media software (Hansard 2011, P Lewis et al. 2011). Facebook and Twitter are both examples of social media; so are media-sharing applications like YouTube and Instagram, direct messaging services like WhatsApp and blogging tools like Tumblr and Medium. Some of the tools have a lot in common while others seem quite different. José van Dijck and Thomas Poell (2013) have developed the notion of social media logic to frame and discuss these similarities. They contrast their social media logic with an established framework developed for mass media communication, and seek to identify grounding principles common to these networked applications. They decide upon four: programmability, popularity, connectivity, and datafication. “The logic of social media, rooted in these grounding principles and strategies, is gradually invading all areas of public life.” (ibid 2).
Social media logic continues to assume an over-arching homogeneity framing different Web technologies, and this remains somewhat problematicxii. The
advantage of social media logic is that the grounding principles are sufficiently specific to permit an analysis of whether these different technologies enable the sort of effects that the authors claim. As they write:
“The quick rise of social media platforms in the first decade of this century was part of a more general networked culture where information and communication got increasingly defined by the affordances of web technologies such as browsers and search engines… Inferring from these conditions, we contend that social media logic refers to the processes, principles, and practices through which these platforms process
information, news, and communication, and more generally, how they channel social traffic.” (ibid 5)
So, for instance, through the application of opaque algorithms to choreograph user interaction with their communicative capabilities, social media technologies shape the relational activities of their users, even though “content is not just programmed by a central agency, even if this agency still has considerable control; users also participate in steering content.” (ibid 6). Similarly, popularity “is conditioned by both algorithmic and socio-economic components” (ibid 7) depending upon the particular characteristics and user groups of individual technologies. The authors describe individual mechanisms for conditioning and promoting popularity for Twitter, Facebook and Google, moving the discussion from a broad social media logic towards more technology-specific investigation.
This sort of approach, in which specific principles are used to construct an over- arching logic, still recognises a network influence, but as an individual force or dynamic within the techno-social construct, rather than as a determining superstructure. The focus then moves to the ability of individual software applications to connect users in ways that might previously not have been possible, rather than on abstract super logics that supposedly transcend the intricacies and affordances of individual technologies.
In developing a definition of software for social research, it is important to note the difference between software and the data that the software produces – which is then captured and studied. van Dijck and Poell call datafication: “the ability of
and explains both the interest for many social scientists in Internet technologies but also many of the approaches taken to studying these media. While the principle of datafication may be useful in terms of constructing a social media logic, it should be distinguished from big data.
Big data has a narrow technological definition from computer science – datasets large enough to require super-computer processing (Manovich 2011) – and a much broader, fuzzier definition from the social sciences and Internet-affiliated commentary. Big data is the notion that datafication enables new approaches to analysis and new forms of empirics based, essentially, on a principle of “total knowledge” (Bowker 2014). It “seems to combine the grand scale and generalizability of methods like national surveys with the granularity and detail of close textual analysis, ethnography, or participant observation.” (Driscoll and Walker 2014, 1746).
The concept is deceptively simple: users supply applications like Facebook and Google with all sorts of information about themselves, and this interaction creates yet more information about communication patterns and preferences. All of this information is recorded inherently by the applications, processed algorithmically and typically used to modify or improve the function of those applications – as well as to monetise users for advertisers. The added ability to store this information long-term in massive databases and to automate many forms of investigation has led to some dramatic claims about the changing nature of knowledge and enquiry. One commentator declared the “end of theory” as big data rendered statistical necessities like sampling and extrapolation obsolete (Anderson 2008). Bowker (2014, 1795) explains that “we are moving
from the knowledge/power nexus portrayed by Foucault to a data/action nexus that does not need to move through theory: All it needs is data together with preferred outcomes.”
Big data, though, is another term that can obscure more than it reveals, including meanings and assumptions that are both value-loaded and debatable. boyd and Crawford (2011, 4) complain that such thinking betrays an “arrogant undercurrent in many Big Data debates where all other forms of analysis can be sidelined by production lines of numbers, privileged as having a direct line to raw knowledge.” Such thinking denies subjectivity, makes unsupportable claims to objectivity, and siphons off knowledge creation to an array of unknown algorithms that may be complex but cannot be value-neutral. Furthermore, it engenders a type of technological solutionsim – the ideology that any problem, social or personal, can be solved by collecting sufficient data and promoting algorithmically-derived efficiencies:
“Recasting all complex social situations either as neatly defined problems with definite, computable solutions or as transparent and self-evident processes that can be easily optimized—if only the right algorithms are in place!—this quest is likely to have unexpected consequences that could eventually cause more damage than the problems they seek to address.” (Morozov 2013b, 5)
It has been a huge boon to the social sciences to discover that so much data is both rich in detail and relatively accessiblexiii. Indeed, it might be suggested that,
ethical or a critical perspective. There is also a risk that data accessibility directs research towards certain technologies above others, and that this happens irrespective of critical interest in the technology.
In this thesis, for example, there are good reasons for concentrating analysis on Twitter, because it enables a fluid and open form of communicative exchange that is particularly of interest, but it must also be acknowledged that it remains comparatively easy to ask for and to retrieve data from Twitter’s application programming interface (API). A search for “Twitter” on Google Scholar returns nearly five million results, and yet the actual number of Twitter users represents a tiny fraction of the global population, and is concentrated mainly in the US and Europe. The nature of that data too – and it is not an objective form; data is defined by programming choices and released discriminately – must also be considered carefully. As Driscoll and Walker (2014, 1747) explain: “The ontology of native Twitter objects is subject to change without warning, and different data sources provide tweets in entirely different formats.”
Nevertheless, many researchers are taking advantage of datafication to produce analysis that is informed, nuanced and insightful. A full summary of that research is not possible; the scope and volume of research is simply too great. Certain applications are better represented than others, either because they are popular with users or because they are accessible and subject well to analysis. A search of the literature reveals that two software applications in particular have dominated research efforts.
through its desktop website or smartphone client – every day (Statista 2015a). Twitter has 284 million users active each month – again, according to the company itself – three-quarters of whom are based outside the US (Twitter 2015a). In terms of the global population, perhaps, these numbers are not so impressive, but these two companiesxiv (Facebook, especially) dominate the
social media landscapexv and, it must be said, the attention of the academic
community. The overwhelming majority of big data research papers – quantitative efforts to explore an emergent, global communication technology – focus on two companies who are based a short drive from each other in San Francisco.
On the whole, papers that concentrate solely on Facebook are not reviewed here unless they offer propositions or insights that are relevant and common across the social media categorisation. The focus in this thesis is on Twitter and its role in socio-political meaning-making. For reasons that will become clear, Twitter is regarded as an archetype: a software bundle that, in many ways, reflects the utopian logics of Internet connectivity (Lewis 1998).
The Twitter code base is partly available for public inspection and partly proprietary (Vaughan-Nichols 2012)xvi. It is clear, however, that to define Twitter
as “an object in the world” involves far more than inspecting the code base – it requires a close study of how that code base shapes affordances into logics and how those logics shape social action.