Learning through online participation: A longitudinal analysis of participatory budgeting using Big Data indicators

Research output: Contribution to journalArticleScientificpeer-review


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.
Original languageEnglish
JournalInformation Polity
Issue number4
Pages (from-to)517-538
Number of pages22
Publication statusPublished - Dec 2022
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 5171 Political Science
  • participatory budgeting
  • Big Data
  • online deliberation
  • learning
  • Helsinki

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