Neuhaus, F., & Webmoor, T. (2012). Agile ethics for massified research and visualization. Information, Communication & Society, 15(1), 43–65. https://doi.org/https://doi.org/10.1080/1369118X.2011.616519
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.
In dem vom BMBF geförderten Projekt FeKoM werden Empfehlungen für forschungsethisches Handeln in der Kommunikations- und Medienwissenschaft systematisch erarbeitet, empirisch fundiert und der Scientific Community zur Verfügung gestellt.