Amongst other methods, political campaigns employ microtargeting, a specific technique used to address the individual voter. In the US, microtargeting relies on a broad set of collected data about the individual. However, due to the unavailability of comparable data in Germany, the practice of microtargeting is far more challenging. Citizens in Germany widely treat social media platforms as a means for political debate. The digital traces they leave through their interactions provide a rich information pool, which can create the necessary (...) conditions for political microtargeting following appropriate algorithmic processing. More specifically, data mining techniques enable information gathering about a people's general opinion, party preferences and other non-political characteristics. Through the application of data-intensive algorithms, it is possible to cluster users in respect of common attributes, and through profiling identify whom and how to influence. Applying machine learning algorithms, this paper explores the possibility to identify micro groups of users, which can potentially be targeted with special campaign messages, and how this approach can be expanded to large parts of the electorate. Lastly, based on these technical capabilities, we discuss the ethical and political implications for the German political system. (shrink)

This article draws on research undertaken by the authors as part of the Administrative Data Research Centre in England. Between 2014 and 2017, we conducted four case studies on government administrative data for education, transport, energy and health. The purpose of the research was to examine stakeholder perspectives about the sharing, linking and re-use of government administrative data. In relation to the role and nature of consent given by data subjects for re-use, our study revealed significant variations in data provider (...) and researcher attitudes. Although our study setting was England, we believe that the findings have wider resonance. Our analysis identified six factors which might account for the variations around consent: the specificities of the legislative framework governing the collection and processing of particular data; the type of data being collected and the relational context in which it is created; the broader information governance framework in which the data resides; the creating organization's approach to data release; the relative levels of risk aversity within the creating organization; and public perceptions and social attitudes. In conclusion, we consider whether consent is still the best mechanism available for data re-use, or whether a social contract model of data sharing should be developed. (shrink)