Evaluating Prototypes and Criticisms for Explaining Clustered Contributions in Digital Public Participation Processes

Abstract

AbstractWe examine the use of prototypes and criticisms for explaining clusterings in digital public participation processes of the e-participation domain. These processes enable people to participate in various life areas such as landscape planning by submitting contributions that express their opinions or ideas. Clustering groups similar contributions together. This supports citizens and public administrations, the main participants in digital public participation processes, in exploring the submitted contributions. However, explaining clusterings remains a challenge. For this purpose, we consider the use of prototypes and criticisms. Our work generalizes the idea of applying the $$k$$-medoids algorithm for computing prototypes on raw data sets. We introduce a centroid-based clusterings method that solely considers clusterings. It allows the retrieval of multiple prototypes and criticisms per cluster. We conducted a user study with 21 participants to evaluate our centroid-based clusterings method and the MMD-critic algorithm for finding prototypes and criticisms in clustered contributions. We examined whether these methods are suitable for text data. The related contributions originate from past, real-life digital public participation processes. The user study results indicate that both methods are appropriate for clustered contributions. The results also show that the centroid-based clusterings method outperforms the MMD-critic algorithm regarding accuracy, efficiency, and perceived difficulty.