When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps

• URL: http://arxiv.org/abs/2508.11516v1
• Date: Fri, 15 Aug 2025 14:55:55 GMT
• Title: When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps
• Authors: Ming Tang, Xiaowen Huang, Jitao Sang,
• Abstract summary: We study the emergence and drivers of echo chambers and user homogenization, as well as actionable guidelines for human-centered recommender design.<n>Our findings provide both theoretical and empirical insights into the emergence and drivers of echo chambers and user homogenization, as well as actionable guidelines for human-centered recommender design.
• Score: 19.047790323760935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems increasingly suffer from echo chambers and user homogenization, systemic distortions arising from the dynamic interplay between algorithmic recommendations and human behavior. While prior work has studied these phenomena through the lens of algorithmic bias or social network structure, we argue that the psychological mechanisms of users and the closed-loop interaction between users and recommenders are critical yet understudied drivers of these emergent effects. To bridge this gap, we propose the Confirmation-Aware Social Dynamic Model which incorporates user psychology and social relationships to simulate the actual user and recommender interaction process. Our theoretical analysis proves that echo chambers and homogenization traps, defined respectively as reduced recommendation diversity and homogenized user representations, will inevitably occur. We also conduct extensive empirical simulations on two real-world datasets and one synthetic dataset with five well-designed metrics, exploring the root factors influencing the aforementioned phenomena from three level perspectives: the stochasticity and social integration degree of recommender (system-level), the psychological mechanisms of users (user-level), and the dataset scale (platform-level). Furthermore, we demonstrate four practical mitigation strategies that help alleviate echo chambers and user homogenization at the cost of some recommendation accuracy. Our findings provide both theoretical and empirical insights into the emergence and drivers of echo chambers and user homogenization, as well as actionable guidelines for human-centered recommender design.

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