Mathematical Modeling of Opinion Dynamics under Algorithmic Amplifications: A Bifurcation Analysis

Abstract

Social media platforms increasingly shape public opinion through algorithmic content curation, yet the precise mathematical conditions under which such algorithms induce societal polarization remain poorly understood. This study extends classical bounded confidence opinion dynamics models by incorporating an algorithmic amplification term capturing the tendency of engagement-maximizing recommendation systems to promote extreme content. We analyze a two-group mean-field reduction using dynamical systems theory and derive exact analytical results for equilibrium structure, stability, and bifurcation behavior. The central finding is a supercritical pitchfork bifurcation at critical algorithmic strength α_c^*=2β , where β denotes the social interaction rate: below this threshold, only extreme consensus states are stable; above it, polarized equilibria emerge continuously with opinion gap δ^*=√(1-2β\/α). We establish a complete phase diagram comprising three regimes: extreme consensus (radicalization), partial polarization with cross-group interaction, and echo chambers with communication breakdown, with boundaries determined by algorithmic strength, interaction rate, and confidence threshold. Notably, within this model class, the centrist equilibrium is unconditionally linearly unstable for any positive algorithmic amplification, suggesting that engagement-driven algorithms may tend to destabilize moderate discourse. Agent-based simulations validate all analytical predictions. These results provide quantitative criteria for platform design and policy interventions aimed at mitigating algorithmic polarization.

Keywords

• Algorithmic amplification
• Bifurcation analysis
• Bounded confidence model
• Opinion dynamics
• Polarization

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