Résumé: Online social networks play a major role in the spread of information at very large scale. A lot of efforts have been made in order to understand this phenomenon, ranging from popular topic detection to information diffusion modeling, including influential spreaders identification. In this article, we present a survey of representative methods dealing with these issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around information diffusion in social networks. This survey is intended to help researchers in quickly understanding existing works and possible improvements to bring.

Résumé: This paper describes SONDY, a tool for analysis of trends and dynamics in online social network data. SONDY addresses two audiences: (i) end-users who want to explore social activity and (ii) researchers who want to experiment and compare mining techniques on social data. SONDY helps end-users like media analysts or journalists understand social network users interests and activity by providing emerging topics and events detection as well as network analysis functionalities. To this end, the application proposes visualizations such as interactive time-lines that summarize information and colored user graphs that reflect the structure of the network. SONDY also provides researchers an easy way to compare and evaluate recent techniques to mine social data, implement new algorithms and extend the application without being concerned with how to make it accessible.

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Résumé: Ce papier décrit la plateforme SONDY qui permet l'analyse et la fouille de données issues de réseaux sociaux en ligne. La plateforme permet d'explorer et de visualiser l'évolution des thématiques populaires et la structure du réseau social de manière interactive. Elle permet également de comparer les méthodes utilisées et d'en intégrer de nouvelles. La démonstration consistera à expérimenter tous les services de SONDY sur des données issues de Twitter.

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Résumé: Online discussions became increasingly widespread with the Web 2.0: no matter the distance, whether you know the person or not, you can discuss and exchange ideas with people all over the world through forums, blogs, and newsgroups. The news websites have extensively used forums in order to encourage the reader being a real participant in the information media. This paper aims at automatically extracting the celebrities from such discussions. We propose certain meta-criteria and we provide an evaluation on a dataset of 35,175 posts written by 14,443 users. The results show that one of the proposed meta-criteria succeeds in extracting celebrities and allows for further improvements.

Résumé: Statistical comparisons for topological equivalence of proximity measures