We are excited to publish a new article that analysed the democratic quality of online deliberation using Big Data indicators in two rounds of OmaStadi, a participatory budgeting project led by the City of Helsinki, Finland. Our main contribution is to propose online and Big data-based monitoring indicators as a tool for learning and improving democratic innovations that are increasingly iterated along with a policy cycle.
Article title: Learning through online participation: A longitudinal analysis of participatory budgeting using Big Data indicators
Authors: Bokyong Shin, Mikko Rask, Pekka Tuominen
Journal: Information Polity
Local authorities increasingly employ digital platforms to facilitate public engagement in participatory budgeting processes. This creates opportunities for and challenges in synthesizing citizens’ voices online in an iterated cycle, requiring a systematic tool to monitor democratic quality and produce formative feedback. In this paper, we demonstrate how cases of online deliberation can be compared longitudinally by using six Big Data-based, automated indicators of deliberative quality. Longitudinal comparison is a way of setting a reference point that helps practitioners, designers, and researchers of participatory processes to interpret analytics and evaluative findings in a meaningful way. By comparing the two rounds of OmaStadi, we found that the levels of participation remain low but that the continuity and responsiveness of online deliberation developed positively.