Topical tags vs . non - topical tags : towards a bipartite classification ?

Abstract : In this paper we investigate whether it is possible to create a computational approach that allows us to distinguish topical tags (i. e. , talking about the topic of a resource) and non-topical tags (i. e. , describing aspects of a resource that are not related to its topic) in folksonomies , in a way that correlates with humans. Towards this goal , we collected 21M tags (1. 2M unique terms) from Delicious and we developed an unsupervised statistical algorithm that classifies such tags by applying a word space model adapted to the folksonomy space. Our algorithm analyses the co-occurrence network of tags to a target tag and exploits graph-based metrics for their classification. We validated its outcomes against a reference classification made by humans on a limited number of terms in three separate tests. The analysis of the outcomes of our algorithm shows , in some cases , a consistent disagreement among humans and between humans and our algorithm about what constitutes a topical tag , and suggests the rise of a new category of overly generic tags (i. e. , umbrella tags) .
Liste complète des métadonnées
Contributeur : Valerio Basile <>
Soumis le : dimanche 15 novembre 2015 - 09:27:47
Dernière modification le : lundi 9 octobre 2017 - 13:18:03
Document(s) archivé(s) le : vendredi 28 avril 2017 - 18:14:15


Fichiers produits par l'(les) auteur(s)




Valerio Basile, Silvio Peroni, Fabio Tamburini, Fabio Vitali. Topical tags vs . non - topical tags : towards a bipartite classification ?. Journal of Information Science, SAGE Publications, 2014, 〈10.1177/0165551515585283〉. 〈hal-01228923〉



Consultations de la notice


Téléchargements de fichiers