Social science research – Page 3

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…

“Pro” ana? Sociability and support in eating disorder online communities

This article was first published on Discover Society, November 2014.

Last June, a group of Italian MPs proposed jail terms and fines for authors of so-called “pro-ana” (anorexia) and “pro-mia” (bulimia) websites. These are self-styled online communities on eating disorders which are viewed as promoting extreme dieting and unhealthy eating practices. France and the United Kingdom preceded Italy’s attempt to pass restrictive legislation as far back as 2008-9, and many internet service providers also endeavoured to ban these contents.

But the potential spread of health-hazardous behaviours is probably only one side of the coin, and these websites might also channel health-enhancing assistance, advice, and support (Yeshua-Katz & Martins 2013). In fact a closer look reveals that website users carefully manage their online socialisation to address their health challenges. Online social spaces enable discussion around the illness and constitute a complement, albeit an admittedly imperfect one, to formal healthcare services. There is no rejection of standard health norms in the name of some extreme ideal of thinness but rather a need – or perhaps, a cry – for extra support.

A social science approach brings out these results. The effect of web interactions on health does not only depend on website contents, but also on how people actually use them, share them, and access resources through them. The social, rather than just clinical dimension of eating disorders, recognized long before the advent of the web (Bell 1985, Orbach 1978), becomes ever more relevant in the current context and calls for a more comprehensive view of the “ana” and “mia” social universe.

(Credit: Roberto Clemente)

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Sociology in 2014: rediscovering methods

I was back last week from the annual conference of British Sociological Association (BSA) in Leeds, and as usual, I try to put down my impressions as long as they’re still fresh in my mind. I wasn’t very quick, though, and the BSA’s members newsletter has already come out with comments and short reports about the plenaries, the prizes awarded, and the conference overall. While the conference is described as having been “very vibrant and sociable”, with “exciting conversations” during the breaks and a “diverse mixture of topics” that “reflected the breadth of interests”. My own feelings, I confess, are a bit more mixed.

In 2012, the BSA conference was followed by a lively debate after an article, by Aditya Chakrabortty on the Guardian, where he complained about the discipline’s lack of engagement with the financial crisis. He pointed to the BSA press releases featuring research on “older bodybuilders”, and to time devoted to the “holistic massage industry” at the conference, as evidence of what he saw as a retreat from public space. The BSA took the criticism very seriously and, apart from responding to the Guardian, put in place a massive effort to encourage public engagement. The 2013 conference was entitled “Engaging Sociology” and many sessions were dedicated to showing that the profession means it. Confrontation and comparison with economics was open and clear. A major project on social class was presented with all honours. The Sociology journal released a call for papers for a special issue to “Sociology and the Global Economic Crisis”.

This year, the “Changing Society” title aimed to stress continuity with last year’s efforts; yet it seems to me that we are back to business as usual. I had the impression that many paper presentations were on topics similar to the body builders and massage that Chakrabortty talked about. That’s why, as I said, my feelings are mixed.

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Qualitative networks

Social Network Analysis (SNA) is booming, and many think it’s because of internet networks and big data. Yet social networks themselves are not new: people have always formed ties to one another, and online platforms such as Facebook, Twitter and LinkedIn only offer channels for networked interactions to occur. Counts and fancy visualisations of myriad likes and shares do not tell the whole story either: networks are primarily about exploring how ties connect us as individuals and as organisations or groups, and how our social relationships affect our lives and behaviours.

In this sense, smaller studies can still have much to teach us. These include not only quantitative, but also qualitative approaches. “Social” networks involve a world of meanings, feelings, relationships, attractions, dependencies, which have traditionally been at the heart of qualitative research and are amenable to a mixed-methods approach.

In this perspective, with the Social Network Analysis Group of the British Sociological Association (BSA-SNAG), I am organising a one-day small conference on “Mixed Methods Approaches to Social Network Analysis”, exploring how the combination of SNA and qualitative methods can enrich and deepen our understanding of network content in conjunction with network structure. The event will take place on 12 May 2014 at Middlesex University, London, and the programme is available here; to register online (deadline 30 April!) click here.

Small data and big models: Sunbelt 2014

Uh, it’s been a while… I should have written more regularly! All the more so as many things have happened this month, not least the publication of our book on the End-of-Privacy hypothesis. Well, I promise, I’ll catch up!

Meanwhile, a short update from St Pete Beach, FL, where the XXXIV Sunbelt conference is just about to end. This is the annual conference of the International Network for Social Network Analysis and in the last few years, I noticed some sort of tension between the (let’s call it like that — no offense!) old-school of people using data from classical sources such as surveys and fieldwork, and big data people, usually from computer science departments and very disconnected from the core of top social network analysts, mostly from the social sciences. This year, though, this tension was much less apparent, or at least I did not find it so overwhelming. There weren’t many sessions on big data this time, but a lot of progress with the old school — which in fact is renewing its range of methods and tools very fast. No more tiny descriptives of small datasets as was the case in the early days of social network analysis, but ever more powerful statistical tools allowing statistical inference (very difficult with network data — I’ll go back to that in some future post), hypothesis testing, very advanced forms of regression and survival analysis. In this sense, a highly interesting conference indeed. We can now do theory-building and modeling of networks at a level never experienced before, and we don’t even need big data to do so.

The keynote speech by Jeff Johnson, interestingly, was focused on the contrast between big and small data. Johnson has strong ethnographic experience with small data, including in very exotic settings such as scientific research labs at the South Pole and fisheries in Alaska. He combined social network analysis techniques, sometimes using highly sophisticated mathematical tools, with fieldwork observation to gain insight into, among other things, the emergence of informal roles in communities. His key question here was, can we bring ethnographic knowing to big data? And how can we do so?

My own presentation (apart from a one-day workshop I offered on the first day, where I taught the basis of social network analysis) took place this afternoon. I realize, and I am pleased to report, that it was in line with the small-data-but-sophisticated-modeling mood of the conference. It is a work derived from our research project Anamia, using data from an online survey of persons with eating disorders to understand how the body image disturbances that affect them are related to the structure of their social networks. The data were small, because they were collected as part of a questionnaire; but the survey technique used was advanced, and the modeling strategy is quite complex. For those who are interested in the results, our slides are here:

Paola Tubaro ANAMIA INSNA Sunbelt2014 St. Pete's Beach FL 22.02.2014 from Bodyspacesociety Blog

Network data, new and old: from informal ties to formal networks

Network data are among those that are changing fastest these days. When I say I study social networks, people almost automatically think of Facebook or Twitter –without necessarily realizing that networks have been around for, well, the whole history of humanity, long before the internet. Networks are just systems of social relationships, and as such, they can exist in any social context — the family, school, workplace, village, church, leisure club, and so forth. Social scientists started mapping and analysing networks as early as the 1930s. But people didn’t think of their social relationships as “networks” and didn’t always see themselves as “networkers” even if they did invest a lot in their relationships, were aware of them, and cared about them. The term, and the systemic configuration, were just not familiar. There was something inherently informal and implicit about social ties.

What has changed with Facebook and its homologues, is that the network metaphor has become explicit. People are now accustomed to talking about “networks”, and think in systemic terms, seeing their own relationships as part of a more global structure. Network ties have become formal — you have to make a clear choice and action when you add a “friend” on Facebook, or “follow” someone on Twitter; you will have a list of your friends/followers/followees (whatever the specific terminology is) and monitor changes in this list. You know who the friends of your friends are, and can keep track of how many people viewed your profile /included you in their “lists” / mentioned you in their Tweets. Now, everyone knows what networks are –so if you are a social network researcher and conduct a survey like in the old days, you won’t fear your respondents may misunderstand. In fact, you may not even need to do a survey at all –the formal nature of online ties, digitally recorded and stored, makes it possible to retrieve your network information automatically. You can just mine network tie data from Facebook, Twitter, or whatever service your target populations happen to be using.

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#bigdataBL

On Friday last week, the British Sociological Association (BSA) held an event on “The Challenge of Big Data” at the British Library. It was interesting, stimulating and relevant – I was particularly impressed by the involvement of participants and the very intense live-tweeting, never so lively at a BSA event! And people were particularly friendly and talkative both on their keyboards and at the coffee tables… so in honour of all this, I am choosing the hashtag of the day #bigdataBL as title here.

(Visualisation: http://www.digitalcoeliac.com/)

Some highlights:

The designation of “big data” is from industry, not (social) science, said a speaker at the very beginning. And it is known to be fuzzy. Yet it becomes a relevant object of scientific inquiry in that it is bound to affect society, democracy, the economy and, well, social science.
Big-data practices change people’s perception of data production and use. Ordinary people are now increasingly aware that a growing range of their actions and activities are being digitally recorded and stored. Data are now a recognized social object.
Big data needs to be understood in the context of new forms of value production.
So, social scientists need to take note (and this was the intended motivation of the whole event). The complication is that Big Data matter for social science in two different ways. First, they are an object of study in themselves – what are their implications for, say, inequalities, democratic participation, the distribution of wealth. Second, they offer new methods to be exploited to gain insight into a wide range of (traditional and new) social phenomena, such as consumer behaviours (think of Tesco supermarket sales data).
Put differently, if you want to understand the world as it is now, you need to understand how information is created, used and stored – that’s what the Big Data business is all about, both for social scientists and for industry actors.

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Big Data and social research

Data are not a new ingredient of socio-economic research. Surveys have served the social sciences for long; some of them like the European Social Survey, are (relatively) large-scale initiatives, with multiple waves of observation in several countries; others are much smaller. Some of the data collected were quantitative, other qualitative, or mixed-methods. Data from official and governmental statistics (censuses, surveys, registers) have also been used a lot in social research, owing to their large coverage and good quality. These data are ever more in demand today.

Now, big data are shaking this world. The digital traces of our activities can be retrieved, saved, coded and processed much faster, much more easily and in much larger amounts than surveys and questionnaires. Big data are primarily a business phenomenon, and the hype is about the potential gains they offer to companies (and allegedly to society as a whole). But, as researcher Emma Uprichard says very rightly in a recent post, big data are essentially social data. They are about people, what they do, how they interact together, how they form part of groups and social circles. A social scientist, she says, must necessarily feel concerned.

It is good, for example, that the British Sociological Association is organizing a one-day event on The Challenge of Big Data. It is a good point that members must engage with it. This challenge goes beyond the traditional qualitative/quantitative divide and the underrepresentation of the latter in British sociology. Big data, and the techniques to handle them, are not statistics, and professional statisticians have trouble with it too. (The figure below is just anecdotal, but clearly suggests how a simple search on the Internet identifies Statistics and Big Data as unconnected sets of actors and ties). The challenge has more to do with the a-theoretical stance that big data seem to involve.

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The fuzziness of Big Data

Fascinating as they may be, Big Data are not without posing problems. Size does not eliminate the problem of quality: because of the very way they are collected, Big Data are unstructured and unsystematized, the sampling criteria are fuzzy, and the classical statistical analyses do not apply very well. The more you zoom in (the more detail you have), the more noise you find, so that you need to aggregate data (that is, to reduce a “big” micro-level dataset to a “smaller” macro one) to detect any meaningful tendency. Analyzing Big Data as they are, without any caution, increases the likelihood of finding spurious correlations – a statistician’s nightmare! In short, processing Big Data is problematic: Although we do have sufficient computational capacity today, we still need to refine appropriate analytical techniques to produce reliable results.

In a sense, the enthusiasm for Big Data is diametrically opposed to another highly fashionable trend in socioeconomic research: that of using randomized controlled trials (RCTs), as in medicine, or at least quasi-experiments (often called “natural experiments”), which enable collecting data under controlled conditions and facilitate detection of causal relationships much more clearly and precisely than in traditional, non-experimental social research. These data have a lot more structure and scientific rigor than old-fashioned surveys – just the opposite of Big Data!

This is just anecdotal evidence, but do a quick Google search for images on RCTs vs. Big Data. Here are the first two examples I came across: on the left are RCTs (from a dentistry course), on the right are Big Data (from a business consultancy website). The former conveys order, structure and control, the latter a sense of being somewhat lost, or of not knowing where all this is heading… Look for other images, I’m sure the great majority won’t be that different from these two.

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