Online community management as social network design: testing for the signature of management activities in online communities

Online communities are used across several fields of human activities, as environments for large-scale collaboration. Most successful ones employ professionals, sometimes called “community managers” or “moderators”, for tasks including onboarding new participants, mediating conflict, and policing unwanted behaviour. Network scientists routinely model interaction across participants in online communities as social networks. We interpret the activity of community managers as (social) network design: they take action oriented at shaping the network of interactions in a way conducive to their community’s goals. It follows that, if such action is successful, we should be able to detect its signature in the network itself. Growing networks where links are allocated by a preferential attachment mechanism are known to converge to networks displaying a power law degree distribution. Growth and preferential attachment are both reasonable first-approximation assumptions to describe interaction networks in online communities. Our main hypothesis is that managed online communities are characterised by in-degree distributions that deviate from the power law form; such deviation constitutes the signature of successful community management. Our secondary hypothesis is that said deviation happens in a predictable way, once community management practices are accounted for. If true, these hypotheses would give us a simple test for the effectiveness of community management practices. We investigate the issue using (1) empirical data on three small online communities and (2) a computer model that simulates a widely used community management activity called onboarding. We find that onboarding produces in-degree distributions that systematically deviate from power law behaviour for low-values of the in-degree; we then explore the implications and possible applications of the finding.ERC Horizon H202

Cohérence d'évènements médiatiques

Nous proposons une méthode pour visualiser et analyser les évènements médiatiques à partir des sujets d'actualité des journaux télévisés de plusieurs chaînes annotées avec des descripteurs textuels. Nous présentons une interface d'exploration basée sur un modèle de graphe de similarité sémantique. Après une étape classique couplant clustering et dessin de graphe, nous avons élaboré une mesure de cohérence inspirée par les travaux de Burt et Schott et offrant un retour visuel qualitatif des agrégats générés. Cette mesure de cohérence permet à l'utilisateur de contrôler et valider différents processus de filtrage et raffinage du clustering initial. La cartographie résultante met en évidence deux types d'agrégats : thématiques ou évènementiels

Mesurer la cohésion sémantique dans les corpus de documents

Exploring document collections remains a focus of research. This task can be tackled using various techniques, typically ranking documents according to a relevance index or grouping documents based on various clustering algorithms. The task complexity produces results of varying quality that inevitably carry noise. Users must be careful when interpreting document relevance or groupings. We address this problem by computing cohesion measures for a group of documents con rming/in rming whether it can be trusted to form a semantically cohesive unit. The index is inspired from past work in social network analysis (SNA) and illustrates how document exploration can bene t from SNA techniques.L'exploration de corpus documentaire reste encore aujourd'hui un domaine actif de recherche. Cette tâche peut être abordé à l'aide de nombreuses techniques, s'appuyant typiquement sur le calcul d'indices de pertinence ou de regroupement thématique (clustering). Ces solutions sont souvent empreintes de bruit, du fait même de la complexit é de la tâche à mener. Les utilisateurs se doivent par conséquent d'être précautionneux lorsqu'il s'agit d'interpréter les résultat d'un ordonnancement ou de regroupement thématique des documents. Nous nous penchons sur cette dernière question et calculons des indices de coh ésion s émantique associ és à un groupe de documents permettant de questionner la coh ésion d'un groupe de documents. Ces indices s'inspirent de travaux pass és en analyse des réseaux sociaux (SNA) et montre combien il semble possible d'exploiter les r ésultats de ce domaine à des fi ns d'exploration de bases documentaires

Analysis and Visualisation of Edge Entanglement in Multiplex Networks

Cette thèse présente une nouvelle méthodologie pour analyser des réseaux. Nous développons l'intrication d'un réseau multiplex, qui se matérialise sous forme d'une mesure d'intensité et d'homogénéité, et d'une abstraction, le réseau d'interaction des catalyseurs, auxquels sont associés des indices d'intrication. Nous présentons ensuite la mise en place d'outils spécifiques pour l'analyse visuelle des réseaux complexes qui tirent profit de cette méthodologie. Ces outils présente une vue double de deux réseaux,qui inclue une un algorithme de dessin, une interaction associant brossage d'une sélection et de multiples liens pré-attentifs. Nous terminons ce document par la présentation détaillée d'applications dans de multiples domaines.When it comes to comprehension of complex phenomena, humans need to understand what interactions lie within them.These interactions are often captured with complex networks. However, the interaction pluralism is often shallowed by traditional network models. We propose a new way to look at these phenomena through the lens of multiplex networks, in which catalysts are drivers of the interaction through substrates. To study the entanglement of a multiplex network is to study how edges intertwine, in other words, how catalysts interact. Our entanglement analysis results in a full set of new objects which completes traditional network approaches: the entanglement homogeneity and intensity of the multiplex network, and the catalyst interaction network, with for each catalyst, an entanglement index. These objects are very suitable for embedment in a visual analytics framework, to enable comprehension of a complex structure. We thus propose of visual setting with coordinated multiple views. We take advantage of mental mapping and visual linking to present simultaneous information of a multiplex network at three different levels of abstraction. We complete brushing and linking with a leapfrog interaction that mimics the back-and-forth process involved in users' comprehension. The method is validated and enriched through multiple applications including assessing group cohesion in document collections, and identification of particular associations in social networks.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

TULIP 4

Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on more than 15 years of research and development, Tulip is built on a suite of tools and techniques , that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide Python and/or C++ developers a complete library, supporting the design of interactive information visualization applications for relational data, that can be customized to address a wide range of visualization problems. In its current iteration, Tulip enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. This development pipeline makes the framework efficient for creating research prototypes as well as developing end-user applications. The recent addition of a complete Python programming layer wraps up Tulip as an ideal tool for fast prototyping and treatment automation, allowing to focus on problem solving, and as a great system for teaching purposes at all education levels