The methodology of a multi-model project examining how Facebook infrastructures social relations

It is the purpose of this paper to make explicit the methodology (the
theory of the methods) by which we conducted research for an
Economic and Social Research Council-funded research project on
the relationship of values to value. Specifically, we wanted to study
the imperative of Facebook to monetize social relationships, what
happens when one of our significant forms of communication is
driven by the search for profit, by the logic of capital. We therefore
wanted to ‘get inside’ and understand what capital’s new lines of
flight, informationally driven models of economic expansion, do to
social relations. Taking up the challenge to develop methods
appropriate to the challenges of ‘big data’, we applied four different
methods to investigate the interface that is Facebook: we designed
custom software tools, generated an online survey, developed data
visualizations, and conducted interviews with participants to discuss
their understandings of our analysis. We used Lefebvre’s [(2004).
Rhythmnanalysis: Space, time and everyday life. London: Continuum]
rhythmanalysis and Kember and Zylinska’s [(2012). Life after new
media: Mediation as a vital process. Cambridge, MA: MIT Press] ideas
about ‘lifeness’ to inform our methodology. This paper reports on a
research process that was not entirely straightforward. We were
thwarted in a variety of ways, especially by challenge to use
software to study software and had to develop our project in
unanticipated directions, but we also found much more than we
initially imagined possible. As so few academic researchers are able
to study Facebook through its own tools (as Tufekci [(2014). Big
questions for social media big data: Representativeness, validity and
other methodological pitfalls. In ICWSM ‘14: Proceedings of the 8th
International AAAI Conference on Weblogs and Social Media
(pp. 505–514)] notes how, unsurprisingly, at the 2013 ICWSM only
about 5% of papers were about Facebook and nearly all of these
were co-authored with Facebook data scientists), we hope that our
methodology is useful for other researchers seeking to develop less
conventional research on Facebook.