A Survey on Algorithmic Interventions in Opinion Dynamics

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.