While at InSIS I worked upon two case studies as part of the Oxford eSocial Science project. The first case study was an ethnography of a very successful computer lab in London. They have been creating middle-ware programmes for hosting visualisations online. These visualisations render academic, publicly available or crowd-sourced information in an interactive format – from the (near) real-time availability of Barclay’s bikes in London to the latest UK census or crime statistics from the Metropolitan Police Service. I explored the tension in the work involving code that must be balanced for the success of such e-research labs. New and innovative types of data are being mashed-up in visualisations, but this creativity is coupled to ad hoc programming on the part of each individual programmer. Rather than focus upon the data, care must be given to ‘code curatorial’ practices to sustain these platforms.
The second case study was a timely study of Twitter. Much discussed and perhaps overly hyped as a means for network mobilisation during recent political upheavals in Tunisia, Egypt, Libya and here in London (and greater UK) during the past riots, Twitter is emerging as a reservoir for data mining by academics, politicos, the private industry and others. (The Guardian, for example, now employs a team to study Twitter). With a colleague at a lab that harvests the ‘back-end’ of social media platforms, I co-authored a paper on the ethical implications of such research. A bit contentiously, we urge a levelling of ourselves with those we study through making ourselves equally vulnerable to potential data-mining. The paper is now available at Taylor and Francis.
“In this paper, the authors examine some of the implications of born-digital research environments by discussing the emergence of data mining and the analysis of social media platforms. With the rise of individual online activity in chat rooms, social networking sites and micro-blogging services, new repositories for social science research have become available in large quantities. Given the changes of scale that accompany such research, both in terms of data mining and the communication of results, the authors term this type of research ‘massified research’. This article argues that while the private and commercial processing of these new massive data sets is far from unproblematic, the use by academic practitioners poses particular challenges with respect to established ethical protocols. These involve reconfigurations of the external relations between researchers and participants, as well as the internal relations that compose the identities of the participant, the researcher and that of the data. Consequently, massified research and its outputs operate in a grey area of undefined conduct with respect to these concerns. The authors work through the specific case study of using Twitter’s public Application Programming Interface for research and visualization. To conclude, this article proposes some potential best practices to extend current procedures and guidelines for such massified research. Most importantly, the authors develop these under the banner of ‘agile ethics’. The authors conclude by making the counterintuitive suggestion that researchers make themselves as vulnerable to potential data mining as the subjects who comprise their data sets: a parity of practice.”
Continue reading the full paper at Information, Communication and Society.