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Le développement des technologies numériques engendre une longue et profonde mutation de notre rapport à la connaissance. Quelle que soit l’étiquette utilisée pour désigner ce phénomène, de nombreuses personnes s’interrogent sur les évolutions de nos pratiques académiques (Lazer et al 2009, Ollion & Bollaert 2016). Explorer les parties pour construire les touts Latour et al avancent en 2012 que de nouvelles façons de représenter et surtout de naviguer dans les données permettraient de revenir sur le rapport entre tout et partie, débat aussi vieux que la sociologie elle-même (Latour 2012). D’après ces auteurs, chaque tout n’est qu’une façon particulière de voir les parties, un trait commun qui rassemble. Un trait parmi d’autres. Le tout est une prise tellement utile, tellement signifiante qu’on en oublie qu’il cache une réduction d’une foule de particularités. Or ce tout peut aujourd’hui être décomposé ou plutôt recomposé dynamiquement dans de nouveaux moyens d’exploration des données appelés Datascapes - autrement dit paysages de données. Reprenant ces intentions, nous avons depuis 2012 conçu des outils d’analyse exploratoire de données (Tukey 1977) qui permettent de multiplier les perspectives sur un même objet.

Publié en 2006
JACOMY Mathieu
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In the last few years, a spirit has been haunting our academic and popular culture — the spirit of networks. Throughout social as well as natural sciences, more and more phenomena have come to be conceived as networks. Telecommunication networks, neural networks, social networks, epigenetic networks, ecological and economic networks , the very fabric of our existence seems to be made of lines and dots. More recently, the interest for graphs overflowed from science to popular culture and images of networks started to appear everywhere. They decorate buildings and objects; they are printed on t-shirts and furniture; they colonize the desktop of our computers and the walls of our airports. Networks have become the emblem of modernity, a way to show and tell our world’s complexity. Our growing fascination for networks is not unjustified. Networks are powerful conceptual tools, encapsulating in a single object multiple affordances for computation (networks as graphs), visualization (networks as maps) and manipulation of data (networks as interfaces).

Cet article reprend une recherche de Luc Boltanski sur les enseignants de l’IEP de Paris. Dans cette recherche, Boltanski s’appuie sur une représentation tabulaire des champs sociaux pour montrer que la classe dominante se caractérise avant tout par sa multipositionnalité, c’est-à-dire par la tendance de ses membres à occuper plusieurs positions dans plusieurs champs. En remplaçant le tableau de Boltanski par un graphe d’individus et d’institutions, nous discuterons les caractéristiques et les avantages d’une sociologie de réseaux hétérogènes.

in Datafied Society: Social Research in the Age of Big Data. Publié en 2017-02-25
BOUNEGRU Liliana
GRAY Jonathan
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No doubt, networks have become indispensable mathematical tools in many aspects of life in the twenty first century. They allow us to calculate all kinds of relational metrics and to quantify the properties of their nodes, clusters and global structures. These modes of calculation are becoming increasingly prevalent in an age of digital data. But networks are more than formal analytical tools. They are also powerful metaphors of our collective life, with all of its complexity and its many dependencies. This is why, among the various strategies of data visualization, networks seem to have assumed a paradigmatic position, spreading to the most different disciplines and colonizing sometimes as mere decoration a growing number of digital and non-digital objects. Contemplating the visual representation of a network, we don’t (always) need to compute its mathematical properties to appreciate its heuristic value – as anyone who has ever used a transportation plan knows well. Networks are extraordinary calculating devices, but they are also maps, instruments of navigation and representation. Not only do they guide our steps through the territories that they represent, but they also invite our imagination to see and explore the world in different ways. [First paragraph]

in Digital Journalism Publié en 2016-06-20
BOUNEGRU Liliana
GRAY Jonathan
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Networks have become the de facto diagram of the Big Data age (try searching Google Images for [big data AND visualisation] and see). The concept of networks has become central to many fields of human inquiry and is said to revolutionise everything from medicine to markets to military intelligence. While the mathematical and analytical capabilities of networks have been extensively studied over the years, in this article we argue that the storytelling affordances of networks have been comparatively neglected. In order to address this we use multimodal analysis to examine the stories that networks evoke in a series of journalism articles. We develop a protocol by means of which narrative meanings can be construed from network imagery and the context in which it is embedded, and discuss five different kinds of narrative readings of networks, illustrated with analyses of examples from journalism. Finally, to support further research in this area, we discuss methodological issues that we encountered and suggest directions for future study to advance and broaden research around this defining aspect of visual culture after the digital turn.

The web is a field of investigation for social sciences, and platform-based studies have long proven their relevance. However the generic web is rarely studied in itself though it contains crucial aspects of the embodiment of social actors: personal blogs, institutional websites, hobby-specific media… We realized that some sociologists see existing web crawlers as “black boxes” unsuitable for research though they are willing to study the broad web. In this paper we present Hyphe, a crawler developed with and for social scientists, with an innovative “curation-oriented” approach. We expose the problems of using web-mining techniques in social science research and how to overcome those by specific features such as step-by-step corpus building and a memory structure allowing researchers to redefine dynamically the granularity of their “web entities”.

In this article, we present a few lessons we learnt in the establishment of the Sciences Po médialab. As an interdisciplinary laboratory associating social scientists, code developers and information designers, the médialab is not one of a kind. In the last years, several of such initiatives have been established around the world to harness the potential of digital technologies for the study of collective life. If we narrate this particular story, it is because, having lived it from the inside, we can provide an intimate account of the surprises and displacements of digital research. Founding the médialab in 2009, we knew that we were leaving the reassuring traditions of social sciences to venture in the unexplored territory of digital inscriptions. What we couldn't foresee was how much such encounter would change our research. Buying into gospel of Big Data, we imagined that the main novelty of digital research came from handling larger amounts of data. We soon realized that the interest of digital inscriptions comes instead from their proliferating diversity. Such diversity encouraged us to reshape our professional alliances, research practices and theoretical perspectives. It also led us to overcome several of the oppositions that used to characterize social sciences (qualitative/quantitative, situation/aggregation, micro/macro, local/global) and to move in the direction of a more continuous sociology.

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A partir du cas du web du livre en France, cet article interroge la notion de zoom dans l’exploration visuelle de réseaux. En soumettant un graphe à des questionnements et traitements successifs, il devient possible de comprendre ce qui apparaît comme une anomalie pour le chercheur, un entrelacement sur le plan visuel de deux communautés pourtant distinctes du point de vue des catégories et de l’algorithme de modularité. En appliquant ces différentes épreuves au graphe apparaît la variété des algorithmes de spatialisation et de clusterisation, les limites des notions de zoom, de découpage et d’exploration des réseaux, toutes métaphores spatiales peu pertinentes. Cette démarche plaide pour des conventions d’exploration qui assurent la robustesse de l’exploitation de ce type de données.

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