Data Theory. Simon Lindgren

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СКАЧАТЬ enfant terrible of the philosophy of science, wrote on the ‘complexity of human change’ and on the ‘unpredictable character of the ultimate consequences’ of people’s actions. ‘Are we really to believe’, he asked, ‘that the naive and simple-minded rules which methodologists take as their guide are capable of accounting for such a maze of interactions?’ (Feyerabend, 1975, p. 9). This question is today more relevant than ever, as datafication affects not only research practices, but also society as such, and thereby the very object of study of our research. Social expressions in the age of the internet are fragmented and entangled, in a system of platforms and relations enabled through digital technologies.

      As argued by Nick Couldry and Andreas Hepp (2017), we now live in an age of deep mediatisation, where media can no longer be seen as specific channels of centralised content. Rather, media are now better understood as platforms for enacting social life (Dijck, Poell, and Waal, 2018). This is symptomatic of a transition from a mass media system to a social media ecology. The transformation has been described in terms of a rise of ‘mass self-communication’ (Castells, 2009), ‘networked individualism’ (Rainie and Wellman, 2012), and ‘connective action’ (Bennett and Segerberg, 2012). In sum, such perspectives argue that politics, opinions, and ideas, as well as social life in general now function in accordance with a much more decentralised and democratic logic (Ito, 2008), but also in more volatile and ‘viral’ ways (Sampson, 2012). This represents something much more than a mere technological transition. Following ongoing processes of digitalisation and datafication, our social world is suffused with technological media of communication that bring about a refiguring of the world in, and on, which we act. As argued by Couldry and Hepp (2017), social relations today are actualised through a system of variously connected digital platforms, that bring about a much more intense embedding of media in social processes than was ever the case before. Now there is a need to adapt social science theories and methods in hybrid ways to better account for this situation.

The digital society has been characterised as a ‘wicked system’ (Törnberg, 2017, p. 52), the analysis of which demands a critical methodological pluralism. In fact, most social systems have this emergent property of wickedness to some degree – a ‘combination of complexity and complicatedness that entails plasticity and deep ontological uncertainty’ (Törnberg, 2017, p. 25). In the specific case of social media and politics, internet researcher Helen Margetts and her colleagues argue that ‘social media are a source of instability and turbulence in political life’, which creates an uncertain environment (Margetts et al., 2017, p. 74). They suggest that:

      Online platforms exhibit what other people are doing in real time and make other people aware of what they themselves are doing, creating feedback loops and chain reactions that draw in more people, whose actions in turn are likely to influence others. It seems reasonable to claim that mobilizations formed in this way are vulnerable to the impulses from which they start, which can push them over into critical mass, or cause them to fade and die almost as soon as they appear, making them hard to understand or predict.

      (Margetts et al., 2017, p. 74)

      As these authors argue, there is indeed a complexity (and complicatedness) of factors, levels, forces, and influences involved, at all levels of the social – especially in the digital society. And this book, in essence, is about approaching this complexity analytically, with a theoretical and methodological openness that can account for this turbulent, wicked, anarchistic, and ambivalent nature.

Datafication

      The ongoing development of the internet and social media increasingly transforms our lives into data. Vast amounts of information about individuals and their interactions are being generated and recorded – directly and indirectly – voluntarily and involuntarily – for free and for profit. These volumes of data offer unforeseen and exciting opportunities for social research. It is because of this that we have witnessed in recent years the rise of the much-hyped phenomenon of big data. Alongside this development, computational methods have become increasingly popular also in scholarly areas where they have not been commonly used before.

’Big data’ refers broadly to the handling and analysis of massively large datasets. According to a popular definition, big data conforms with three Vs. It has volume (enormous quantities of data), velocity (is generated in real-time), and variety (can be structured, semi-structured, or unstructured). Various writers and researchers have suggested a number of other criteria be added to this, such as exhaustivity, relationality, veracity, and value. Big data has indeed been a mantra in the fields of commercial marketing and political campaigning throughout the last decade. High hopes and strong beliefs have been connected with how these new types of data – enabled by people’s use of the internet, social media, and technological devices – might be collected and analysed to generate knowledge about how to get people to click on adverts, or to buy things or ideas. Similar methods are also becoming more and more used in fields such as healthcare and urban planning.

      All of this is a consequence of what can be called the datafication of social life. This is what happens when ‘we have massive amounts of data about many aspects of our lives, and, simultaneously, an abundance of inexpensive computing power’ (Schutt and O’Neil, 2013, p. 4). Also beyond the internet and social media, there has been an increased influence of data into most industries and sectors. There has been huge interest, and many efforts made, to try to extract new forms of insight and generate new kinds of value in a variety of settings. As explained on Wikipedia (2018), lately ‘the term “big data” tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set’. As underlined by internet researchers Kate Crawford and danah boyd, ‘big data’ is in fact a poorly chosen term. This is because its alleged power is not mainly about its size, but about its capacity to compare, connect, aggregate, and cross-reference many different types of datasets (that also often happen to be big). They define big data as:

a cultural, technological, and scholarly phenomenon that rests on the interplay of: (1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets. (2) Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims. (3) Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.

      (boyd and Crawford, 2012, p. 664)

      From a critically sociological perspective, Lupton (2014, p. 101) argues that the hype that surrounds the new technological possibilities afforded by big data analytics contribute to the belief that such data are ‘raw materials’ for information – that they contain the untarnished truth about society and sociality. In reality, each step of the process in the generation of big data relies on a number of human decisions relating to selection, judgement, interpretation, and action. Therefore, the data that we will have at hand are always configured via beliefs, values, and choices that ‘“cook” the data from the very beginning so that they are never in a “raw” state’. So, there is no such thing as raw data, even though the orderliness of neatly harvested and stored big datasets can create an illusion to the contrary.

      Sociologist David Beer (2016, p. 149) argues that we now live in ‘a culture that is shaped and populated with numbers’, where trust and interest in anything that cannot be quantified diminishes. Furthermore, in the age of big data, there is an obsession with causation. As boyd and Crawford (2012, p. 665) argue, the mirage and mythology of big data demand that a number of critical questions are raised with regard to ‘what all this data means, who gets access to what data, how data analysis is employed, and to what ends’. There is a risk that the lure of big data will sideline other forms of analysis, and that other alternative methods with which to analyse the beliefs, choices, expressions, and strategies of people are pushed aside by the sheer volume of numbers. ‘Bigger data are СКАЧАТЬ