Paper Type
Complete
Abstract
Algorithmic recommendation systems shape information exposure and engagement, yet scalable measures of “content traps” rarely integrate both network structure and content-level influence. This study proposes TrapIntensity, a sociotechnical construct for quantifying content traps in three YouTube recommendation networks case studies by combining structural entrapment and persuasive intensity. Structural entrapment is estimated through hop-aware attraction and retention dynamics using random-walk simulations over multi-hop recommendation graphs, with candidate trap regions extracted using Collective Influence–Focal Structure Analysis (CI-FSA) and assessed via resiliency-based fragmentation tests. Persuasive intensity is measured using a structured large language model pipeline grounded in four persuasion theories applied to video titles, descriptions, and transcripts. Using engagement outcomes as external behavioral indicators, we compare an equal-weight composite against a context-sensitive weighting scheme derived from Cliff’s delta. Results show that dataset-specific weighting improves engagement alignment and that the dominant driver of trap intensity varies across contexts, alternating between structural lock-in and persuasive reinforcement. The work contributes an auditable measurement framework for recommendation system governance.
Paper Number
1910
Recommended Citation
Agarwal, Nitin and Bhuiyan, Md Monoarul Islam, "Quantifying Algorithmic Entrapment in YouTube Recommendation Network: A Composite Measure of Structure and Persuasion" (2026). AMCIS 2026 Proceedings. 21.
https://aisel.aisnet.org/amcis2026/sig_hci/sig_hci/21
Quantifying Algorithmic Entrapment in YouTube Recommendation Network: A Composite Measure of Structure and Persuasion
Algorithmic recommendation systems shape information exposure and engagement, yet scalable measures of “content traps” rarely integrate both network structure and content-level influence. This study proposes TrapIntensity, a sociotechnical construct for quantifying content traps in three YouTube recommendation networks case studies by combining structural entrapment and persuasive intensity. Structural entrapment is estimated through hop-aware attraction and retention dynamics using random-walk simulations over multi-hop recommendation graphs, with candidate trap regions extracted using Collective Influence–Focal Structure Analysis (CI-FSA) and assessed via resiliency-based fragmentation tests. Persuasive intensity is measured using a structured large language model pipeline grounded in four persuasion theories applied to video titles, descriptions, and transcripts. Using engagement outcomes as external behavioral indicators, we compare an equal-weight composite against a context-sensitive weighting scheme derived from Cliff’s delta. Results show that dataset-specific weighting improves engagement alignment and that the dominant driver of trap intensity varies across contexts, alternating between structural lock-in and persuasive reinforcement. The work contributes an auditable measurement framework for recommendation system governance.
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