🤖 AI Summary
This study addresses the challenge in online deliberation where existing opinion selection algorithms often marginalize minority viewpoints and struggle to balance proportional representation with diversity. To tackle this issue, the paper introduces social choice theory into opinion selection for the first time and proposes a novel algorithm that jointly optimizes diversity and balanced representation. The method integrates representative sampling, diversity-aware optimization, and a multi-criteria evaluation mechanism. Through systematic comparison with alternative strategies, the authors demonstrate that their approach achieves a superior trade-off between proportional representation and diversity, significantly outperforming existing methods that optimize only a single objective.
📝 Abstract
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.