Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study

TL;DR

This paper benchmarks various algorithmic opinion selection methods for online deliberation, highlighting trade-offs between diversity and representation, and introduces a novel social-choice-based algorithm that balances these democratic criteria.

Contribution

It provides a comparative analysis of existing strategies and proposes a new algorithm inspired by social choice theory to improve democratic representation and diversity.

Findings

No single strategy dominates all criteria

The proposed social-choice-inspired algorithm balances diversity and representation effectively

Benchmark results highlight trade-offs among different selection approaches

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.