Ideentifying featured articles in Spanish Wikipedia

Abstract

Information Quality assessment in Wikipedia has become an ever-growing research line in the last years. However, few e orts have been accomplished in Spanish Wikipedia, despite being Spanish, one of the most spoken languages in the world by native speakers. In this respect, we present the rst study to automatically assess information quality in Spanish Wikipedia, where Featured Articles identi cation is evaluated as a binary classi cation task. Two popular classi cation approaches like Naive Bayes and Support Vector Machine (SVM) are evaluated with di erent document representations and vocabulary sizes. The obtained results show that FA identi cation can be performed with an F1 score of 0.81, when SVM is used as classi cation algorithm and documents are represented with a binary codi cation of the bag-of-words model with reduced vocabulary.