(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 Simmel (1895, p. 54) argued that the most important task for the sociologist is to separate analytically the form of social life from its content, even though the two are in reality inseparably united. The aim of the analysis must be to detach the forms from their contents and to bring them together systematically: ‘For it is evident that the same form […] can arise in connection with the most varied elements.’ Simmel continued to explain that:
We find, for example, the same forms of authority and subordination, of competition, imitation, opposition, division of labor, in social groups which are the most different possible.
(Simmel, 1895, p. 55)
Let us assume, to take but one example, that we were to establish empirically that people on social media sometimes find themselves disillusioned by their own social media use, and that they feel as if they are just like cogs in a bigger machine beyond their individual control. Let us also assume that our analysis made us think that this may even be a form of oppression or exploitation, where social media conglomerates make a profit from what disillusioned and exploited users post online. We may simply invent a new flashy theoretical concept for this, say: ‘digital brainwash’ or ‘social media disconnect’. But we could also make the effort of going back to already established social theories. In my present example, a good option may have been Karl Marx’s 1844 theory about alienation (Marx, 1844, pp. 69–84). The social form of alienation, in that case, may transcend the contexts of nineteenth-century industrial capitalism and social life on the twenty-first-century internet. Once we see that, we also enable other insights such as, for example, that our present-day society may still be quite similar in some respects to nineteenth-century industrial capitalism.
I do not mean to say that such theoretical connections are not already made by many scholars, nor do I mean that anyone who does not do it at every opportunity is lazy or wrong. I myself am a repeat offender. And, conversely, it may indeed sometimes be a good idea actually to invent new concepts – how else would theories develop? – and in most cases there needs to be some sort of updating or modification of the old theory that is re-employed. On the one hand, this book is an explicit effort to explore and show how to apply existing, trusty, and well-worn social theory systematically, through data science, to social media politics with this kind of ambition and aspiration. On the other hand, the book is just as much an encouragement to combine and re-invent theories in eclectic ways. I will return, throughout the book, to issues of theory, as universal truth versus theory, as emergent and constantly renegotiated.
A bit of anarchy
Data
