Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems

2025/09/14 by Xiuli Wang, Xiaowen Huang, Wang, Xin +3
Computer Science · #Advanced Text Analysis Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques

paper · pdf · doi:10.48550/arxiv.2509.11139

openalex publication_date 2025/09/14 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28

Abstract

Personalized news recommendation systems inadvertently create information cocoons--homogeneous information bubbles that reinforce user biases and amplify societal polarization. To address the lack of comprehensive assessment frameworks in prior research, we propose a multidimensional analysis that evaluates cocoons through dual perspectives: (1) Individual homogenization via topic diversity (including the number of topic categories and category information entropy) and click repetition; (2) Group polarization via network density and community openness. Through multi-round experiments on real-world datasets, we benchmark seven algorithms and reveal critical insights. Furthermore, we design five lightweight mitigation strategies. This work establishes the first unified metric framework for information cocoons and delivers deployable solutions for ethical recommendation systems.

Citations

• Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns
• Evolution of Filter Bubbles and Polarization in News Recommendation
• CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
• The Effect of People Recommenders on Echo Chambers and Polarization
• Choosing the Best of Both Worlds: Diverse and Novel Recommendations\n through Multi-Objective Reinforcement Learning
• Is News Recommendation a Sequential Recommendation Task?
• Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation
• Neural News Recommendation with Attentive Multi-View Learning
• Graph Neural Networks for Social Recommendation
• DKN: Deep Knowledge-Aware Network for News Recommendation
• The Strength of Weak Ties
• ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models

Related

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• Recommenders with a mission: assessing diversity in newsrecommendations
• Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems
• Is Polarization an Inevitable Outcome of Similarity-Based Content Recommendations? -- Mathematical Proofs and Computational Validation
• Evolution of Filter Bubbles and Polarization in News Recommendation