Posts Tagged ‘ Online social networks ’

Online health communities: data for doctors, patients and families

Online health communities have been demonstrated to be an important part of the self-empowering experience of today’s patients. While most attention so far has been devoted to self-styled health communities, where patients autonomously share expertise and experience, today policymakers and healthcare providers are harnessing the power of this very idea and are offering similar solutions themselves.

Earlier this week at the OuiShare Fest Barcelona – a major get-together of the Spanish-speaking collaborative economy community in Europe – a few of these initiatives were presented.

keyboard-and-stethoscopeSocial Diabetes is a small company founded by and for patients, that offers a mobile app for online, real-time health monitoring services. Diabete sufferers can use it to optimally adjust their insulin dosage based on their carb count and blood sugar levels; in some cases, they can also track their exercise and patterns of behavior to receive alerts whenever relevant. Patients can share this information with their doctors, also through the app; and can discuss with other patients. This is an example of a user-based innovation where autonomous patients take the initiative, aiming to take control of their health and life. Still, physicians have been allowed in: the platform has a medical advisory board, and individual doctors can register as users to follow their patients.

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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.

FOAFMore 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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“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.

SupportANAMIA(Credit: Roberto Clemente)

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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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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.

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:

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Whose fantasy are you living in? Your employer’s or Mark Zuckerberg’s?

A now classical result of the sociology of social networks is the distinction between formal social structures defined by kinship, inherited hierarchy or companies’ organisational charts, and informal structures arising from nets of friendship, trust, solidarity, similarities and dissimilarities. As far back as 1954, John A. Barnes (who incidentally, is credited with coining the wording ‘social networks’) in a renowned study of a small community of fishers in a Norwegian parish demonstrated that exogenously defined positions such as those arising from political administration, economic activity or family are insufficient to explain the social structure of the community, which largely depends on less codified relationships of friendship and acquaintance. In organisational studies, it appeared that the formal chart of a company and the actual networks of advice, trust or communication of members may differ widely, and surveys aimed at eliciting network ties (with ‘name generators’ for example) became a privileged means to bring to light the ‘company behind the chart‘ (Krackhardt & Hanson 1993) and to make ‘invisible work visible‘ (Cross, Parker & Borgatti 2002). Social network scholars advised managers on how, by using employee questionnaires, they could generate network maps and get to the root of many organisational problems. Another major finding was about the emergence of informal roles – the leader, the deviant, the broker – and their important contribution to driving the behaviours and outcomes of human groups, beyond all prescribed, formal authorities (Johnson, Boster & Palinkas 2003).

FormalInformal

The formal chart of a company and the network obtained by asking each employee, “With whom do you discuss work-related issues?” Central individuals (who receive most nominations) are NOT the formal leaders.
 

The research and consultancy activity that built on these ideas had a strong impact on organisational culture worldwide, especially as companies tended to flatten and rely on teams and cross-divisional, project-based work, so that managers’ authority mattered less and understanding these informal networks became a potential key for success. Many would admit today that the organisational chart is the fantasy of the employer, not an actionable tool, and even less so a reliable reflection of reality. But then, what are the advice, trust, and communication networks mapped by the researcher – shouldn’t we say they are the fantasy of the sociologist? These networks are built from questionnaires and therefore rely on the subjective responses of participants; and it is well known in the area of survey design research, that question wording orients responses, that different cultures and groups tend to interpret questions differently, and that people may give biased answers due to forgetting, deliberate concealing of sensitive information, ambiguity of definitions, and diversity in perceptions. The survey is the traditionally primary tool of investigation of the social networks scholar, but brings with it its limitations and distortions.

One may think that the formal organisational chart and the informal advice (or trust or communication) network are just two different ways of construing social structure and objectivating it. They are informed by different political and epistemological orientations: those of (old-style) employers for the former, those of social researchers (and perhaps enlightened employers) for the latter. The resulting formal-informal dichotomy would then be the result of a cleavage between two competing approaches to the management of organisations (and more generally of human groups or communities), one more hierarchical and functional, the other flatter and more collaborative.

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