Reducing information overload in e-participation: A data-driven prioritization framework for policy-makers

Abstract

An increasingly common practice for policy-makers is to leverage e-participation to collect citizens’ opinions and improve their decision-making processes. This practice, however, is hindered by the large quantity of collected opinions which are often overloading and hard to value. This is referred to as information overload. As a way to mitigate this challenge for policy-makers, this article develops a prioritization framework for citizens’ ideas collected through e-participation. The framework builds on Design Science Research and is validated on a real-world case in collaboration with the European Commission. The resulting contributions are threefold. First, theoretical criteria, popularity and polarization, are developed to prioritize citizens’ proposals. Then, automated and quantitative metrics are proposed to measure these criteria. Finally, a prioritization matrix is developed to visually assess the relative priority of these citizens’ proposals.

Keywords

• Data-driven decision making
• Information overload
• Citizen participation
• Ideas prioritization
• Digital government
• Smart governance