Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study
This work addresses the problem of automated opinion selection for online deliberation platforms, which is incremental as it builds on existing strategies to improve democratic outcomes.
The study compared algorithmic approaches for selecting representative opinions in online deliberation, finding that no single strategy dominated across all democratic criteria, but their novel social-choice-inspired algorithm achieved the strongest trade-off between proportional representation and diversity.
During deliberation processes, mediators and facilitators typically need to select a small and representative set of opinions later used to produce digestible reports for stakeholders. In online deliberation platforms, algorithmic selection is increasingly used to automate this process. However, such automation is not without consequences. For instance, enforcing consensus-seeking algorithmic strategies can imply ignoring or flattening conflicting preferences, which may lead to erasing minority voices and reducing content diversity. More generally, across the variety of existing selection strategies (e.g., consensus, diversity), it remains unclear how each approach influences desired democratic criteria such as proportional representation. To address this gap, we benchmark several algorithmic approaches in this context. We also build on social choice theory to propose a novel algorithm that incorporates both diversity and a balanced notion of representation in the selection strategy. We find empirically that while no single strategy dominates across all democratic desiderata, our social-choice-inspired selection rule achieves the strongest trade-off between proportional representation and diversity.