The Internet is naturally a simple and immediate mean to retrieve information. However, not everything one can find is equally ac- curate and reliable. In this paper, we continue our line of research towards effective techniques for assessing the quality of online content. Focusing on the Wikipedia Medicinal Portal, in a previous work we implemented an automatic technique to assess the quality of each article and we com- pared our results to the classification of the articles given by the portal itself, obtaining quite different outcomes. Here, we present a lightweight instantiation of our methodology that reduces both redundant features and those not mentioned by the WikiProject guidelines. What we obtain is a fine-grained assessment and a better discrimination of the articles' quality, w.r.t. previous work. Our proposal could help to automatically evaluate the maturity of Wikipedia medical articles in an efficient way.

Improved maturity assessment of Wikipedia medical articles

Emanuel Marzini;Angelo Spognardi;Ilaria Matteucci;Paolo Mori;Marinella Petrocchi;Riccardo Conti
2014

Abstract

The Internet is naturally a simple and immediate mean to retrieve information. However, not everything one can find is equally ac- curate and reliable. In this paper, we continue our line of research towards effective techniques for assessing the quality of online content. Focusing on the Wikipedia Medicinal Portal, in a previous work we implemented an automatic technique to assess the quality of each article and we com- pared our results to the classification of the articles given by the portal itself, obtaining quite different outcomes. Here, we present a lightweight instantiation of our methodology that reduces both redundant features and those not mentioned by the WikiProject guidelines. What we obtain is a fine-grained assessment and a better discrimination of the articles' quality, w.r.t. previous work. Our proposal could help to automatically evaluate the maturity of Wikipedia medical articles in an efficient way.
2014
Istituto di informatica e telematica - IIT
Quality Assurance
Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/266207
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