Posts Tagged ‘ Digital data ’

Le moment big data des sciences sociales: quel accès aux données du web et des médias sociaux ?

Table ronde, Sciences Po Paris, 6 décembre 2018, 18h00

RFS2018

Pour que la recherche en sciences sociales puisse pleinement tirer profit des grandes bases de données numériques, un verrou reste à lever : l’accès à ces données est limité, inégalement distribué, et entouré d’un flou juridique et déontologique. Nous proposons d’en discuter à l’occasion de la parution du numéro spécial de la Revue Française de Sociologie sur “Big data, sociétés et sciences sociales” (n. 59/3). Cette table ronde réunit les chercheur.e.s avec d’autres parties prenantes publiques et
privées.

Avec :

  • Garance Lefèvre, Policy senior associate, Uber
  • Roxane Silberman, Conseillère scientifique, Centre d’Accès Sécurisé aux Données (CASD)
  • Sophie Vulliet-Tavernier, Directrice des relations avec les publics et la recherche, Commission Nationale de l’Informatique et des Libertés (CNIL)
  • Les auteurs du numéro spécial.

Modérateurs : Gilles Bastin (Univ. Grenoble Alpes) et Paola Tubaro (CNRS), coordinateurs du numéro spécial.

Entrée libre et gratuite, dans la limite des places disponibles: pour s’inscrire, cliquez ici.

Accès : Sciences Po, salle Goguel. Entrée par le 27 rue Saint-Guillaume, 75007 Paris (traverser le jardin et prendre l’ascenseur jusqu’au dernier étage). La table ronde est organisée par la Revue Française de Sociologie en collaboration avec les Presses de Sciences Po. Elle sera suivie d’un pot.

 

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Science XXL: digital data and social science

I attended last week (unfortunately only part of) an interesting workshop on the effects of today’s abundance and diversity of digital data on social science practices, aptly called “Science XXL“. A variety of topics were discussed and different research experiences were shared, but I’ll just summarize here a few lessons learned that I find interesting.

  • Digital data are archive data. Data retrieved automatically from the digital traces of individual actions, such as those mined from the APIs of platforms such as Twitter, are unlike survey data in that they were not originally recorded for research purposes. The researcher must select relevant records on the basis of some understanding of the conditions under which these data were produced. Perhaps ironically, digital data share these characteristic with data from historical or literary archives.
  • Digital data are not necessarily “big”, in the sense that their volume is often small (at least in social science research so far!), even though they may share other characteristics of big data such as velocity (being generated on the fly as people use digital platforms) or variety (being little or not structured).
  • Digital data can help fill gaps in survey data, for example when survey sampling is not statistically representative: detail and volume can provide extra information that supports general conclusions.
  • Non-clean data, outliers and aberrant observations may be very informative, revealing details that would escape attention if researchers focused only on the average or center of the distribution (the normal law cherished in classical statistical approaches). Special cases are no longer a prerogative of qualitative research.
  • Data analysis is a key ingredient of “computational social science” a field that is growing in importance after an initial phase in which it was largely confined to agent-based simulation and complexity theory.

Hierarchy, market or network? The disruptive world of the digital platform

Economics traditionally considered firms and markets as two alternative ways of coordinating economic activities. Nobel prize winner Ronald H. Coase (1937) demonstrated that it all hinges on “transaction costs”, such as the need to search for a trade partner, the time needed to negotiate a contract, the legal expenses to draw it up and if necessary, to enforce it. When these costs are high, then hiring people in a firm is the right solution. When they are low, then a harmonious state will emerge spontaneously from the choices of independent, self-employed individuals. The difference, further emphasized by the work of Oliver Williamson, another Nobel, is between the world of bureaucracy, hierarchy and salaried work, and the world of the market and myriad micro-entrepreneurs.

This dichotomous description seemed reductive to economic sociologists, and Mark Granovetter (1985) pointed to social networks as coordination devices. Networks enable circulation of knowledge, formation of trust, emergence of shared norms in informal ways, thereby lowering costs and smoothing economic transactions. Walter W. Powell (1990) saw networks as an alternative to market and hierarchy, while others thought of it as a complement rather than a substitute. In some cases, the relevance of networks is flagrant: think of “collegial“, horizontal organizations such as legal partnerships, which are clearly not markets, and which have no vertical hierarchy either.

HierarchyMktNetwork

The rise of online platforms challenges these older views today. Powered by digital data and matching algorithms, platforms are meeting places for actors on the two sides of a market: riders and drivers (Uber, Lyft, BlaBlaCar), guests and hosts (Airbnb), buyers and sellers (eBay), and so on. Officially, platforms are intermediaries only, able to put in touch, say, those who need a lift and those who have a car, so that they can share the ride. Platforms don’t employ drivers and don’t own cars.

Platform

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