Between technical features and analytic capabilities: Charting a relational affordance space for digital social analytics

Abstract

Digital social analytics is a subset of Big Data methods that is used to understand the social environment in which people and organizations have to act. This paper presents an analysis of eight projects that are experimenting with the use of these methods for various purposes. It shows that two specific technological features influence the work with such methods in all the cases. The first concerns the need to distribute choices about the structure of data to third-party actors and the second concerns the need to balance machine intelligence and human intuition when automating the analysis. These features set specific conditions for knowledge production, and the paper identifies two opposite approaches for engaging with each of these conditions. These features and approaches are finally combined into a two-dimensional affordance space that illustrates how there is flexibility in the way project leaders interact with the features of the data environment. It thereby also shows how digital social analytics come to have different affordances for different projects.