Data in society – Page 5 – Data Big and Small

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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International Program in Survey and Data Science

A new, master’s level programme of study in Survey and Data Science is to be offered jointly by the University of Mannheim, the University of Maryland, the University of Michigan, and Westat. Applications for the first delivery are accepted until 3 January, for a start in Spring 2016. Prospective students are professionals with a first degree, at least one year of work experience, and some background in statistics or applied mathematics. All courses are delivered in English, fully online, to small classes (it’s not a MOOC!). Tuition is free, thank to support from German public funds at least for the first few cohorts.

What is most interesting about this master is its twofold core, involving both more classical survey methodology and today’s trendy data science. Fundamental changes in the nature of data, their availability, the way in which they are collected, integrated, and disseminated, have found many professionals unprepared. These changes are partly due to “big” data from the internet and digital devices becoming increasingly predominant relative to “small” data from surveys. Big data offer the benefit of fast, low-cost access to an unprecedented wealth of informational resources, but also bring challenges as these are “found” rather than “designed” data: less structured, less representative, less well documented (if at all…). In part, these changes are also due to the world of surveys changing internally, with new technical challenges (regarding for example data preservation, in a world of pre-programmed digital obsolescence), legislative issues (such as those triggered by greater awareness of privacy protection), increased demand by multiple users, and a growing need to merge surveys and data from other (such as business and administrative) sources. It is therefore necessary, as the promoters of this new study programme rightly recognize, to prepare students for the challenges of working both with designed data from surveys and with big data.

It will be interesting to see how data science, statistics, and social science / survey methodology feed into each other and support each other (or fail to do so…). There is still work to be done to develop techniques for analyzing data that allow us to gain insights more thoroughly, not just more quickly, and help us develop solid theories, rather than just uncovering new relationships that might eventually turn out to be spurious.

archive_pic — *A paper archive – more and more often replaced by digitised versions today.*

Yesterday at Biblithèque Nationale de France, I took part in a panel discussion on longue durée in history, organised by the Revue Annales – Histoire et Sciences Sociales. Of course I am not a historian, and I wouldn’t be able to tell whether one interpretation of longue durée is better than another. But historians are now raising questions that are common to the social sciences and humanities more generally: how to benefit from big data and how to re-think the political engagement of the researcher. So I was there to talk about big data and how they change not just research practices and methods, but also researchers’ position relative to power, politics, and industry. This questions cross disciplinary boundaries, and all may benefit from dialogue.

BellinghamCB001 — *Collection of older sources is now often online and enables application of new methods.*

What ignited the historians’ debate was an attempt by two leading scholars, David Armitage and Jo Guldi, to restore history’s place as a critical social science, based on (among other things) increased availability of large amounts of historical data and the digital tools necessary to analyze them. Before their article in Annales, they published a full book in open access, the History Manifesto, where they develop their argument in more detail. Their writing is deliberately provocative, and indeed triggered strong (and sometimes very negative) reactions. Yet the sheer fact that so many people took the trouble to reply, proves that they stroke a chord.

What do they say about big data? They highlight the opportunity of accessing large and rich archives and to expand research beyond any previous limitations. Their enthusiasm may seem excessive but it is entirely understandable insofar as their goal is to shake up their colleagues. My approach was to take their suggestion seriously and ask: what opportunities and challenges do data bring about? How would they affect research, especially for historians?

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“Data for Humanity”: a simple message, but so necessary

The recent VW emissions scandal says it all: even a large company can’t get away with behaviours that disrespect key societal values. Protection of the environment is among these values today, so much so that not only public authorities step in to defend it, but even markets punish the transgressors.

Data protection is not (yet) such a value. Admittedly, some associations, individuals, and government officials fight for it, but the larger public is still unsure. It’s not that people don’t care, but that uncertainty as to what data are actually collected, for what usages, and by whom, is overwhelming; and it becomes difficult to identify the best course of action.

In this context, a new initiative is most welcome: an open letter on “Data for Humanity“, initiated by two scholars of the University of Frankfurt, pleads for a more responsible use of data. The message is simple: Do no harm. And if you can, on top of it, do something good. It’s so simple, and so necessary.

Sure, the world won’t change after this letter, but it will be a first step. Even the promotion of environmental protection started with simple, basic declarations, 30-40 years ago; and it was by insisting and perseverating, that it finally gained the conscience of everybody.

New publications on big data and official statistics

National Statistical Institutes (NSIs) have long been the recognised repositories of all socio-economic information, mandated by governments to collect and analyse data on their behalf. The development of big data is shaking this world. New actors are coming in and commercially-oriented, privately-produced information challenges the monopoly of NSIs. At the same time, NSIs themselves can tap into digital technologies and produce “big” data. More generally, these new sources offer a range of opportunities, challenges and risks to the work of NSIs.

The Statistical Journal of the IAOS, the flagship journal of the International Association for Official Statistics, has published a special section on big data – of particular interest to the extent that it is free of charge!

Fride Eeg-Henriksen and Peter Hackl introduce this special section by defining big data and emphasising its interest for official statistics. But it is crucial, albeit admittedly not easy, to separate the hype around big data from its actual importance.

The other papers are concrete examples of how big data may be integrated into official statistics:

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The data of my friend are my data

The rise of digital data, particularly data from the internet, is to be understood in social relational perspective. Online interactions – from email exchanges to use of VOIP services and participation in social media such as Facebook, Twitter and LinkedIn – make people’s social connections explicit and visible. The “social network”, once a metaphor used only in a small sub-field within sociology, is now familiar to everybody as the archetype of computer-mediated social interaction. Digital devices systematically record network structures, so that social ties become an essential part of every individual profile, and users are more and more aware of them.

One consequence of this is the booming popularity of network analysis concepts, which support the algorithms that handle digital data: for example, centrality measures are at the heart of search engine functionalities, and transitivity measures found “friend-of-a-friend” algorithms in social media. In passing, social network analysis itself which had been originally developed for small-sized, non-digital datasets (like surveys about friendship in schools) has undergone a major upgrade to account for social data from the web.

More importantly, the relational nature of digital data and the underlying possibilities to use social network analysis, open up new avenues for data collection. If user B publishes a post on, say, their Facebook wall, comments and “likes” received from their friends A, D and E will be connected to the profile of B, accessible and visible from it; in other words, it is possible to retrieve information on A, D or E through the profile of just B. In general social networks, a friend of my friend is my friend; in digital networks, the data of my friends are my data.

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Data and social networks: empowerment and new uncertainties (in Italian)

I gave a presentation on the topic of “Data and social networks: empowerment and new uncertainties” at the Better Decisions Forum on Big Data and Open Data that took place in Rome on 12 November 2014. The event brought together six speakers from different backgrounds on a variety of topics related to data, and participants were businesspeople, public administration managers, journalists, data and computer scientists.

Here is a video of my talk:

Unfortunately as you will have noticed, the slides are not always very clearly visible, so it’s better to download them from their original source:

My interview before my talk:

See? I am trying to stick to my 1st-January commitment of blogging more this year…

How many friends do you have?

How many people do you know? How many friends do you have? You may have tried to count your contacts on Facebook or other social networking websites. You may even have felt a bit weird realizing that your “real” friends — those you can rely on — are just a handful. As unexpected it might seem, business professionals have this question in mind too: they want to get a sense of the potentially useable social capital of their associates and employees.

Social research has investigated this matter intensely and can offer insight. There are, in fact, two aspects to be considered: the size of personal networks and the effects of online communication on socialisation.

The size of personal networks

A personal network. Hollow circles represent face-to-face contacts, filled circles represent online contacts. Green = emotionally intimate, blue = very close, yellow = close, red = not-so-close. — A personal network. Hollow circles represent face-to-face contacts, filled small circles represent online contacts, nested circles are both face-to-face and online. Green = emotionally intimate, blue = very close, yellow = close, red = not-so-close.

Let us first start with the size of personal networks. A milestone in this debate is the so-called “Dunbar’s number“, based on a 1992 study of Oxford anthropologist Robin Dunbar. The idea is that human cognitive capacities as measured by the size of the neocortex lead to a network size of around 148 (with some range of variation). The original study compared the size of the neocortex in various groups of primates and humans and referred to cohesive communities. The resulting limit indicates the number of people with whom one can maintain “stable” social relationships, i.e., know who each contact is, and how they are related to one another.

Other parts of the brain may be involved too, suggest neuroscientists: Lisa Barrett and her co-authors (2010) found a correlation between amygdala volume and social network size in humans. (I understand that the amygdala is the part of brain that regulates emotional responses and aggression, while the neocortex to which Dunbar referred is the part of the brain that presides higher mental functions.) (see this Blogpost for further information).

In social network analysis perspective, it is also important to define which social network we are measuring. Peter Marsden (1987) distinguished “core” networks from whole personal networks, pointing out that even when people have many friends, there are only a handful with whom they “can discuss important matters”. In this sense, core networks may not include more than five or six people. So if you thought you had very few friends, you shouldn’t feel weird after all… apparently the Portuguese have a saying, “You have five friends, and the rest is landscape.”

On the other hand, your full network also including mere acquaintances and weaker ties may be much larger than Dunbar’s: counts of full networks taken by Peter Killworth, H. Russel Bernard, Chris McCarthy and co-authors in the 1990s – 2000s went up to about 1500 for the average American. From these, they extracted more meaningful measures of networks that are really relevant for people’s daily lives and came up with other numbers: they found a mean personal network size of 290 (twice the Dunbar number!); more recently, Matthew Salganik and his co-authors (2010) have come up with an even larger size of 610 (twice Killworth’s number…).

Overall, an issue that emerges from many of these discussions is that cognitive capacities (however defined) matter primarily because they are associated with a basic limitation of all living beings –time is finite. Therefore, increasing the size of one’s personal network implies that less time is available for each contact: the size of the overall network increases, but the size of the core network doesn’t. Weak ties may gain at the expense of strong ties.

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The transformative powers and the politics of data visualisation: a case with personal network data

Data visualisation is still relatively uncommon in the social sciences, and is not normally expected to be part of the standard work of a scholar (contrary, some would say, to what happens in the sciences, where visualisation is sometimes necessary to figure out the properties of objects whose existence is proven, but which cannot be seen). Yet data visualisation has an extraordinary history of accomplishments even in the social realm, as cleverly documented in a forthcoming article by James Moody and Kieran Healy; and classics such as Pierre Bourdieu valued it and attempted to use it in at least some of their work, as Baptiste Coulmont interestingly reported in a blog post.

Yet the digital age offers new opportunities for data visualisation, that are largely unexploited in the social sciences. It becomes not only a tool for the researcher — to explore data prior to conducting statistical analyses, or to present results once the work is done — but also for the general user, the study subject, the beneficiary of any policy under discussion, and the general public. As theorists in the arts and digital humanities (but not much in the social sciences, I am afraid) have noticed, the Internet and all digital infrastructures are becoming today interfaces with databases, and users of all types are immersed in a world of data in a way that was unknown before. This means that data visualisations can have new and more transformative uses, empowering study subjects and people in general, by offering them intuitive and aesthetically appealing tools to better navigate this digital world. But it also involves new dangers, as to who sets the agenda and what aspects or characteristics of the data are being stressed; data are not just objective, ‘raw’ materials but mediated ones, and the choice of how to make them perceptible by the senses is not neutral.

At the annual conference of the British Sociological Association today in Leeds, in the Methodological Innovations Stream, I am presenting data visualisation work I have done with colleagues Antonio A. Casilli, Lise Mounier and Fred Pailler, as well as data visuliaser Quentin Bréant, as part of the research project ANAMIA. We developed three tools — one for data collection, one for data exploration and preliminary analysis, one as a basis for heuristics and presentation of results. The first was for our study subjects, the second for us researchers and our colleagues, the third for us and the larger public. My slides are available: