Online Homogeneity Can Emerge Without Filtering Algorithms or Homophily Preferences

• URL: http://arxiv.org/abs/2508.10466v1
• Date: Thu, 14 Aug 2025 09:08:46 GMT
• Title: Online Homogeneity Can Emerge Without Filtering Algorithms or Homophily Preferences
• Authors: Petter Törnberg,
• Abstract summary: Ideologically homogeneous online environments are seen as drivers of polarization, radicalization, and misinformation.<n>A central debate asks whether such homophily stems primarily from algorithmic curation or users' preference for like-minded peers.<n>This study challenges that view by showing that homogeneity can emerge in the absence of both filtering algorithms and user preferences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ideologically homogeneous online environments - often described as "echo chambers" or "filter bubbles" - are widely seen as drivers of polarization, radicalization, and misinformation. A central debate asks whether such homophily stems primarily from algorithmic curation or users' preference for like-minded peers. This study challenges that view by showing that homogeneity can emerge in the absence of both filtering algorithms and user preferences. Using an agent-based model inspired by Schelling's model of residential segregation, we demonstrate that weak individual preferences, combined with simple group-based interaction structures, can trigger feedback loops that drive communities toward segregation. Once a small imbalance forms, cascades of user exits and regrouping amplify homogeneity across the system. Counterintuitively, algorithmic filtering - often blamed for "filter bubbles" - can in fact sustain diversity by stabilizing mixed communities. These findings highlight online polarization as an emergent system-level dynamic and underscore the importance of applying a complexity lens to the study of digital public spheres.

Related papers

• Segregation Before Polarization: How Recommendation Strategies Shape Echo Chamber Pathways [3.7863431327418198]
  We show that content-based algorithms steer social networks toward a segregation-before-polarization pathway.<n>We reveal a paradox in information sharing: Reposting increases the number of connections in the network, yet it simultaneously reinforces echo chambers.
  arXiv  Detail & Related papers  (2026-01-23T05:28:49Z)
• Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation [82.74100448579857]
  We introduce Bubble Escape Potential (BEP), a behavior-aware measure that quantifies how easily users can escape from filter bubbles.<n>We conduct extensive experiments across multiple recommendation models to examine the relationship between predictive accuracy and bubble escape potential across different groups.
  arXiv  Detail & Related papers  (2025-11-27T07:21:04Z)
• Mitigating Filter Bubble from the Perspective of Community Detection: A Universal Framework [19.047790323760935]
  This paper proposes a universal framework called CD-CGCN to address the filter bubble issue in recommender systems.<n>By analyzing user-item interaction histories with a community detection algorithm, we reveal that state-of-the-art recommendations often focus on intra-community items, worsening the filter bubble effect.
  arXiv  Detail & Related papers  (2025-08-15T05:57:38Z)
• Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns [4.197682068104959]
  We develop a more refined definition of homogenization and the filter bubble effect by decomposing them into two key metrics. We then use a novel agent-based simulation framework that enables a holistic view of the impact of recommendation systems on homogenization and filter bubble effects. We introduce two new recommendation algorithms that take a more nuanced approach by accounting for both types of diversity.
  arXiv  Detail & Related papers  (2024-02-22T23:12:20Z)
• Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? [90.35044668396591]
  A recurring theme in machine learning is algorithmic monoculture: the same systems, or systems that share components, are deployed by multiple decision-makers. We propose the component-sharing hypothesis: if decision-makers share components like training data or specific models, then they will produce more homogeneous outcomes. We test this hypothesis on algorithmic fairness benchmarks, demonstrating that sharing training data reliably exacerbates homogenization. We conclude with philosophical analyses of and societal challenges for outcome homogenization, with an eye towards implications for deployed machine learning systems.
  arXiv  Detail & Related papers  (2022-11-25T09:33:11Z)
• Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods [0.0]
  Polarization arises when the underlying network becomes characterized by highly connected groups with weak inter-group connectivity. This work presents an annotated review of network polarization measures and models used to handle the polarization.
  arXiv  Detail & Related papers  (2022-07-27T21:23:27Z)
• The drivers of online polarization: fitting models to data [0.0]
  echo chamber effect and opinion polarization may be driven by several factors including human biases in information consumption and personalized recommendations produced by feed algorithms. Until now, studies have mainly used opinion dynamic models to explore the mechanisms behind the emergence of polarization and echo chambers. We provide a method to numerically compare the opinion distributions obtained from simulations with those measured on social media.
  arXiv  Detail & Related papers  (2022-05-31T17:00:41Z)
• Fair Group-Shared Representations with Normalizing Flows [68.29997072804537]
  We develop a fair representation learning algorithm which is able to map individuals belonging to different groups in a single group. We show experimentally that our methodology is competitive with other fair representation learning algorithms.
  arXiv  Detail & Related papers  (2022-01-17T10:49:49Z)
• Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id [109.1730454118532]
  Unsupervised person re-identification (Re-Id) has attracted increasing attention due to its practical application in the read-world video surveillance system. We present the hybrid dynamic cluster contrast and probability distillation algorithm. It formulates the unsupervised Re-Id problem into an unified local-to-global dynamic contrastive learning and self-supervised probability distillation framework.
  arXiv  Detail & Related papers  (2021-09-29T02:56:45Z)
• Correlation Clustering Reconstruction in Semi-Adversarial Models [70.11015369368272]
  Correlation Clustering is an important clustering problem with many applications. We study the reconstruction version of this problem in which one is seeking to reconstruct a latent clustering corrupted by random noise and adversarial modifications.
  arXiv  Detail & Related papers  (2021-08-10T14:46:17Z)
• Designing Recommender Systems to Depolarize [0.32634122554913997]
  Polarization is implicated in the erosion of democracy and the progression to violence. While algorithm-driven social media does not seem to be a primary driver of polarization at the country level, it could be a useful intervention point in polarized societies. This paper examines algorithmic depolarization interventions with the goal of conflict transformation.
  arXiv  Detail & Related papers  (2021-07-11T03:23:42Z)
• Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling [68.69431580852535]
  We introduce a novel GP regression to incorporate the subgroup feedback. Our modified regression has provably lower variance -- and thus a more accurate posterior -- compared to previous approaches. We execute our algorithm on two disparate social problems.
  arXiv  Detail & Related papers  (2021-07-07T03:57:22Z)
• An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization [53.592597682854944]
  We recast generalization over sub-groups as an online game between a player minimizing risk and an adversary presenting new test. We show that ERM is provably minimax-optimal for both tasks.
  arXiv  Detail & Related papers  (2021-02-25T19:06:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.