A Survey on Algorithmic Interventions in Opinion Dynamics

TL;DR

This survey reviews algorithmic strategies designed to influence opinion dynamics on social media, focusing on objectives like consensus and polarization, and discusses theoretical and empirical insights for healthier online environments.

Contribution

It provides a structured synthesis of interdisciplinary research on algorithmic interventions in opinion dynamics, organizing work by objectives and reviewing optimization methods and findings.

Findings

Algorithms can effectively steer opinions toward consensus or reduce polarization.

Theoretical models help understand the impact of interventions on opinion evolution.

Empirical studies demonstrate the potential and limitations of current intervention strategies.

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.