Éditeur(s) : HAL CCSD
Résumé : International audience
EmailPrintRequest PermissionsThe emergence of new communication means such as online newspapers and exchange and sharing websites allow us to go further in the understanding of diffusion phenomena. Indeed, these public discussion areas are now firmly established in our societies and are known to be strong sensors of both human behaviors and collective feelings. In this paper we focus on diffusion phenomena that occur on Twitter and we propose a set of measures that aims to characterize globally and locally the processes. Our objective is to identify what are the conditions in which a person decides to forward the information. Our measures have been used to study two events occurred in January 2015: the presentation of Microsoft HoloLens, a new augmented reality headset, and the political election in Greece. The results obtained show a strong heterogeneity in the individual behaviours involved in the diffusion process. Our approach has been implemented into a graphical tool which is able to conduct real time analysis on any kind of topics occurring on Twitter.
IEEE International Conference on Research Challenges in Information Science (RCIS)
Athens, Greece

hal-01312382
https://hal.univ-antilles.fr/hal-01312382
DOI : 10.1109/RCIS.2015.7128894

Éditeur(s) : HAL CCSD Springer
Résumé : International audience
Numerous methods have been proposed in order to perform clustering from social networks. While significant works have been carried out on the design of new approaches, able to search for various kinds of clusters, a major challenge concerns the scalability of these approaches. Indeed, given the mass of data that can now be collected from online social networks, particularly from social platforms, it is important to have efficient methods for exploring and analyzing these very large amount of data. One of the recent social network clustering approaches is the extraction of conceptual links, a new approach that performs link clustering by exploiting both the structure of the network and attributes of nodes to identify strong links between groups of nodes in which nodes share common attributes. In this paper, we focus on the optimization of the search for conceptual links. In particular, we propose PALM, a parallel algorithm that aims to improve the efficiency of the extraction by simultaneously exploring several areas of the search space. For this purpose, we begin by demonstrating that the solution space forms a concept lattice. Then, we propose an approach that explores in parallel the branches of the lattice while reducing the search space based on various properties of conceptual links. We demonstrate the efficiency of the algorithm by comparing the performances with the original extraction approach. The results obtained show a significant gain on the computation time.
28th International Conference on Database and Expert Systems Applications (DEXA)
Lyon, France

hal-01637554
https://hal.archives-ouvertes.fr/hal-01637554
DOI : 10.1007/978-3-319-64471-4_21