • Ting Wang
    • 1
  • Zhunchen Luo
    • 3
  • Maarten de Rijke
    • 2
  1. 1.National University of Defense TechnologyChangshaChina
  2. 2.University of AmsterdamAmsterdamThe Netherlands
  3. 3.China Defense Science and Technology Information CenterBeijingChina

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