The dynamics and potentials of big data for audience research
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Media, Culture & Society
2018, Vol XXXXXXXXXX –74
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The dynamics and
potentials of big data
for audience research
Adrian Athique
University of Queensland, Australia
Abstract
This article considers the future of audience research in an era of big data. It does so
y inte
ogating the dynamics and potentials of the big data paradigm in an era of user-
generated content and commercial exploitation. In this context, it is proposed that the
major dynamics of big data are a conjoint application of numerology and alchemy in the
information age. On this basis, the potentials of new data techniques are addressed in
light of the critical gap between audience data and the audiences themselves.
Keywords
alchemy, audience research, big data, cultural studies, data mining, numerology,
predictive analysis, social media
Electronics digested a mix of digital numerology and alchemy,
collecting metadata as input to pattern recognition algorithms,
eathing life into a machine capable of doing what
men and women spent a century trying to do
Carvalko (2016: 121).
Audience research has entered the era of ‘big data’, a paradigm emerging from two dec-
ades of innovative and aggressive information management. In the context of this data
arcadia, the need to reconsider our epistemological premise may be less apparent than
the sudden expansion of the methodological toolkit, but it is no less pressing. With this
in mind, my proposition is that we need to consider both the dynamics and the potentials
of such techniques. Media dynamics are determined in this instance as the presumptions,
imperatives and motives that shape the paradigm itself, along with the interaction of
Co
esponding author:
Adrian Athique, Level 4 Forgan Smith Building, The University of Queensland, St Lucia, QLD 4272,
Australia.
Email: XXXXXXXXXX
693681 MCS XXXXXXXXXX/0163443717693681Media, Culture & SocietyAthique
esearch-article2017
Original Article
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60 Media, Culture & Society 40(1)
institutional forces at play in the utilisation of audience data. With the ground set by
those dynamics, the media potentials of big data procedures emerge from both the appli-
cations and the possible outcomes of these techniques. Since audience research is social
esearch, these outcomes have to be understood outside of the computational process,
that is, in terms of likely implications for the operators, clients and subjects of big data.
Indeed, it is fair to assume that audience researchers will themselves occupy any one, or
perhaps all, of these classes in the course of their work. In establishing the dynamics of
ig data, I make the assertion here that the two primary motivations of this paradigm
stem from two longstanding preoccupations of human science, namely numerology and
alchemy. Having, I hope, established this claim I will seek to establish the potentials that
are becoming apparent due to the increasing centrality of audience data in academic
esearch.
User-generated content to self-replicating automata
YouTube (as with other instances of YouMedia) is an exemplar of the digital economy
precisely because it is a medium without any content of its own. It relies upon its users
to supply the value of the service, and to do so freely and without payment (Andrejevic,
2011). User-led systems have become predominant in an era where media production
technologies are cheap and media distribution has been ‘liberated’ from both expert
intermediaries and costs. With the digital future looking highly retrospective and/or
mundane at the level of content, the primary logics of the digital media industries have
centred upon capturing the commercial value of the World Wide Web in other ways. The
answer to the free content conundrum appears to have been found in the unique proper-
ties of the Internet as a medium of record, where every action produces its own data
point. At the turn of the millennium, the introduction of user tracking into web
owsers
and the individual addressability of devices both made it theoretically possible (and per-
haps, more critically, permissible) to identify individual users. Subsequently, the ‘Web
2.0’ project was bankrolled by an Internet-advertising boom for two reasons: First, it
created a vast body of detailed consumer profiles and click trails and second, individual
users could be more effectively targeted with advertising on the basis of this information.
These motivations were reflective of
oader shift in commercial logic, where credit card
companies, Internet service providers (ISPs) and online retailers all woke up to the sec-
ondary usage potentials of their transaction records.
The commercial value of a network system increases exponentially as the user base
expands. In the 2000s, Google’s growing monopoly on basic search facilities left the
company uniquely placed to aggregate profiles of users viewing habits (Halavais, 2009).
This capability allowed Google to aggressively market Internet-advertising and rapidly
ecome one of the world’s largest companies (Levy, XXXXXXXXXXAt the other end of the scale,
user tracking allowed for people to be picked out within that vast space, matching uni-
versal reach with individual addressability. The ‘personal’ look and feel of the concomi-
tant digital culture nonetheless rest upon a faux individuation, given the vast bulk of
actual usage remains centred upon mass-produced commodities and universal formats.
This unprecedented standardisation of YouMedia is itself fundamental, since the under-
lying economics of Web 2.0 rest upon the real-time application of targeted marketing via
Athique 61
automated algorithms. For this to work, it is essential that user-input variables operate in
a recognisable series. That is why the prevailing form of ‘identity’ in digital culture has
een imposed through the clumsy mash-up of the fan survey, dating ad and cu
iculum
vitae (CV) formats. Facebook, like YouTube, effectively categorises human beings on
the basis of the films, music and sport teams that they endorse, while also working hard
to establish where we were born, where we work and who we know (Cheney-Lippold,
2011). Since this is a global system, everyone in the world must conform to this (let us
face it) ridiculous template of human identity as best they can.
Despite the obvious shortcomings of commercially defined digital profiling, it remains
the case that sufficient scale confers numerical value to even the most clumsy survey
instrument. In that respect, the global scale at which contemporary social media plat-
forms operate is inconceivably vast (two billion is an abstract only tangible, perhaps, to
a seasoned quant). Nobody has ever had so much data, nor had it in an individuated and
elational structure so purposefully designed for co
elation. Unlike an earlier era, where
market researchers would examine consumers in direct relation to a given product, the
vast datasets of the social media era are purposefully intended to automatically co
elate
past and potential behaviours in relation to all or any products, activities or actions. This
constitutes something of a tipping point in the informational economy. It became possi-
le only because processing power increased exponentially (as anticipated by Moore’s
law). It became possible only because data storage became inconceivably vast and cheap.
It became possible only because a user-led medium of record removed the need to
employ millions of data entry clerks to capture the data. With this architecture in place,
the engineers of Web 2.0 consciously initialised a chain reaction in the generation of
data. The ultimate output of this vast experiment is a mode of informatics where particu-
lar logics of co
elation can be deployed to create new knowledge from the raw material
(see Zafarani et al., XXXXXXXXXXTo further draw out the analogy between applied nuclear
physics and informational physics, this is the point at which more energy is coming out
of the process than is going into it.
The magic system
For Technorati, whose desired crop is readily comparable data, social information must
e collected somewhat generically. By firmly anchoring user activities to a set of profil-
ing systems, this vast audience is systematically captured as an informational commod-
ity. The potentials of this commodity are largely determined by what we understand data
to be, and this understanding has evolved in co
espondence with the evolution of infor-
mation technology (see Puschmann and Burgess, XXXXXXXXXXIn ancient times, data were
taken as a priori, as something given (whether that was a place, a thing or a point in
time). With the rise of natural sciences, data were reinterpreted as an indexical record of
an established fact. As these facts began to proliferate, and were applied to human sub-
jects by social scientists, these records became the primary resource for governance.
Emerging from this legacy, the binary logics of the computer revolution have subse-
quently redefined data as simply a unit of information and, thus, essentially an integer
entered into or derived from an algorithmic process. In this context, the collection, analy-
sis or manipulation of data becomes a mathematical exercise, regardless of what we want
62 Media, Culture & Society 40(1)
to do with our YouTube dataset on popular culture. For the purposes of the computational
process alone, it is fundamentally i
elevant whether this information being unitised is
about people or
ightly coloured rocks. Nonetheless, in a dynamic system that relies
upon large-scale inputs from its user base, the human contribution to the generative
capacity of numerical data becomes highly significant.
For some years now, a whole series of propositions have been made regarding the
potentials of directing these large numbers towards the resolution of mathematical prob-
lems (Howe, 2009; Shirky, 2008; Surowiecki, XXXXXXXXXXMass participation through a global
interactive system apparently realises one of the major aspirations of computer science:
the advent of infinitely regenerative data. Billions of users continuously inputting numer-
ical sequences that can then be used to generate an effectively infinite series of calcula-
tions promise a mathematical manifestation of Von Neumann’s XXXXXXXXXXself-replicating
automata. Proposals to harvest the value of such large-scale participation for this purpose
often have fantastic motivations, but this is not what I would define as numerology (e.g.
Kurzweil, XXXXXXXXXXThe numerological dimension of big data arises instead from the analy-
sis of numerical trends in those inputs in order to make inferences about the future.
Meteorology and stock market trading are established practices of this kind, largely
determined by mathematical predictions derived from a comprehensive, but tightly
defined, dataset. With the advent of Web 2