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.

While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of “filter bubbles.” These bubbles restrict the range of information users interact with, inadvertently reinforcing their existing viewpoints. Many studies have been dedicated to improving the recommendation algorithms to tackle this issue. Yet, approaches that maintain the integrity of the original algorithms remain largely unexplored. This article introduces the agent-based adaptive information neutrality (AAIN) model, grounded in Yin-Yang theory. The proposed novel approach targets the imbalance in information perception within existing recommendation systems. It is designed to integrate with these preference-based systems, ensuring the delivery of recommendations with neutral information. Our empirical evaluation of this model proved its effectiveness, showcasing its capacity to expand information diversity while respecting user preferences. Therefore, AAIN proves to be an effective model in reducing the adverse impact of filter bubbles on how information is consumed.

AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types of ideological isolation, i.e., the individual isolation and the topological isolation, in terms of the filter bubble and echo chamber effects, respectively. Simulation results show that AI recommendation strategies severely facilitate the evolution of the filter bubble effect, leading users to become ideologically isolated at an individual level. Whereas, at a topological level, recommendation algorithms show eligibility in connecting individuals with dissimilar users or recommending diverse topics to receive more diverse viewpoints. This research sheds light on the ability of AI recommendation strategies to temper ideological isolation at a topological level.