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[2602.15439] Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study
Summary
This article examines various algorithmic approaches to opinion selection in online deliberation, highlighting the trade-offs between diversity and proportional representation.
Why It Matters
As online deliberation becomes more prevalent, understanding the implications of algorithmic opinion selection is crucial for maintaining democratic values. This study provides insights into how different strategies can impact representation and content diversity, which is vital for fostering inclusive discussions in digital spaces.
Key Takeaways
- Algorithmic selection can automate opinion representation but risks erasing minority voices.
- No single selection strategy excels across all democratic criteria; trade-offs exist.
- A novel algorithm inspired by social choice theory balances diversity and representation effectively.
- Understanding these algorithms is essential for improving online deliberation processes.
- The study benchmarks various strategies, providing empirical evidence for their effectiveness.
Computer Science > Computers and Society arXiv:2602.15439 (cs) [Submitted on 17 Feb 2026] Title:Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study Authors:Salim Hafid, Manon Berriche, Jean-Philippe Cointet View a PDF of the paper titled Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study, by Salim Hafid and 2 other authors View PDF 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 democrati...
