Jennifer Pybus

Hacking the social life of Big Data

This paper builds off the Our Data Ourselves research project, which examined ways of understanding and reclaiming the data that young people produce on smartphone devices. Here we explore the growing usage and centrality of mobiles in the lives of young people, questioning what data-making possibilities exist if users can either uncover and/or capture what data controllers such as Facebook monetize and share about themselves with third-parties. We outline the MobileMiner, an app we created to consider how gaining access to one’s own data not only augments the agency of the individual but of the collective user. Finally, we discuss the data making that transpired during our hackathon. Such interventions in the enclosed processes of datafication are meant as a preliminary investigation into the possibilities that arise when young people are given back the data which they are normally structurally precluded from accessing.

Introduction. Politics of Big Data

This special issue offers a critical dialogue around the myriad political dimensions of Big Data. We begin by recognising that the technological objects of Big Data are unprecedented in the speed, scope and scale of their computation and knowledge production. This critical dialogue is grounded in an equal recognition of continuities around Big Data’s social, cultural, and political economic dimensions.

Simondon on Datafication. A Techno-Cultural Method

Abstract This article proposes the techno-cultural workshop as an innovative method for opening up the materiality of computational media and data flows and order to increase understanding of the socio-cultural and political-economic dimensions of datafication. Building upon the critical, creative hacker ethos of technological engagement, and the collective practice of the hackathon, the techno-cultural workshops is directed at humanities researchers and social and cultural theorists. We conceptually frame this method via Simondon as a practice-led opportunity to rethink the contested relationship between the human, nature and technology, with a view to challenging social and cultural theory that ignores the human reality of the technical object. We outline an exemplar techno-cultural workshop which explored mobile apps as i) an opportunity to use new digital tools for empirical research, and ii) as technical objects and elements for better understanding their social and cultural dimensions. We see political efficacy in the techno-cultural method not only in augmenting critical and creative agency, but as a practical exploration of the concept of data technicity, an inexhaustible relationality that exceeds the normative and regulatory utility of the data we generate and can be linked anew into collective capacities to act.

Trump, the First Facebook President: Why Politicians Need Our Data Too

This chapter considers how advertising platforms like Facebook or companies like Cambridge Analytica leveraged vast amounts of data to produce granulated, psychographic profiles that matched American voters with targeted political messages in the recent Trump elections. In so doing, it begins by examining the relationship between current political practices and the technological changes that have rapidly transformed advertising and marketing industries. It goes on to discuss how processes of datafication should no longer be uniquely understood as economic but also as political to garner influence, raising important questions around the myriad ways in which political parties are now using algorithmic processes to reach potential voters. The chapter concludes by considering the datafied tactics of persuasion or ‘nudge politics’, given the small margins and means by which Trump won.

Research on Online Digital Cultures - Community Extraction and Analysis by Markov and k-Means Clustering

We investigate approaches to personal data analytics that involves the participation of all actors in our shared digital culture. We analyse their communities by identifying and clustering social relations using mobile and social media data. The work is part of our effort to develop tools to create a “social data commons”, an open research environment that will share innovative tools and data sets to researchers interested in accessing the data that surrounds the production and circulation of digital culture and their actors. This experiment focuses on the groups of clustered relations that are formed within a user’s social data traces. Community extraction is a popular part of the analysis of social data. We have applied the technique of Markov Clustering to the Twitter networks of social actors. Qualitatively, we demonstrate that it is more effective than the Louvain method for finding social groups known to the subjects, while still being very simple to implement. We also demonstrate that traces of cell towers captured using our “MobileMiner” mobile application are sufficient to capture significant details about their social relations by the simple application of k-means.