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Predicting Low-Quality Wikipedia Articles Using User’s Judgements

  • Ning Zhang
  • Lingyun Ruan
  • Luo Si
Chapter
Part of the Computational Social Sciences book series (CSS)

Abstract

Wikipedia has become the most popular on-line encyclopedia. Millions of users rely on it to obtain desired knowledge and thus it becomes important and practical to model the quality of Wikipedia articles and to have inferior contents which bother readers or even mislead readers to be predicted. While identifying low-quality articles with manual efforts is a possible solution, it costs too much manpower and is too time-consuming. In this paper, we utilize article ratings from Wikipedia users for the first time to assess article quality. We define “low-quality” based on those ratings and design automatic methods to identify potential low-quality articles. More specifically, we formulate the problem as a set of binary classification problems and label articles according to whether they are “low-quality”. We compare two baseline algorithms and Logistic Regression algorithm, and the results indicate that it is promising to design effective and efficient automatic solutions for the task. We believe that our work is important for ensuring the quality of Wikipedia, as well as other knowledge markets.

Keywords

WikipediaArticle qualityUser ratingPrediction 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer SciencePurdue UniversityWest LafayetteUSA
  2. 2.GoogleMountain ViewUSA
  3. 3.Department of Computer SciencePurdue UniversityWest LafayetteUSA

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