This paper presents a novel model for social network analysis in which, rather than analyzing the quantity of relationships (co-authorships, business relations, friendship, etc.), we analyze their communicative content. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular themes. The social analyst is then able to perform a study of the network evolution in terms of the relevant themes of collaboration, the detection of new concepts gaining popularity, and the existence of popular themes that could benefit from better cooperation. The methodology is experimented in the domain of a Network of Excellence on enterprise interoperability, INTEROP. © 2008 IEEE.
A new content-based model for social network analysis / Velardi, Paola; Navigli, Roberto; Alessandro, Cucchiarelli; Fulvio, D'Antonio. - STAMPA. - (2008), pp. 18-25. (Intervento presentato al convegno 2nd Annual IEEE International Conference on Semantic Computing, ICSC 2008 tenutosi a Santa Clara, CA nel 4 August 2008 through 7 August 2008) [10.1109/icsc.2008.30].
A new content-based model for social network analysis
VELARDI, Paola;NAVIGLI, ROBERTO;
2008
Abstract
This paper presents a novel model for social network analysis in which, rather than analyzing the quantity of relationships (co-authorships, business relations, friendship, etc.), we analyze their communicative content. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular themes. The social analyst is then able to perform a study of the network evolution in terms of the relevant themes of collaboration, the detection of new concepts gaining popularity, and the existence of popular themes that could benefit from better cooperation. The methodology is experimented in the domain of a Network of Excellence on enterprise interoperability, INTEROP. © 2008 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
