SN_box2Antonio A. Casilli and I carried out a study during the OuiShare Fest 2016, a major international get-together of the collaborative economy community that took place in Paris on 18-21 May 2016.

Our goal is to look at how people network at this important event and how their meetings, their talking to each other and their informal interactions help shape the community — so as to foster the emergence of new ideas, trends and topics.

If you were a participant, speaker, journalist organizer or teamSN_slide member/volunteer, you were asked to complete a brief questionnaire in paper format that was handed out to you upon arrival at the Fest.

If you didn’t manage to fill in the questionnaire onsite, there is still time to do so online. It takes only about 8 minutes to do so and your contribution will help scientific research as well as the organization of the Fest.

Results will be made available through the OuiShare Magazine and other online outlets.

⇒Read more here

Complete the questionnaire

Thank you for your invaluable contribution!

Ethical issues in research with online data

Some time ago, I wrote a post on ethical issues in research with secondary data – a somewhat grey area, where students and scholars often feel guidance is insufficient. Even more complex is research with internet data – neither primary nor secondary strictly speaking, but “big” data. A recent case fuelled an international debate on how researchers should deal with data that are, apparently, accessible to all on the web: a Danish graduate student published a large dataset of users of the online dating site OkCupid (he apparently did so without any institutional backing, and Aarhus University where he studies, is now on the case). Michael Zimmer, a specialist of information studies and the policy and ethics of online research, properly summarizes the issues in a recent Wired article:

  • Don’t say that “the data are already public”. The fact that OkCupid users knowingly share some personal information, does not mean they consent to it being used for purposes other than interactions with other users on that site. By scrapping data, one may be able to put together the whole history of  users’ presence on that platform, revealing more of their life or personality than they themselves are aware of. More dangerously, data extracted in this way might in some cases be matched with other information, thereby potentially becoming much more disclosive than what the persons concerned ever intended or agreed. And the disclosure may be aggravated by releasing the data outside the platform.

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First steps toward “Data Inclusion”

The concept of “data inclusion” is new and still slowly seeking its way in our linguistical habits, but it is gaining ground in the minds of those who care for disadvantaged, low-income, or otherwise underserved segments of society. A recent report of the US Federal Trade Commission (FTC) does precisely this. Looking at the commercial use of big data analytics, it considers cases in which big data analytics lead companies to make choices that are detrimental to the most vulnerable segments of society, for example by excluding them from credit or from employment opportunities. Instead, it asks how big data may be used in inclusive ways.

A first set of recommendations they make is for companies to be well aware of the regulations: on financial and credit reporting, equal opportunities, consumer protection. The second set of recommendations, though specifically aimed at research done in (or for) companies, is of relevance for public research as well, and consists in asking key questions about the quality of data and models, and about the reliability and validity of results:

  • How representative is your data set? In popular discourse, big data carry a promise of exhaustivity, which however is rarely fulfilled in practice (see this great FT article by Tim Hartford). In fact, big data sets are not necessarily statistically representative of the population they refer to, and  information may be disproportionately missing about specific, possibly disadvantaged, populations.
  • Does your data model account for biases? Selection effects, which occur whenever some members of the population are less likely to be included in the sample than others, must be controlled for in order for results to be generalizable.
  • How accurate are your predictions based on big data? The issue is that most research with big data is predictive without being able to uncover the social or economic mechanisms underlying observed correlations, so that interpretation of results is potentially misleading. The report does not say, though, that recent developments in machine learning that support causality reasoning may alleviate this problem in the not-so-far future.
  • Does your reliance on big data raise ethical or fairness concerns? In all honesty, this is not specifically a question for research on big data, but for research in general. If a company’s analysis of employees’ behavior lead to solutions that involve forms of, say, racial or gender-based behavior, then that analysis shouldn’t be used – whether it’s done with “big” or “small” data.

It is important that major regulators like the FTC are taking notice. Big data open the way to major improvements in our life conditions, but not because data-driven analysis will take the lead over current best practices in research. Regulations, awareness of statistical issues and potential pitfalls, and ethics are ever more necessary for big data to fulfill their potential.

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.


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.


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Second European Social Networks Conference (EUSN 2016)

I am lucky enough to be part of the organizing committee of the second European Social Networks Conference, which will take place at Sciences Po Paris on 14-17 June 2016. The EUSN conferences have been created to offer a single place for the European community of social networks researchers to gather, in place of previous national annual conferences; and has been endorsed as a regional conference by INSNA, the international association of network researchers. A first, successful EUSN conference was held in Barcelona in 2014.


Somehow, the European social networks crowd seems more diverse than the US-based core of scholars who gave life to INSNA and drove its development over time. While remaining affectionate to the INSNA format and philosophy (for example, by selecting proposals only on the basis of an abstract, to be maximally inclusive), the European conferences can afford exploring new ideas, and variants on classical schemes. In particular, this year, we are trying to enlarge patricipation and attract delegates from a wider variety of disciplines, beyond those traditionally most represented – the social sciences, mathematics, and more recently statistics. Hence for example, the keynote speakers will give a sense of continuity – we will have social anthropologists Miranda Lubbers and José Luis Molina, the organisers of the first EUSN in Barcelona, on “Ethnography and multilevel networks in the study of migration and transnationalism”. But the plenary speech is an opening to recent, relevant developments in computer science: Jean-Daniel Fekete of INRIA will talk about “Challenges in social network visualization: bigger, dynamic, multivariate”.

Submissions are now invited for paper and poster proposals (abstract only – deadline 16 February 2016). There are special thematic sessions and general sessions, and all fields are welcome. A prize will be awarded for the best poster – where all participants will be able to vote.

The day before the conference, 15 training workshops are offered into the theory, data collection, methods of analysis and visualization of social networks.

16 February: Deadline for abstract/poster proposals, and pre-registration opening
1 March: Registration opening
16 March: Notification to authors
18 April: Early registration closure
14 June: Workshops
15-17 June: Conference

New year, new job, new life…

keep-calm-you-start-a-new-job-mondayYes I must admit it: I didn’t keep my new-year-2015 promise of posting more often on my blog… and the annual report I received yesterday from WordPress, showing a couple of peaks of activity and frigthening silence the rest of the year, isn’t something I would be proud to share… but I have a justification! Seriously, it’s not just an excuse – it’s that I’ve been busy trying to change life… and yes, I managed. On Monday 4 January, I’ll start an exciting new position as senior research scientist at the National Center of Scientific Research (CNRS, or in French, Centre national de la recherche scientifique) in Paris. CNRS can be loosely compared to what is, in other countries, a National Research Council, but there’s more to it than international comparisons might vaguely suggest: this is probably the single most desired job in French academia, with a mission “to contribute to the development of knowledge… in all fields that contribute to the advancement of society“. In plain words, that’s basically pure research with almost no teaching apart from some PhD supervision… a dream that would hardly be possible in the UK, where I was before.

I’ll be at the Lab for Computer Science (LRI, Laboratoire de Recherche en Informatique, UMR8623) on the Saclay Computer+sciencecampus, and I’ll work with the A&O (Learning and Optimization) research team. The interesting thing is that mine is an interdisciplinary position, designed to facilitate dialogue and collaboration between the social sciences and computer science around big data and their use for the advancement of knowledge, policy, and more generally society. I have been especially selected by the sociology section of CNRS to work in a computer science research centre. There, I am asked to develop my personal, long-term research project on the “sharing economy” of digital platforms and how they create value from the social ties in which economic action is embedded: this will require blending my research on data, social networks and the digital economy with machine learning and optimization approaches (more on this later … yes on this blog! promise!).

eusn2016What else will I do this year at LRI? I am in the organising committee of the Second European Social Networks Conference which will take place in Paris next June, I am finishing a book on so-called “pro-anorexia” websites as the conclusion of my past project ANAMIA, and I am in the Editorial Board of Revue Française de Sociologie.

I won’t entirely forget England though… I’ll keep my doctoral students at Greenwich and continue my engagement at UCL’s Institute of Education as external examiner. Come on, you can’t just disappear after six years! Indeed, I’ll always remember those six years as most productive and fulfilling ones. And however happy I am now to join CNRS, I’ll never forget the expressions of love, sympathy and friendliness I received from colleagues and students when I left Greenwich in December. The cards, the presents, the parties… all beyond any expectations I might have had before! Thank you Greenwich. And well, yes, a big thank you to all those who made it possible – both those in London who made me have a great time far from home for so long, and those in Paris who helped me come back, not without effort, and have welcomed me now.

A great new year is about to start, and I promise I’ll document it more… 😉

Discussing platform cooperativism

On Monday, 7 December 2015 at Telecom ParisTech, I was discussant at a seminar by New School scholar Trebor Scholz on “Unpacking Platform Cooperativism“.


Internet platforms carry an unprecedented potential of value creation, exploiting the extraordinary power of data and algorithms to extract and distribute information to an extent never seen before. Information, we know since Hayek’s times, is the fuel that keeps markets going, that eliminates “lemons” and ensures an ever-better coordination between buyers and sellers, borrowers and lenders, or landlords and tenants. At the same time, the internet has channeled the dream of a viable non-market society, since Rheingold’s 1993 revival of the “community” and Barbrook’s 1998 “hi-tech gift economy“. So, can we put this informational efficiency to the service of a more humane economy, based on relationships, solidarity and reciprocation, rather than on the sheer market system?

The so-called “sharing economy” suggests answers, but also displays a tension: the efforts of myriad grassroots associations to develop collaboration as a value and a practice, sharply contrasts the spectacular growth of firms like Airbnb and Uber, now large multinationals, and their alleged cavalier attitude to anti-trust regulations and workers’ rights. If some say Uber is not really about sharing and collaboration, it is difficult to draw the line.

This ambiguity is fostered by a public discourse that focuses on the sharing of assets – the spare room in your home, or a sit in your car – that digital platforms enable. Asset-sharing has economic and social appeal: it increases efficiency by preventing assets from lying idle, while reducing waste, shifting emphasis away from consumerist values (“access is better than ownership“), and facilitating sociality beyond mere consumption.

But it is often forgotten that asset-sharing does not produce value by itself: it involves extra labour. In economic jargon, capital and labour and complementary production factors. In practice, if you want to put your spare room on Airbnb, you must produce an ad, monitor your message inbox and reply swiftly. You must clean the room and do the laundry before and after a guest’s visit. You must show your guests around when they arrive.

More importantly, the very opportunity of asset-sharing changes the incentives that shape labour supply – people’s willingness to sell their time and effort against a payment. Because of the expected compensation, some people will renounce use of a (non-spare) room to accommodate visitors instead, and others will do more journeys to drive passengers around – so it’s not really about sharing unused assets, it is about self-employment and starting a micro-business. A work opportunity as a complement to (and sometimes a substitute for) a main job.

This is where debates on internet platforms and the sharing economy rejoin the growing literature on digital labour — and where the contribution of Trebor Scholz is illuminating. Where others see assets (ie, capital), he sees labour. He shows us that the bottlenecks here are about labour, not capital, and that the success — be it economic or social– of the sharing economy is closely tied to the destiny of labour. Whether it appears on the surface as self-employment, micro-entrepreneurship or salaried work, doesn’t really matter. Trebor reminds us of Marx’s fundamental principle that production relations are central to our (capitalist) society, and value generation rests ultimately on labor. If this very crucial part of the human experience goes wrong, even the best side of the sharing economy – the one that endorses trust, reciprocity, and zero-waste – may fail to perform any transformative effects on society.


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