Quality of Wikipedia Articles: Analyzing Features and Building a Ground Truth for Supervised Classification

Elias Bassani, Marco Viviani


Wikipedia is nowadays one of the biggest online resources on which users rely as a source of information. The amount of collaboratively generated content that is sent to the online encyclopedia every day can let to the possible creation of low-quality articles (and, consequently, misinformation) if not properly monitored and revised. For this reason, in this paper, the problem of automatically assessing the quality of Wikipedia articles is considered. In particular, the focus is (i) on the analysis of groups of hand-crafted features that can be employed by supervised machine learning techniques to classify Wikipedia articles on qualitative bases, and (ii) on the analysis of some issues behind the construction of a suitable ground truth. Evaluations are performed, on the analyzed features and on a specifically built labeled dataset, by implementing different supervised classifiers based on distinct machine learning algorithms, which produced promising results.


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