Adv Data Anal Classif DOI 10.1007/s11634-012-0122-2 REGULAR ARTICLE Non parametric statistical models for on-line text classification Paola Cerchiello · Paolo Giudici Received: 29 December 2011 / Revised: 28 June 2012 / Accepted: 1 August 2012 © Springer-Verlag Berlin Heidelberg 2012 Abstract Social media, such as blogs and on-line forums, contain a huge amount of information that is typically unorganized and fragmented. An important issue, that has been raising importance so far, is to classify on-line texts in order to detect possible anomalies. For example on-line texts representing consumer opinions can be, not only very precious and profitable for companies, but can also represent a serious damage if they are negative or faked. In this contribution we present a novel statistical methodol- ogy rooted in the context of classical text classification, in order to address such issues. In the literature, several classifiers have been proposed, among them support vector machine and naive Bayes classifiers. These approaches are not effective when coping with the problem of classifying texts belonging to an unknown author. To this aim, we propose to employ a new method, based on the combination of classification trees with non parametric approaches, such as Kruskal–Wallis and Brunner–Dette–Munk test. The main application of what we propose is the capability to classify an author as a new one, that is potentially trustable, or as an old one, that is potentially faked. Keywords Non parametric statistical models · Kruskal–Wallis test · Brunner–Dette–Munk test · Text analysis · Opinion spam detection Mathematics Subject Classification 62G10 · 62H30 P. Cerchiello (B ) · P. Giudici University of Pavia, Pavia, Italy e-mail: paola.cerchiello@unipv.it P. Giudici e-mail: giudicio@unipv.it 123