2(2) 2016: Politics of Big Data

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Big Data and the Paradox of Diversity
Rieder, Bernhard (2016) , S. 39-54
This paper develops a critique of Big Data and associated analytical techniques by focusing not on errors – skewed or imperfect datasets, false positives, underrepresentation, and so forth – but on data mining that works. After a quick framing of these practices as interested readings of reality, I address the question of how data analytics and, in particular, machine learning reveal and operate on the structured and unequal character of contemporary societies, installing “economic morality” (Allen 2012) as the central guiding principle. Rather than critiquing the methods behind Big Data, I inquire into the way these methods make the many differences in decentred, non-traditional societies knowable and, as a consequence, ready for profitable distinction and decision-making. The objective, in short, is to add to our understanding of the “profound ideological role at the intersection of sociality, research, and commerce” (van Dijck 2014: 201) the collection and analysis of large quantities of multifarious data have come to play. Such an understanding needs to embed Big Data in a larger, more fundamental critique of the societal context it operates in.
From Data Analytics to Data Hermeneutics. Online Political Discussions, Digital Methods and the Continuing Relevance of Interpretive Approaches
Gerbaudo, Paolo (2016) , S. 95-111
To advance the study of digital politics it is urgent to complement data analytics with data hermeneutics to be understood as a methodological approach that focuses on the interpretation of the deep structures of meaning in social media conversations as they develop around various political phenomena, from digital protest movements to online election campaigns. The diffusion of Big Data techniques in recent scholarship on political behavior has led to a quantitative bias in the understanding of online political phenomena and a disregard for issues of content and meaning. To solve this problem it is necessary to adapt the hermeneutic approach to the conditions of social media communication, and shift its object of analysis from texts to datasets. On the one hand, this involves identifying procedures to select samples of social media posts out of datasets, so that they can be analysed in more depth. I describe three sampling strategies – top sampling, random sampling and zoom-in sampling – to attain this goal. On the other hand, “close reading” procedures used in hermeneutic analysis need to be adapted to the different quality of digital objects vis-à-vis traditional texts. This can be achieved by analysing posts not only as data-points in a dataset, but also as interventions in a collective conversation, and as utterances of broader “discourses”. The task of interpretation of social media data also requires an understanding of the political and social contexts in which digital political phenomena unfold, as well as taking into account the subjective viewpoints and motivations of those involved, which can be gained through in-depth interviews, and other qualitative social science methods. Data hermeneutics thus holds promise for a closing of the gap between quantitative and qualitative approaches in the study of digital politics, allowing for a deeper and more holistic understanding of online political phenomena.
Introduction. Politics of Big Data
Coté, Mark; Gerbaudo, Paolo; Pybus, Jennifer (2016) , S. 5-15
The Alternative Epistemologies of Data Activism
Milan, Stefania; van der Velden, Lonneke (2016) , S. 57-74
As datafication progressively invades all spheres of contemporary society, citizens grow increasingly aware of the critical role of information as the new fabric of social life. This awareness triggers new forms of civic engagement and political action that we term “data activism”. Data activism indicates the range of sociotechnical practices that interrogate the fundamental paradigm shift brought about by datafication. Combining Science and Technology Studies with Social Movement Studies, this theoretical article offers a foretaste of a research agenda on data activism. It foregrounds democratic agency vis-à-vis datafication, and unites under the same label ways of affirmative engagement with data (“proactive data activism”, e. g. databased advocacy) and tactics of resistance to massive data collection (“reactive data activism”, e. g. encryption practices), understood as a continuum along which activists position and reposition themselves and their tactics. The article argues that data activism supports the emergence of novel epistemic cultures within the realm of civil society, making sense of data as a way of knowing the world and turning it into a point of intervention and generation of data countercultures. It offers the notion of data activism as a heuristic tool for the study of new forms of political participation and civil engagement in the age of datafication, and explores data activism as an evolving theoretical construct susceptible to contestation and revision.
Visual Social Media and Big Data. Interpreting Instagram Images Posted on Twitter
Murthy, Dhiraj; Gross, Alexander; McGarry, Marisa (2016) , S. 113-133
Social media such as Twitter and Instagram are fast, free, and multicast. These attributes make them particularly useful for crisis communication. However, the speed and volume also make them challenging to study. Historically, journalists controlled what/how images represented crises. Large volumes of social media can change the politics of representing disasters. However, methodologically, it is challenging to study visual social media data. Specifically, the process is usually labour-intensive, using human coding of images to discern themes and subjects. For this reason, Studies investigating social media during crises tend to examine text. In addition, application programming interfaces (APIs) for visual social media services such as Instagram and Snapchat are restrictive or even non-existent. Our work uses images posted by Instagram users on Twitter during Hurricane Sandy as a case study. This particular case is unique as it is perhaps the first US disaster where Instagram played a key role in how victims experienced Sandy. It is also the last major US disaster to take place before Instagram images were removed from Twitter feeds. Our sample consists of 11,964 Instagram images embedded into tweets during a twoweek timeline surrounding Hurricane Sandy. We found that the production and consumption of selfies, food/drink, pets, and humorous macro images highlight possible changes in the politics of representing disasters – a potential turn from top-down understandings of disasters to bottom-up, citizen informed views. Ultimately, we argue that image data produced during crises has potential value in helping us understand the social experience of disasters, but studying these types of data presents theoretical and methodological challenges.
What Counts? Reflections on the Multivalence of Social Media Data
Gerlitz, Carolin (2016) , S. 19-38
Social media platforms have been characterised by their programmability, affordances, constraints and stakeholders – the question of value and valuation of platforms, their data and features has, however, received less attention in platform studies. This paper explores the specific socio-technical conditions for valuating platform data and suggests that platforms set up their data to become multivalent, that is to be valuable alongside multiple, possibly conflicting value regimes. Drawing on both platform and valuation studies, it asks how the production, storing and circulation of data, its connection to user action and the various stakeholders of platforms contribute to its valuation. Platform data, the paper suggests, is the outcome of capture systems which allow to collapse action and its capture into pre-structured data forms which remain open to divergent interpretations. Platforms offer such grammars of action both to users and other stakeholders in front- and back-ends, inviting them to produce and engage with its data following heterogeneous orders of worth. Platform data can participate in different valuation regimes at the same time – however, the paper concludes, not all actors can participate in all modes of valuation, as in the end, it is the platform that sets the conditions for participation. The paper offers a conceptual perspective to interrogate what data counts by attending to questions of quantification, its entanglement with valuation and the various technologies and stakeholders involved. It finishes with an empirical experiment to map the various ways in which Instagram data is made to count.

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