In this work, we propose a novel data model that integrates and combines information on users belonging to one or more heterogeneous Online Social Networks (OSNs), together with the content that is generated, shared and used within the related environments, using an hypergraph-based approach. Then, we discuss how the most diffused centrality measures – that have been defined over the introduced model – can be efficiently applied for a number of data privacy issues, such as lurkers detection, especially in “interest-based” social networks. Some preliminary experiments using the Yelp dataset are finally presented.
Detection of Lurkers in Online Social Networks / Amato, Flora; Castiglione, Aniello; Moscato, Vincenzo; Picariello, Antonio; Sperli', Giancarlo. - 10581:(2017), pp. 1-15. (Intervento presentato al convegno 9th International Symposium on Cyberspace Safety and Security, CSS 2017 tenutosi a Xi'an, China nel 23-25 October, 2017) [10.1007/978-3-319-69471-9_1].
Detection of Lurkers in Online Social Networks
Flora Amato;Aniello Castiglione;Vincenzo Moscato;Antonio Picariello;Giancarlo Sperlì
2017
Abstract
In this work, we propose a novel data model that integrates and combines information on users belonging to one or more heterogeneous Online Social Networks (OSNs), together with the content that is generated, shared and used within the related environments, using an hypergraph-based approach. Then, we discuss how the most diffused centrality measures – that have been defined over the introduced model – can be efficiently applied for a number of data privacy issues, such as lurkers detection, especially in “interest-based” social networks. Some preliminary experiments using the Yelp dataset are finally presented.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
