Data Theory. Simon Lindgren
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of theoretical grounding. In a seminal paper outlining the emerging discipline of ‘computational social science’, David Lazer and colleagues wrote that:
We live life in the network. We check our e-mails regularly, make mobile phone calls from almost any location, swipe transit cards to use public transportation, and make purchases with credit cards. Our movements in public places may be captured by video cameras, and our medical records stored as digital files. We may post blog entries accessible to anyone, or maintain friendships through online social networks. Each of these transactions leaves digital traces that can be compiled into comprehensive pictures of both individual and group behavior, with the potential to transform our understanding of our lives, organizations, and societies.
(Lazer et al., 2009, p. 721)
Furthermore, they argued that there was an inherent risk in the fact that existing social theories were ‘built mostly on a foundation of one-time “snapshot” data’ and that they therefore may not be fit to explain the ‘qualitatively new perspectives’ on human behaviour offered by the ‘vast, emerging data sets on how people interact’ (Lazer et al., 2009, p. 723). While I agree that social analysis must be re-thought in light of these developments, I am not so sure that it is simply about ‘compiling’ the data, and then being prepared that existing theories may no longer work. Rather, I argue, we should trust a bit more that even though the size and dynamics of the data may be previously unseen, the social patterns that they can lay bare – if adequately analysed – can still largely be interpreted with the help of ‘old’ theories, and with an ‘old’ approach to theorising. After all, theories are not designed to understand particular forms of data, but instead the sociality to which they bear witness.
My point is that data need theory, for considering both the data, the methods, the ethics, and the results of the research. By extension, still, theories may always need to be updated, revised, discarded, or newly invented – but that has always been true. As such, this book is therefore positioned within the broad field of ‘digital sociology’ as outlined by authors such as Deborah Lupton (2014) and Noortje Marres (2017). One strand within the debate about what digital sociology is, and what it entails, relates to the emergence of ‘digital methods’. In general, there is widespread disagreement about what such methods are, and whether there should be a focus on continuity with established social research traditions, or on revolutionary innovation. In a sense, this book can be read as one out of many possible ventures in the direction pointed out by Noortje Marres when she writes:
The digitization of social life and social research opens up anew long-standing questions about the relations between different methodological traditions in social enquiry: what are the defining methods of sociological research? Are some methods better attuned to digital environments, devices and practices than others? Do interpretative and quantitative methods present distinct methodological frameworks, or can these be combined?
(Marres, 2017, p. 105)
With co-author Caroline Gerlitz, Marres suggests that we go beyond previous divisions of methods by thinking in terms of ‘interface methods’ (Marres and Gerlitz, 2016). This means highlighting that digital methods are dynamic and under-determined, and that a multitude of methodologies are intersecting in digital research. By recognising ‘the unstable identity of digital social research techniques’, we can ‘activate our methodological imagination’ (Marres, 2017, p. 106). Marres continues to say that:
Rather than seeing the instability of digital data instruments and practices primarily as a methodological deficiency, i.e. as a threat to the robustness of sociological data, methods and findings, the dynamic nature of digital social life may also be understood as an enabling condition for social enquiry.
(Marres, 2017, p. 107)
In this book, I suggest a general stance by which more integrated methodologies can be developed and propagated. Writing from my own personal position as a social media researcher and cultural sociologist, I will present an argument that the data-drivenness of big data science does not in essence need to be conceived as being different from the data-drivenness of ethnography and anthropology. My end goal is to outline a framework by which theoretical interpretation and a ‘qualitative’ approach to data is integrated with ‘quantitative’ analysis and data science techniques.
Verstehen and Evidenz
The book, in the end, is especially focused on what interpretive sociology can bring to the table here. With this concept I refer to the classic notion of sociology as ‘a science concerning itself with the interpretive understanding of social action […] its course and consequences’ (Weber, [1921] 1978, p. 4). This kind of sociology is about the understanding (Verstehen) of social life and has a focus on processes of how meaning is created through social activities. In other words, it is not a positivist and objectivist science. As Max Weber put it, ‘meaning’ never refers:
to an objectively ‘correct’ meaning or one which is ‘true’ in some metaphysical sense. It is this which distinguishes the empirical sciences of action, such as sociology and history, from the dogmatic disciplines in that area […] which seek to ascertain the ‘true’ and ‘valid’ meanings associated with the objects of their investigation.
(Weber, [1921] 1978, p. 4)
Still, he continued, interpretive sociology ‘like all scientific observations, strives for clarity and verifiable accuracy of insight and comprehension (Evidenz)’ (Weber, [1921] 1978, p. 4). The interpretive stance should entail moving back and forth between such evidence – data – and their iterative and cumulative interpretation – theory.
Empirically speaking, this is a book about social media politics (see Chapter 2). In a set of different case studies, it will say things about how social media are used today for various political ends, under which circumstances, and to what effects. The underlying and driving scholarly aim of the book, however, is more methodological, and is about developing an analytical approach for bringing together the Verstehen and the Evidenz in general, and social theory and data science in particular. This agenda, rather than any one core research question about social media politics, is the main driving force through the chapters that follow.
I wrote this book as a reminder that, also (or maybe especially) in the age of datafication, data (still) need theory, and theory (still) needs data. The book provides a suggestion as to how one may conceptualise and do research that aligns with that insight. The chapters in this book include theoretical and methodological discussions, as well as a number of explorative and experimental case studies, focused on how social media politics can be analysed based on these premises. Ultimately, the book presents an approach that, while being data-driven and making use of social media data, and computational data science techniques, is still firmly set within a theoretically sensitive and sociologically interpretive framework of analysis.
Theories old and new
Sociological theory, and often such theories that were developed in the pre-digital age, can contribute immensely to our understanding of things that we are now in the process of, maybe unnecessarily, inventing new names for: ‘viral communication’, ‘user-generated content’, ‘the blogosphere’, ‘online hate’, ‘cyber bullying’, and so on. I do not mean that such words, at least not all of them, are merely superfluous synonyms for things that we already have names for. Nor do I claim that any old theory is always better than a new one, or that such old theories can be applied unproblematically to twenty-first-century phenomena without modification. But, in many cases, we run the infamous risk of throwing the baby out with the bath water. When researching the peculiarities and novelties of interaction and communication in the datafied society, we risk mistaking theories about general patterns of social life as being obsolete just because they were developed in non-digital contexts.
The already established theories are useful because, even though settings change, we may often be dealing with the same underlying social forms as before. Georg