Empowering Collaborative Intelligence by the use of User-centered Social Network Aggregation

Abstract : Online social networks (OSNs) such as Facebook, Twitter, LinkedIn, Google+, have become extremely popular and ubiquitous today. Users are actively connected to these services for creating and sharing contents and events with others, and, in some cases, this activity takes place in the scope of groups of interests. Therefore, from amongst the morass of data generated every day by users, a part of information may match the interests of certain groups. In practice, members are not all linked to each other via each OSN they are connected to. It is also not realistic to assume that each member can manually explore all others' social profiles to reach the information that may be relevant to their interests. Thus, there is a need for aggregating members' social streams on a single information support to collect relevant information, and, consequently, to promote collaborative knowledge-sharing. However, the disconnected nature of today social websites prevents a straightforward aggregation process. An efficient automated aggregation model is needed. We present, in this paper, the idea of empowering collaborative intelligence by the use of a user-centered approach for OSN aggregation. We illustrate the approach by a first experience to evaluate its impact on users information sharing and enrichment capabilities.
Liste complète des métadonnées

Cited literature [17 references]  Display  Hide  Download

Contributor : Pierre Morizet-Mahoudeaux <>
Submitted on : Friday, December 6, 2013 - 4:18:40 PM
Last modification on : Thursday, February 7, 2019 - 5:50:26 PM
Document(s) archivé(s) le : Saturday, April 8, 2017 - 5:17:05 AM


Files produced by the author(s)




Xuan-Truong Vu, Pierre Morizet-Mahoudeaux, Marie-Hélène Abel. Empowering Collaborative Intelligence by the use of User-centered Social Network Aggregation. Vijay, Raghavan and Hu, Xiaolin and Liau, Churn-Jung and Treur, Jan. 2013 IEEE/WIC/ACM International Conference on Web Intelligence WI 2013, Nov 2013, Atlanta, United States. IEEE Computer Society, pp.425-430, 2013, 〈10.1109/WI-IAT.2013.60〉. 〈hal-00915163〉



Record views


Files downloads