Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation
Loading...
Files
Size: 2.22 MB, File format: Adobe PDF
Date
Authors
Wang, Xiaodan
Liu, Yanbin
Wu, Shiqing
Zhao, Ziying
Hu, Yuxuan
Li, Weihua
Bai, Quan
Supervisor
Item type
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Ideological isolation in online social networks, including selective exposure, echo chambers, filter bubbles, tunnel vision, and polarization, has become a central concern for computational and neural modeling of information ecosystems. With the rapid adoption of graph learning, representation learning, and feedback-driven recommender systems, a growing body of work has proposed diverse metrics and models to quantify and mitigate these phenomena. However, existing studies and surveys rely on heterogeneous definitions and incompatible measurements, making empirical findings difficult to compare and obscuring how different forms of ideological isolation arise in learning-based systems. This survey provides a computationally grounded and comprehensive review of existing approaches to defining, analyzing, measuring, and mitigating ideological isolation in online social networks. We examine the mechanisms underlying content personalization, user behavior, and network structure that drive exposure concentration and attention narrowing. We then systematically review methodological approaches for detecting and quantifying ideological isolation, covering network-, content-, and behavior-based metrics, and synthesize empirical findings across platforms to assess their applicability and limitations. We further organize computational mitigation strategies, including network-topological interventions and recommendation-level controls, compare mitigation families and their trade-offs, and examine the dual role of large language models. The key ethical considerations in the design and deployment of diversity-aware systems are also discussed. By resolving the definition-metric-intervention mismatch that characterizes existing work, this survey provides a principled foundation for the design, evaluation, and deployment of neural and learning-based systems aimed at diagnosing and mitigating ideological isolation in online social networks.
Description
Keywords
08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences, Artificial Intelligence & Image Processing, 40 Engineering, 46 Information and computing sciences, 52 Psychology, Online social networks, Ideological isolation, Selective exposure, Filter bubble, Echo chamber, Recommender systems
Source
Neurocomputing, ISSN: 0925-2312 (Print); 1872-8286 (Online), Elsevier, 134333-134333. doi: 10.1016/j.neucom.2026.134333
Publisher's version
Rights statement
© 2026 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.
Permanent link
Endorsement
Review
Supplemented By
Referenced By
Creative Commons license
Except where otherwise noted, this item's license is described as © 2026 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.