The myth of ‘us’ in a digital age

In his A necessary disenchantment: myth, agency and injustice in a digital worldNick Couldry argues that transitions in media infrastructure are facilitating the emergence of a new myth of collectivity:

A new myth about the collectivities we form when we use platforms such as Facebook. An emerging myth of natural collectivity that is particularly seductive, because here traditional media institutions seem to drop out altogether from the picture: the story is focused entirely on what ‘we’ do naturally, when we have the chance to keep in touch with each other, as of course we want to do.

http://onlinelibrary.wiley.com/doi/10.1111/1467-954X.12158/abstract

This is coming to replace an older sense of media as the point of access to the centre of society. The reliance on media organisations to access flows of content helped constitute an understanding of centre and periphery, with the media facilitating access to the (mythical) centre of value, knowledge and meaning for the majority who experienced themselves as peripheral to it. The rapid diffusion of the internet, mobile computing and social networking engenders a new form of mediation, by ‘us’ rather than content producing media organisations, which helps shatter this previous myth of the ‘mediated centre’ and substitute it with a vision of human networks, animated by natural sociability, dispersed across national boundaries. As I understand Couldry’s argument, the power of this new myth derives in part from its displacement of the old: once our reliance on the old media organisations is seen to be shattered, our sociality is unbound, revealing a naturally co-operative inclination towards discussion, creation and sharing (see for example Clay Shirky’s theory of ‘cognitive surplus’). Obviously, the perception is erroneous and it serves vested interests: media organisations haven’t ceased to be party to communication, either in the sphere of content-production or facilitating communication, it’s only that their role has shifted with a change in the logic of their competition. This obfuscation serves the interests of platform providers in particular, as they drift towards being seen solely in terms of the provision of infrastructure rather than as corporate actors with increasingly vast lobbying operations.

Couldry’s concern is that “we must be wary when our most important moments of ‘coming together’ seem to be captured in what people happen to do on platforms whose economic value is based on generating just such an idea of natural collectivity”. Social media platforms present themselves as providing new enablements for and eliminating old constraints upon ‘natural collectivity’: their business model simultaneously relies upon monetizing the crowd which they have encouraged to gather, profiling behaviour in a manner susceptible to inference and allowing the growing data mining industry to do further work to this end. Their concern becomes less a matter of reaching as many people with adverts as possible (on occasions of mass attention driven by shared spectacle) but reaching the right people all the time. This is why ‘big’ data analytics are so tied up in the broader transformation of the media: the process itself demands innovation in order to extract the value it promises to generate. However this genuine computational challenge, as well as the economic interests which partly drive it, stand obscured behind the ‘myth of big data’ which Couldry takes aim at:

Myth works, as I’ve often argued following Maurice Bloch (1989) and Roland Barthes (1972), through ambiguity: through sometimes claiming to offer ‘truth’ and at other times to be merely playful, providing what, in the George W. Bush era, was called ‘plausible deniability’, but here at the level of claims about knowledge claims! So Mayer-Schonberger and Cukier, on the one hand, say big data bring ‘an  essential enrichment in human comprehension’ (2013: 96). They go further, proposing a large project of ‘datafication’ that involves quantifying every  aspect of everyday phenomena to enable big data analysts to find its hidden order: the result will be ‘a great infrastructure project’ like Diderot’s 18th- century encyclopaedia: ‘this enormous treasure chest of datafied information . . . once analysed, will shed light on social dynamics at all levels, from the individual to society at large’ (2013: 93–94, emphasis added). The world too will look different: ‘we will no longer regard our world as a string of happenings that we explain as a natural or social phenomenon, but as a universe comprised essentially of information’ (2013: 96, emphasis added). On the other hand, when the moral consequences of acting on the basis of ‘big data’ arises – for example, arresting people for offences they are predicted to commit but haven’t yet – they back off and say that big data only provide probabilities, not actualities, and worry about ‘fetishizing the output of our [data] analysis’ (2013: 151)

http://onlinelibrary.wiley.com/doi/10.1111/1467-954X.12158/abstract

It’s the final points which will be so crucial to understanding the trajectory of ‘big data’ in a social world rapidly acclimatising itself to these forms of intervention. The mythical sociability of ‘us’ stands in sharp contrast to the quantity and quality of the interventions we are potentially susceptible to in virtue of our participation in (digitised) social life: we stand exposed, fragmented and scrutinised before a diffuse and inscrutable power. Under these circumstances might we come to cling to the myth more tightly than ever for the security it provides? As Couldry points out in relation to big data, “we too are involved in its reproduction, supplying information (to government and countless other collectors, including social media platforms) about what we do, as we do it, allowing that information to supplant other possible types of information about ourselves, what we say, and how we reflect”. He goes on to call for an ethical engagement with these questions and the implications that they have for the social order:

The CEO of a big-data-based sentiment analysis company, sounds reasonable when he says that ‘if we’re right 75% to 80% of the time, we don’t care about any single story’ (quoted Andrejevic, 2013: 56). 4 . 4 But if the big data model works by equating our only forms of social knowledge with such probabilities, then we have already started organizing things so that the single story – your story,my story – really doesn’t matter. That raises fundamental questions about individual voice, and the way voice is valued in our societies.

http://onlinelibrary.wiley.com/doi/10.1111/1467-954X.12158/abstract

He doesn’t develop the point but it strikes me there’s a contradiction between the myth of ‘us’ and the myth of big data which could provide a focal point for resistance. In reality, the networked ‘us’ makes ‘big data’ possible. However symbolically, the reality of big data serves to negate the imagined promise of the ‘us’: can we reclaim an impulse towards networked sociality and co-operation in a way that resists corporate capture? Could the very force of the myth of ‘us’ be something that can be drawn upon to mobilise resistance to a world in which, as Couldry puts it, “corporate interests and the state seek to know us through big data”?


Categories: Digital Sociology

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