The Algorithmic Distortion of Consensus: Perceived Exaggerated Amplification (P-E-A) as a Framework for Political Science

This paper introduces Perceived Exaggerated Amplification (P-E-A) as a critical framework for analyzing distortions in digital-age political discourse. While existing political science concepts—such as polarization, echo chambers, and agenda-setting—explain ideological clustering and media influence, they fail to account for the systematic inflation of perceived political consensus driven by social media algorithms. P-E-A describes the phenomenon whereby algorithmically curated feeds amplify extreme and emotionally charged political content, creating the illusion that fringe or polarizing opinions are widely held. The framework emerges from three interacting mechanisms: algorithmic incentives that prioritize engagement, cognitive biases that focus attention on high-arousal content, and feedback loops that reinforce perceived prevalence. Through a detailed case study of the 2016 U.S. Presidential Election, the paper demonstrates how P-E-A can quantify the gap between online perception and electoral reality, offering a measurable "Political P-E-A Score." This lens refines classical theories of public opinion (Lippmann, 1922; Noelle-Neumann, 1974) for the digital era, revealing how algorithmic amplification shapes voter anxiety, campaign strategy, and democratic deliberation. By providing an operationalizable metric, P-E-A equips political scientists, campaign strategists, and policymakers to diagnose perceptual distortions, calibrate communications, and design interventions that mitigate the threat algorithmic amplification poses to informed civic discourse and democratic integrity.