Relationships among actors in traditional social network analysis are modelled as a function of the quantity of relations (co-authorships, business relations, friendship, etc.). In contrast, within a business, social or research community, network analysts are interested in the communicative content exchanged by the community members, not merely in the number of relationships. In order to meet this need, this paper presents a novel social network model, in which the actors are not simply represented through the intensity of their mutual relationships, but also through the analysis and evolution of their shared interests. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular topics.

Content-Based Social Network Analysis / Velardi, Paola; Navigli, Roberto; A., Cucchiarelli; M., Curzi. - STAMPA. - 178(2008), pp. 765-766. ((Intervento presentato al convegno 18th European Conference on Artificial Intelligence (ECAI) tenutosi a Patras, Greece nel July 21-25th, 2008 [10.3233/978-1-58603-891-5-765].

Content-Based Social Network Analysis

VELARDI, Paola;NAVIGLI, ROBERTO;
2008

Abstract

Relationships among actors in traditional social network analysis are modelled as a function of the quantity of relations (co-authorships, business relations, friendship, etc.). In contrast, within a business, social or research community, network analysts are interested in the communicative content exchanged by the community members, not merely in the number of relationships. In order to meet this need, this paper presents a novel social network model, in which the actors are not simply represented through the intensity of their mutual relationships, but also through the analysis and evolution of their shared interests. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular topics.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/224516
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