Posts Tagged ‘ Social network data ’

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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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:

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

Fig1Network 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 nowSocial-Media-Network 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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Small Data to study the Web: The ANAMIA project

We have just published the results of our research project ANAMIA, studying the personal networks and online interactions of persons with eating disorders (“ana” and “mia” in web jargon). The report has just come out:

Documents

Report: Young internet users and eating disorder websites: beyond the notion of “pro-ana” (pdf, 92 pp, in French)

Infographic: results and recommendations of the ANAMIA project (pdf, in French)

Summary (in English!)

The ana-mia webosphere had remained opaque for long, with little data available for a science-based understanding of it. As a result, misconceptions proliferated and policy-makers hesitated — threatening censorship but without devising solutions to reach out and support a population in distress. Our study has been the first to overcome these limitations and reveal the social environment, actual eating practices and digital usages of persons with eating disorders in the English and French web.

Fig1

Visualization of the personal networks of four individuals with, respectively, EDNOS (Eating Disorders Not Otherwise Specified, top panel, left), anorexia nervosa (top, right), bulimia nervosa (bottom, left), binge eating (bottom right). Hollow circles represent their face-to-face acquaintances, filled circles their online ones. Colours indicate relational proximity to the subject (green: intimate, blue: very close, yellow: close, red: somewhat close). Source: ANAMIA project report.

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Three tools to visualize personal network data – continued

Yesterday, Antonio Casilli and I gave our promised talk on network data visualization. It was an opportunity to discuss the extension of the tools we developed within a given research project to other network studies, and to reflect on the contribution as well as the limitations of data visualizations. Here are our slides:

Three tools to visualize personal networks

Data visualization techniques are enjoying ever greater popularity, notably thank to the recent boom of Big Data and our increased capacity to handle large datasets. Network data visualization techniques are no exception. in fact, appealing diagrams of social connections (sociograms) have been at the heart of the field of social network analysis since the 1930s, and have contributed a lot to its success. Today, all this is evolving at unprecedented pace.

In line with these tendencies, the research team of the project ANAMIA (a study of the networks and online sociability of persons with eating disorders, funded by the French ANR) of which I was one of the investigators, have developed new software tools for the visualization of personal network data, with different solutions for the three stages of data collection, analysis, and dissemination of results.

Specifically:

– ANAMIA EGOCENTER is a graphical version of a name generator, to be embedded in a computer-based survey to collect personal network data. It has turned out to be a user-friendly, highly effective interface for interacting and engaging with survey respondents;

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