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A Survey on Algorithmic Interventions in Opinion Dynamics

Atsushi Miyauchi, Yuko Kuroki, Federico Cinus, Stefan Neumann, Francesco Bonchi

TL;DR

This survey organizes prior work by the objective optimized -- overall opinion, polarization and disagreement, and other quantities -- and reviews the associated optimization formulations and representative algorithms with mathematical rigor to outline concrete future directions that emerge from this survey.

Abstract

Social media platforms have become critical infrastructures for public communication, where large-scale interaction can both support socially beneficial collective pressure and amplify polarization and conflict. While opinion-dynamics research has long modeled how beliefs evolve through interpersonal influence, the central challenge for healthier online environments increasingly lies in algorithmic interventions: mechanisms that steer collective opinion toward desirable outcomes or dampen harmful dynamics. This survey offers a structured synthesis of this fast-growing, interdisciplinary literature. We organize prior work by the objective optimized -- overall opinion (e.g., consensus or mean opinion), polarization and disagreement, and other quantities -- and review the associated optimization formulations and representative algorithms with mathematical rigor. We also compile intervention-relevant theoretical and empirical findings. Finally, we outline concrete future directions that emerge from this survey.

A Survey on Algorithmic Interventions in Opinion Dynamics

TL;DR

This survey organizes prior work by the objective optimized -- overall opinion, polarization and disagreement, and other quantities -- and reviews the associated optimization formulations and representative algorithms with mathematical rigor to outline concrete future directions that emerge from this survey.

Abstract

Social media platforms have become critical infrastructures for public communication, where large-scale interaction can both support socially beneficial collective pressure and amplify polarization and conflict. While opinion-dynamics research has long modeled how beliefs evolve through interpersonal influence, the central challenge for healthier online environments increasingly lies in algorithmic interventions: mechanisms that steer collective opinion toward desirable outcomes or dampen harmful dynamics. This survey offers a structured synthesis of this fast-growing, interdisciplinary literature. We organize prior work by the objective optimized -- overall opinion (e.g., consensus or mean opinion), polarization and disagreement, and other quantities -- and review the associated optimization formulations and representative algorithms with mathematical rigor. We also compile intervention-relevant theoretical and empirical findings. Finally, we outline concrete future directions that emerge from this survey.
Paper Structure (21 sections, 9 equations, 1 figure, 3 tables)

This paper contains 21 sections, 9 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Opinion-Dynamics Models
  3. DeGroot Model
  4. Friedkin--Johnsen Model
  5. Other Models
  6. Optimization of Overall Opinion
  7. Interventions on Innate Opinions
  8. Interventions on Leader Selection
  9. Interventions on Network Structure
  10. Interventions on Susceptibility to Persuasion
  11. Multi-Stage Interventions
  12. Interventions in Competitive Environments
  13. Optimization of Polarization and Disagreement
  14. Polarization
  15. Disagreement
  16. ...and 6 more sections

Figures (1)

  • Figure 1: A high-level map of the main part of this survey. Blue boxes denote the objectives being optimized, while red boxes represent intervention mechanisms.