Algorithmic recommendation systems shape user exposure by structuring pathways through content and reinforcing sustained engagement within specific regions of the network. Prior work on content traps has primarily emphasized structural dynamics or isolated content signals, limiting the ability to measure how multiple forms of influence jointly contribute to algorithmic entrapment. In this study, we extend the TrapIntensity framework by integrating four components: structural entrapment, measured through attraction and retention dynamics in recommendation networks; persuasion intensity, derived from textual signals grounded in four persuasion theories; transcript-guided keyframe extraction to capture temporally relevant segments of video content; and social, cultural, economic, and political cues extracted from those keyframes. Rather than interpreting higher TrapIntensity scores as stronger trapping in themselves, we treat TrapIntensity as a measurement construct and evaluate whether the inclusion of these additional signals improves its validity. Across fourteen YouTube recommendation-network datasets, we find that the extended formulation improves alignment with engagement and more clearly distinguishes top focal structures from the rest of the network. These findings suggest that measuring content traps requires a joint consideration of structural exposure, persuasion, and visual-symbolic content cues.