Digital Scholarship Day of Ideas 2018 – Live Blog

Today I am at the Digital Scholarship Day of Ideas, organised by the Digital Scholarship programme at University of Edinburgh. I’ll be liveblogging all day so, as usual, I welcome additions, corrections, etc. 

Welcome & Introduction – Melissa Terras, Professor of Digital Cultural Heritage, University of Edinburgh

Hi everyone, it is my great pleasure to welcome you to the Digital Day of Ideas 2018 – I’ve been on stage here before as I spoke at the very first one in 2012. I am introducing the day but want to give my thanks to Anouk Lang and Professor James Loxley for putting the event together and their work in supporting digital scholarship. Today is an opportunity to focus on digital research methods and work.

Later on I am pleased that we have speakers from sociology and economic sociology, and the nexus of that with digital techniques, areas which will feed into the Edinburgh Futures Institute. We’ll also have opportunity to talk about the future of digital methods, and particularly what we can do here to support that.

Lynn Jameson – Introduction

Susan Halford is professor of sociology but also director of the institution-wide Web Science Institute.

Symphonic Social Science and the Future of Big Data Analytics – Susan J Halford, Professor of Sociology & Director of Web Science Institute, University of Southampton

Abstract: Recent years have seen ongoing battles between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about the social world, and many social scientists who remain sceptical about the value of big data, its associated methods and claims to knowledge. This talk suggest that we must move beyond this, and offers some possible ways forward. The first part of the talk takes inspiration from a mode of argumentation identified as ‘symphonic social science’ which, it is suggested, offers a potential way forward. The second part of talk considers how we might put this into practice, with a particular emphasis on visualisation and the role that this could play in overcoming disciplinary hierarchies and enabling in-depth interdisciplinary collaboration.

It’s a great pleasure to be here in very sunny Edinburgh, and to be speaking to such a wide ranging audience. My own background is geography, politics, english literature, sociology and in recent years computer sciences. That interdisciplinary background has been increasingly important as we start to work with data, new forms of data, new types of work with data, and new knowledge – but lets query that – from that data. All this new work raises significant challenges especially as those individual fields come from very different backgrounds. I’m going to look at this from the perspective of sociology and perhaps the social sciences, I won’t claim to cover all of the arts and humanities as well.

My talk today is based on work that I have been doing with Mike Savage on “big data” and the new forms of practice emerging around these new forms of data, and the claims being made about how we understand the social world. In this world there has been something of a stand off between data scientists and social scientists. Chris Anderson (in 2008), a writer for Wired, essentially claimed “the data will speak for itself” – you won’t need the disciplines. Many have pushed back hard on this. The push back is partly methodological: these data do not capture every aspect of our lives, they capture partial traces, often lacking in demographic detail (do we care? sociologists generally do…) and we know little of its promise. And it is very hard to work with this data without computational methods – tools for pattern recognition generally, not usually thorough sociological approaches. And present concerning, something ethically problematic, results that are presented as unproblematic. So, this is highly challenging. John Goldthorpe says “whatever big data may have for “knowing capitalism” it’s value to social science has… remained open to questions…”.

Today I want to move beyond that stand out. The divisiveness and siloing of disciplines is destructive for the disciplines – it’s not good for social science and it’s not good for big data analytics either. From a social science perspective, that position marginalises social sciences, sociology specifically, and makes us unable to take part in this big data paradigm which – love it or loathe it – has growing importance, influence, and investment. We have to take part in this for three major reasons: (1) it is happening anyway – it will march forward with or without it; (2) these new data and methods do offer new opportunities for social sciences research and; (3) we may be able to shape big data analytics as the field emerges – it is very much in formation right now. It’s also really bad for data science not to engage with the social sciences… Anderson and others made these claims ten years ago… Reality hasn’t really shown that happen. In commercial contexts – recommendations, behaviour tracking and advertising, the data and analysis is doing that. But in actually drawing understanding from the world, it hasn’t really happened. And even the evangelists have moved on… Wired itself has moved to saying “big data is a tool, but should not be considered the solution”. Jeff Hammerbacker (co-credited for coining the term “data science” in 2008, said in 2013 “the best minds of my generation are thinking about how to make people click ads… that sucks”.

We have a wobble here, a real change in the discourse. We have a call for greater engagement with domain experts. We have a recognition that data are only part of the picture. We need to build a middle ground between those two positions of data science and social science. This isn’t easy… It’s really hard for a variety of reasons. There are bodies buried here… But rather than focus on that, I want to focus on how we take big steps forward here…

The inspiration here are three major social science projects: Bowling Alone (Robert Putnam); The Spirit Level – Richard Wilkinson and Kate Pickett; Capital – Thomas Piketty. These projects have made huge differences, influencing public policy and in the case of Bowling Alone, really reshaped how governments make policy. These aren’t by sociologists. They aren’t connected as such. The connection we make in our paper is that we see a new style of social science argumentation – and we see it as a way that social scientists may engage in data analytics.

There are some big similarities between these books. They are all data driven. Think about sociologists at the end of 20th century was highly theoretical… At the beginning of the 21st century we see data driven works. And they haven’t done their own research generating data here, they have drawn on existing research data. Piketty has drawn together diverse tax data… But also Jane Austen quotes… Not just mixed methods but huge repurposing. These books don’t make claims for causality based on data, their claims for causality is supported by theory. However they present data throughout and supporting their arguments. Data is key, with images to hold the data together. There is a “visual consistency”. The books each have a key graph that essentially summarises the book. Putnam talks about social capital, Piketty talks about the rise and fall of wealth inequality in the 20th century.

In each of these texts data, method and visualisation are woven into a repeat refrain, combined with theory as a composite whole to makes powerful arguments about the nature of social life and social change over the long term. We call this a “Symphonic Aesthetic” as different instruments and refrains build, come in and go… and the whole is greater than the sum of the parts.

OK, thats an observation about the narrative… But why does that matter? We think it’s a way to engage with and disrupt big data. There are similarities: re-purposing multiple and varied “found” data sources; an emphasis on correlation; use of visualistion. There are differences too: theoretical awareness; choice of data; temporality is different – big data has huge sets of data looking at tiny focused and often real time moments. Social Science takes long term comparisons – potentially over 100 years. The role of correlation is different. Big data analytics looks for a result (at least in the early stage), in symphonic aesthetics there is a real interest in correlation through statistical and theoretical understandings. Practice of visualisation varies as well. In big data it is the results, in symphonic aesthetics it is part of the process, not the end of the process.

Those similarities are useful but there is much still to do: symphonic authors do not use new forms of digital data, their methods cannot simply be applied, big data demand new and unfamiliar skills and collaborations. So I want to talk about the prospective direction of travel around data; method; theory; visualisation practice.

So, firstly, data. If we talk about symphonic aesthetics we have to think about critical data pragmatism. That is about lateral thinking – redirection of what data exist already. And we have to move beyond naivety – we cannot claim they are “naturally occurring” mirrors/telescopes etc. They are deliberately social-technical constructions. And we need to understand what the data are and what they are not: socio-technical processes of data construction (eg carefully constructed samples); understanding and using demographic biases (go with the biases and use the data as appropriate, rather than claiming they are representative; or maybe ignore that, look at network construction, flows, mobilities – e.g. John Murrey’s work).

Secondly method. We have to be methodologically plural. Normally we do mixed methods – some quantitative, some qualitative. But most of us aren’t yet trained for computational methods, and that is a problem. Many of the most interesting things about these data – their scale, complexity etc. – are not things we can accommodate in our traditional methods. We need to extend our repertoire here. So social network analysis has a long and venerable history – we can apply the more intensive smaller version of large scale social network analysis. But we also need machine learning – supervised (with training sets) and unsupervised (without). This allows you to seek evidence of different perhaps even contradictory patterns. But also machine learning can help you find the structures and patterns in the data – which you may well not know in data sets at this scale.

We have this quote from Ari Goldberg (2015): “sociologists often round up the usual suspects. They enter the metaphorical crime scene every dat, armed with strong and well-theorised hypotheses about who the murderer should or at least plausibly might be.”

To be very clear I am not suggesting we outsource analysis to computational methods: we need to understand what the methods are doing and how.

Thirdly, theory. We have to use abductive reasoning – a constant interplay between data, method and theory. Initial methods may be informed by initial hunches, themes, etc. We might use those methods to see if there is something interesting there… Perhaps there isn’t, or perhaps you build upon this. That interplay and iterative process is, I suspect, something sociologists already do.

So, how do we bring this all together in practice? Most sociologists do not have a sophisticated understanding of the methods; and most computer scientists may understand the methods but not the theoretical elements. I am suggesting something end to end, with both sociologists and computer scientists working together.

It isn’t the only answer but I am suggesting that visualisation becomes an analytical method, rather than a “result”. And thinking about a space for work where both sociological and computer science expertise are equally valid rather than combatorial. At best visualisations are “instruments for reasoning about quantitative information. Often the most effective way to describe, explore and summarise a set of numbers – even a very large set – is to look at pictures of those numbers” (Tufte 1998). Visualisations as interdisciplinary boundary objects. Beyond a mode of argumentation… visualisation becomes a mode of practice.

An example of this was a visualisation of the network of a hashtag that was collaborative with my colleague Ramin, which developed over time as we asked each other questions about how the data was presented and what that means…

In conclusion, sociology flourished in the C20th. Developing methods, data and theory that gave us expertise in “the social” (a near monopoly). This is changing – new forms of data, new forms of expertise… And claims being made which we may, or may not, think are valid. And that stands on the work of sociologists. But there is some promise in the idea of symphonic aesthetic: for data science – data science has to be credible and there is recognition of that – see for instance Cathy O’Neil’s work on data science, “Weapons of Math Destruction” which also pushes in this direction. ; for sociological research – but not all of it, these won’t be the right methods for everyone; for public sociology – this being used in lots of ways already, algorithm sentencing debates, Cambridge Analytics… There is a real place for sociologists to reshape sociology in the public understanding. There are big epistemological implications here… Changing the data and methods changes what we study… But it has always been like that. Big data can do something different – not necessarily better, but different.


Q1) I was really interested in your comments about visualisations as a method… Joanna Drucker talks about visual technology and visual discourse – and issues of visualisations as being biased towards positivistic approaches, and advocates for getting involved in the design of visualisation tools.

A1) I’m familiar with these concepts. That work I did with Ramin is early speculative work… But it builds and is based on classic social network analysis so yes, I agree, that reflects some issues.

Q2 – Tim Squirrel) I guess my question is about the trade off between access and making meaningful critiques. Often sociology is about critiquing power and methods by which power is transmitted. The more data proliferates, the more the data is locked behind doors – like the kind of data Facebook holds. And in order to access that data you ahve to compromise the kinds of critiques you can make. How do you navigate that narrow channel, to make critiques without compromising those…

Q2) The field is quite unsettled… It looks settled a year ago but I think Cambridge Analytica will have major impact… That may make the doors more closed… Or perhaps we will see these platforms – for instance Facebook – understanding that to retain credibility it has to create a segregation between their own use of the data, and research (not funded by Facebook), so that there is proper separation. But I’m not naive about how that will work in practice… Maybe we have to tread a careful line… And maybe that does mean not being critical in all the ways we might be, in every paper. Empirical data may help us make critical cases across the diverse range of scholarship taking place.

Q3 – Jake Broadhurst) Data science has been used in the social world already, how do we keep up and remain relevant?

A3) It is a pressing challenge. The academy does not have the scale or capacity to address data science in the way the private sector does. One of the big issues is ethics… And how difficult it is for academics to navigate ethics of social media and social data. And it is right that we are bound to ethical processes in a way data scientists and even journalists do not need to. But it is also absolutely right that our ethics committees have to understand new methods, and the realities of the gold standard consent and other options where that is not feasible.

The discussion we are having now, in the wake of Cambridge Analytica, is crucial. Two years ago I’d ask students what data they felt was collected, they just didn’t know. And understanding that is part of being relevant.

Q4 – Karen Gregory) If you were taking up a sociology PhD next year, how would you take that up?

A4) My official response would be that I’d do a PhD in Web Science. We have a programme at University of Southampton, taking students from a huge array of backgrounds, and giving them all the same theoretical and methodological backgrounds. They then have to have 2 supervisors, from at least 2 different disciplines for their PhD.

Q5 – Kate Orton Johnson) How do we tackle the structures of HE that prevent those interdisciplinary projects, creating space, time, collaborative push to create the things that you describe?

A5) It’s a continuous struggle. Money helps – we’ve had £10m from EPSRC and that really helps. UKRI could help – I’m sceptical but hopeful about interdisciplinary possibilities here. Having PhD supervision across really different disciplines is a beautiful thing, you learn so much and it leads to new things. Universities talk about interdisciplinary work but the reality doesn’t always match up. Money helps. Interdisciplinary research helps. Collaboration on small scales – conference papers etc. also help.

Q6 – David, research in AI and Law) I found your comments about dialogues between data scientists and social scientists… How can you achieve similar with law scholars and data scientists… Especially if trying to avoid hierachichal issues. Law and data science is a really interesting space right now… GDPR but also algorithmic accountability – legal aspects of equality, protected categories, etc. Very few users of big data have faced up to the risks of how they use the data, and potential for legal challenge on the basis of discrimination. You have to find joint enthusiasm areas, and fundable areas, and that’s where you have to start.

The Economics Agora Online: Open Surveys and the Politics of Expertise – Tod van Gunten, Lecturer in Economic Sociology, University of Edinburgh

Abstract: In recent years, research centres in both the United States and United Kingdom have conducted open online surveys of professional economists in order to inform the public about expert opinion.  Media attention to a US-based survey has centred on early research claiming to show a broad policy consensus among professional economists.  However, my own research shows that there is a clear alignment of political ideology in this survey.  My talk will discuss the value and limitations of these online surveys as tools for informing the public about expert opinion.

Workshops: Parallel workshop sessions – please see descriptors below.

  • Text Analysis for the Tech Beginner
  • An Introduction to Digital Manufacture – Mike Boyd (uCreate Studio Manager, UoE)
  • ‘I have the best words’: Twitter, Trump and Text Analysis – Dave Elsmore (EDINA)
  • An Introduction to Databases, with Maria DB & Navicat – Bridget Moynihan (LLC, UoE)
  • Introduction to Data Visualisation in Processing – Jules Rawlinson (Music, ECA, UoE)
  • Jupyter Notebooks and The University of Edinburgh Noteable service – Overview and Introduction – James Reid (EDINA)
  • Obtaining and working with Facebook Data – Simon Yuill (Goldsmiths)

Round Table Discussion

  • Melissa Terras, Professor of Digital Cultural Heritage
  • Kirsty Lingstadt, Head of Digital Library and Depute Director of Library and University Collections
    Ewan McAndrew, Wikimedian in Residence
    Tim Squirell, PhD Student, Science, Technology and Innovation Studies