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

Salim Hafid, Manon Berriche, Jean-Philippe Cointet

TL;DR

This work builds on social choice theory to propose a novel algorithm that incorporates both diversity and a balanced notion of representation in the selection strategy, and finds that this social-choice-inspired selection rule achieves the strongest trade-off between proportional representation and diversity.

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.

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

TL;DR

This work builds on social choice theory to propose a novel algorithm that incorporates both diversity and a balanced notion of representation in the selection strategy, and finds that this social-choice-inspired selection rule achieves the strongest trade-off between proportional representation and diversity.

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.
Paper Structure (25 sections, 7 equations, 16 figures, 2 tables, 2 algorithms)

This paper contains 25 sections, 7 equations, 16 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Theoretical framework
  5. DiverseBJR: Diversity-aware Balanced Justified Representation
  6. Experiments
  7. Evaluation metrics
  8. (ii) Consensus.
  9. Baselines
  10. Data
  11. Results
  12. Illustrative differences in selection between DiverseBJR and Engagement
  13. Relationship between JR, BJR and DiverseBJR
  14. Detailed algorithms
  15. DiverseBJR approximation algorithm
  16. ...and 10 more sections

Figures (16)

  • Figure 1: Random selection for a consensual (blue) and controversial (orange) question. At $k=5$, the consensual question leaves $\leq$5% unrepresented, whereas the controversial question leaves $\geq$20% unrepresented, motivating more sophisticated selection.
  • Figure 2: Evolution of various democratic desiderata with the number of selected opinions (dotted lines are used for better distinction of overlapping zones).
  • Figure 3: Visualization of the opinion space - Comparison of selecting 3 representative opinions based on Engagement (left) versus DiverseBJR (right). Embeddings are based on MDS (distance-preserving) - Color = distance to nearest selected opinion (normalized Hamming). DiverseBJR selects more distant (i.e., diverse) thoughts, whereas Engagement selects based on close clusters, therefore potentially selecting redundant opinions.
  • Figure 4: Q2: "What are features or characteristics that make a protest appropriate?" (307 participants, 306 opinions)
  • Figure 5: Q3: "What characteristics or actions, in your view, deem a protest inappropriate?" (306 participants, 299 opinions)
  • ...and 11 more figures

Theorems & Definitions (4)

  • Definition 3.1: Justified Representation (JR) revel2025representativeaziz2017justified
  • Definition 3.2: Balanced Justified Representation (BJR) fish2024generative, adapted to binary approvals
  • Definition 3.3: $\varepsilon$-neighbor
  • Definition 3.4: DiverseBJR