For a situational analytics: An interpretative methodology for the study of situations in computational settings

摘要

This article introduces an interpretative approach to the analysis of situations in computational settings called situational analytics. I outline the theoretical and methodological underpinnings of this approach, which is still under development, and show how it can be used to surface situations from large data sets derived from online platforms such as YouTube. Situational analytics extends to computationally-mediated settings a qualitative methodology developed by Adele Clarke, Situational Analysis (2005), which uses data mapping to detect heterogeneous entities in fieldwork data to determine ‘what makes a difference’ in a situation. Situational analytics scales up this methodology to analyse situations latent in computational data sets with semi-automated methods of textual and visual analysis. I discuss how this approach deviates from recent analyses of situations in computational social science, and argue that Clarke’s framework renders tractable a fundamental methodological problem that arises in this area of research: while social researchers turn to computational settings in order to analyse social life, the social processes unfolding in these envirnoments are fundamentally affected by the computational architectures in which they occur. Situational analytics offers a way to address this problematic by making a heterogeneously composed situation – involving social, technical and media elements – the unit of computational analysis. To conclude, I show how situational analytics can be applied in a case study of YouTube videos featuring intelligent vehicles and discuss how situational analysis itself needs to be elaborated if we are to come to terms with computational transformations of the situational fabric of social life.