Semantic Social Network Analysis: A Concrete Case

Semantic Social Network Analysis: A Concrete Case

Guillaume Erétéo (Orange Labs, France), Freddy Limpens (EDELWEISS, INRIA Sophia-Antipolis, France), Fabien Gandon (EDELWEISS, INRIA Sophia-Antipolis, France), Olivier Corby (EDELWEISS, INRIA Sophia-Antipolis, France), Michel Buffa (University of Nice Sophia Antipolis, France), Mylène Leitzelman (University of Nice Sophia Antipolis, France) and Peter Sander (University of Nice Sophia Antipolis, France)
DOI: 10.4018/978-1-60960-040-2.ch007
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In this chapter we present our approach to analyzing such semantic social networks and capturing collective intelligence from collaborative interactions to challenge requirements of Enterprise 2.0. Our tools and models have been tested on an anonymized dataset from, one of the biggest French social web sites centered on multimedia sharing. This dataset contains over 60,000 users, around half a million declared relationships of three types, and millions of interactions (messages, comments on resources, etc.). We show that the enriched semantic web framework is particularly well-suited for representing online social networks, for identifying their key features and for predicting their evolution. Organizing huge quantity of socially produced information is necessary for a future acceptance of social applications in corporate contexts.
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The web is now a major medium of communication in our society and, as the web is becoming more and more social, a huge amount of content is now collectively produced and widely shared online. Even early on, the social interactions on the web highlighted a social network structure (Wellman 1996), a phenomena dramatically amplified by web 2.0 which follows inexorably Metcalfe’s Law1 (Hendler and Golbeck 2008). Individuals and their activities are at the core of the web, along with all the easily-available social software and services, e.g., Delicious, Flickr, Linkedin, Facebook. After the explosion of the “web of content” at the end of 90’s, we are witnessing the outburst of the “web of people”. Taken together, “we use people to find content whereas we use content to find people” (Morville 2004), and we need new means to investigate the relationship between people and content.

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