Recommendation systems, while designed to personalize user experiences, can sometimes lead to recommendation traps, which are structurally embedded areas within a recommendation network that draw users in and keep them entrapped longer than intended. One such system is YouTube’s recommendation engine, which not only plays a central role in shaping content consumption [1] but also raises concerns about algorithmic entrapment and prolonged user retention. In this study, we leverage network-based frameworks to detect and analyze recommendation traps within YouTube’s recommendation network. Focusing primarily on three contexts: viz., the China–Uyghur discourse, the Cheng Ho propaganda, and the 2024 Trump assassination attempt, we apply Focal Structure Analysis (FSA) to extract dense, high-impact subgraphs. These focal structures are then compared with equal-sized groups constructed from top-ranked nodes through different centrality measures (i.e., degree, betweenness, closeness, and eigenvector). To evaluate the impact of recommendation traps, we introduce two structural metrics: attraction - which captures how quickly these structures are reached from nodes outside the group, and retention - which measures how long the traversal remains within the structure once entered. Simulated random walks revealed that focal structures consistently outperformed baseline groups, particularly in the retention metric, suggesting focal structures’ effectiveness in identifying recommendation traps. These findings further emphasize the role of network structural cohesion in shaping user pathways and offer a scalable method for identifying entrapment zones in algorithmic systems.