Réseau social

En sciences humaines et sociales, l'expression réseau social désigne un agencement de liens entre des individus ou des organisations, constituant un groupement qui a un sens : la famille, les collègues, un groupe d'amis, une communauté, etc. L'anthropologue australien John Arundel Barnes a introduit l'expression en 1954. L'analyse des réseaux sociaux est devenue une spécialité universitaire dans le champ de la sociologie, se fondant sur la théorie des réseaux et l'usage des graphes. L’expression « réseau social » dans l'usage habituel renvoie généralement à celle de « médias sociaux », qui recouvre les différentes activités qui intègrent technologie, interaction sociale entre individus ou groupes d'individus, et la création de contenu. Andreas Kaplan et Michael Haenlein les définissent comme . Applications en sciences sociales Analyse des réseaux sociaux Les études sur l'urbanisation de l'École de Manchester autour de Max Gluckman, réalisées principalement en Zambie du

A JavaScript Software Development Kit for uJoin

Christophe Clément Tafani-Dereeper

uJoin is a social network developed by a Fribourg start-up aiming to connect people through real-life activities. uJoiners (members of the community) fill in their hobbies, and relevant activities are brought to them so they can take part to fun events and meet people they share common tastes with. The goal of this project is to create a JavaScript SDK to provide developers with a simple and elegant way to handle the communication with uJoin API.

2014

Social Network Analysis for Automatic Role Recognition

Sarah Favre

The computing community has shown a significant interest for the analysis of social interactions in the last decade. Different aspects of social interactions have been studied such as dominance, emotions, conflicts, etc. However, the recognition of roles has been neglected whereas these are a key aspect of social interactions. In fact, sociologists have shown not only that people play roles each time they interact, but also that roles shape behavior and expectations of interacting participants. The aim of this thesis is to fill this gap by investigating the problem of automatic role recognition in a wide range of interaction settings, including production environments, e.g. news and talk-shows, and spontaneous exchanges, e.g. meetings. The proposed role recognition approach includes two main steps. The first step aims at representing the individuals involved in an interaction with feature vectors accounting for their relationships with others. This step includes three main stages, namely segmentation of audio into turns (i.e. time intervals during which only one person talks), conversion of the sequence of turns into a social network, and use of the social network as a tool to extract features for each person. The second step uses machine learning methods to map the feature vectors into roles. The experiments have been carried out over roughly 90 hours of material. This is not only one of the largest databases ever used in literature on role recognition, but also the only one, to the best of our knowledge, including different interaction settings. In the experiments, the accuracy of the percentage of data correctly labeled in terms of roles is roughly 80% in production environments and 70% in spontaneous exchanges (lexical features have been added in the latter case). The importance of roles has been assessed in an application scenario as well. In particular, the thesis shows that roles help to segment talk-shows into stories, i.e. time intervals during which a single topic is discussed, with satisfactory performance. The main contributions of this thesis are as follows: To the best of our knowledge, this is the first work where social network analysis is applied to automatic analysis of conversation recordings. This thesis provides the first quantitative measure of how much roles constrain conversations, and a large corpus of recordings annotated in terms of roles. The results of this work have been published in one journal paper, and in five conference articles.

EPFL2011

Social Network Analysis for Automatic Role Recognition

Sarah Favre

EPFL2011

Real Time Discussions ("chats") for a Social Network

A JavaScript Software Development Kit for uJoin

Social Network Analysis for Automatic Role Recognition

Social Network Analysis for Automatic Role Recognition

Real Time Discussions ("chats") for a Social Network

A JavaScript Software Development Kit for uJoin