Quality flaw prediction in Spanish Wikipedia: A case of study with verifiability flaws |
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| Authors: | Edgardo Ferretti Leticia Cagnina Viviana Paiz Sebastián Delle Donne Rodrigo Zacagnini Marcelo Errecalde |
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| Institution: | 1. Departamento de Informática, Universidad Nacional de San Luis (UNSL), Ejército de los Andes 950, San Luis, Argentina;2. Laboratorio de Investigación y, Desarrollo en Inteligencia Computacional (UNSL), Argentina;3. Consejo Nacional de Investigaciones, Científicas y Técnicas (CONICET), Argentina |
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| Abstract: | In this work, we present the first quality flaw prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove). Based on the underlying characteristics of each flaw, different state-of-the-art approaches were evaluated. For articles not citing any references, a well-established rule-based approach was evaluated and interesting findings show that some of them suffer from Refimprove flaw instead. Likewise, for articles that need additional citations for verification, the well-known PU learning and one-class classification approaches were evaluated. Besides, new methods were compared and a new feature was also proposed to model this latter flaw. The results showed that new methods such as under-bagged decision trees with sum or majority voting rules, biased-SVM, and centroid-based balanced SVM, perform best in comparison with the ones previously published. |
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| Keywords: | Information quality Quality flaw prediction Semi-supervised learning Supervised learning Wikipedia |
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