Automatic quality assessment of content created collaboratively by web communities: a case study of wikipedia

  • Authors:

  • Daniel Hasan Dalip;Marcos André Gonçalves;Marco Cristo;Pável Calado

  • Affiliations:

  • Federal University of Minas Gerais, Belo Horizonte, Brazil;Federal University of Minas Gerais, Belo Horizonte, Brazil;FUCAPI - Analysis, Research and Tech. Innovation Center, Manaus, Brazil;Instituto Superior Técnico/INESC-ID, Porto Salvo, Portugal

  • Venue:
  • Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
  • Year:
  • 2009

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Abstract

The old dream of a universal repository containing all the human knowledge and culture is becoming possible through the Internet and the Web. Moreover, this is happening with the direct collaborative, participation of people. Wikipedia is a great example. It is an enormous repository of information with free access and edition, created by the community in a collaborative manner. However, this large amount of information, made available democratically and virtually without any control, raises questions about its relative quality. In this work we explore a significant number of quality indicators, some of them proposed by us and used here for the first time, and study their capability to assess the quality of Wikipedia articles. Furthermore, we explore machine learning techniques to combine these quality indicators into one single assessment judgment. Through experiments, we show that the most important quality indicators are the easiest ones to extract, namely, textual features related to length, structure and style. We were also able to determine which indicators did not contribute significantly to the quality assessment. These were, coincidentally, the most complex features, such as those based on link analysis. Finally, we compare our combination method with state-of-the-art solution and show significant improvements in terms of effective quality prediction.