Posts Tagged ‘ Big data ’

Special RFS issue on Big Data

Revue Française de Sociologie invites article proposals for a special issue on “Big Data, Societies and Social Sciences”, edited by Gilles Bastin (PACTE, Sciences Po Grenoble) and myself.

Focus is on two inextricably interwoven questions: how do big data transform society? How do big data affect social science practices?

Substantive as well as epistemological / methodological contributions are welcome. We are particularly interested in proposals that examine the social effects and/or the scientific implications of big data based on first-hand experience in the field.

The deadline for submission of extended abstracts is 28 February 2017; for full contributions, it is 15 September 2017. Revue Française de Sociologie accepts articles in French or English.

Further details and guidelines for submission are in the call for papers.


Data, health online communities and the collaborative economy: my tour of Québec

This November gave me the opportunity to give talks and participate in scientific events throughout Québec.

comsanteI started in Montréal, with a seminar at ComSanté, the health communication research centre of Université du Québec à Montréal (UQAM), where I presented my recently published book on websites on eating disorders. While most media attention focused on controversial “pro-anorexia” contents, presented as an undesirable effect of online free speech, I made the point that this part of the webosphere is rather to be seen as a symptom of the effects of current transformations of healthcare systems under austerity policies. Cuts in public health spending encourage patients to be active, informed and equipped, but the resulting social pressure creates paradoxical behaviors and risk-taking.

Also in Montréal, I was invited to a discussion with economic journalist Diane Bérard on the growth and crisis of theecocoll collaborative economy. About 50 people attended the event, co-organised by co-working space L’Esplanade, OuiShare Montréal and the journal Les Affaires. Diane summarized the essentials of the event in a blog post just the day after, and noted six main points:

  • The Uber case dominates discussions and divides the audience – though the collaborative economy is not (just) Uber.
  • The discussion gets easily polarized – a result of the tension between commercial and non-commercial goals of the collaborative economy.
  • We still know little of the business models of these platforms and the external factors that facilitate or hinder their success.
  • Sharing is in fact a niche market – now probably declining after the first enthusiasms.
  • The key issue for the future is work – its transformations, and how it is re-organizing itself.
  • Collaborative principles advance even outside the world of digital platforms, and sometimes permeate more traditional sectors. The near future of collaboration are sharing cities.

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Are we all data laborers?

autonomyI gave today a talk at AUTONOMY, a major festival of urban mobility in Paris, where new technologies are at center stage, from driverless cars to electric scooters, bike-sharing solutions, and connected infrastructure for the smart city. I had been asked to talk about labor in digital platforms, such as those offering mobility services.

Digital platforms are often thought of in terms of automation, but it islogos clear that there is labor too: we all have in mind the example of the couriers and drivers of the “on-demand” economy. But there’s more: I’ll show how platforms involve the labor of everyone, including passengers and users of all types. By labor, I mean here human activity that produces data and information – the key source of value for platforms. It is often an implicit, invisible activity of which we may not even be aware – as we tend to focus more on consumption aspects as we talk routinely about “car pooling” or “car sharing”, rather than looking at the underlying productive effort. This is what scholars call “digital labor”.

Four eco-systems

Specialist Antonio Casilli distinguishes four forms of digital labor in platforms, and I am now going to briefly outline them.

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Data and theory: substitutes or complements? Lessons from history of economics

EEToday, my chapter on “Formalization and mathematical modelling” is published in a new series of three reference books on History of Economic Analysis (edited by G. Faccarello and H. Kurz, Edward Elgar). The chapter draws heavily on key ideas I developed as part of my thesis on the origins of mathematical economics. But this was a long time ago and reading it again today, I see it in a different light. I notice in particular that economics developed its distinctive mathematical flavour, which makes it neatly stand out relative to the other social sciences, at times in which social research was data-poor – and it did so not despite data paucity, but precisely because of it. William S. Jevons, a 19th-century forefather of the discipline who was clearly aware of the relevance of maths, wrote in 1871:

“The data are almost wholly deficient for the complete solution of any one problem”


“we have mathematical theory without the data requisite for precise calculation”

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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.

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… 😉