A Computational Framework for Automated Reconstruction and Analysis of Dynamic Consent Interaction

Dynamic consent ecosystems have become increasingly complex due to the widespread adoption of Consent Management Platforms (CMPs), multi-layer preference interfaces, asynchronous rendering architectures, and adaptive interaction workflows. Existing privacy-auditing approaches primarily rely on static interface inspection and therefore provide limited support for reconstructing and evaluating dynamic consent interactions. To address these limitations, this study proposes a computational measurement framework for the automated reconstruction and analysis of dynamic consent ecosystems. The framework integrates five computational layers for browser-based acquisition, interaction sensing, multi-layer synchronization, consent-state verification, and Adaptive Cognitive Load Dark Pattern (ACL-DP) operationalization. The proposed methodology combines asynchronous browser automation, interaction workflow reconstruction, multi-source evidence synchronization, backend consent verification, and rule-based mechanism scoring to transform complex consent interactions into reproducible quantitative representations. Evaluation across 18,665 consent ecosystems generated 59 synchronized variables spanning interface, interaction, textual, and consent-state dimensions. Workflow reconstruction successfully recovered interaction trajectories for 99.6% of observable consent environments, while backend verification identified consent mismatches in 77.6% of environments with complete frontend–backend aligned evidence, revealing substantial divergence between observable consent decisions and backend consent behavior. The ACL-DP framework operationalizes four mechanism families, Effort Engineering, Attention Engineering, Cognitive Load Amplification, and Algorithmic Adaptivity, the latter capturing observable runtime, session-dependent, context-sensitive, and backend-mediated variation in consent behavior. The results revealed persistent procedural and attentional asymmetries, recurrent hidden rejection mechanisms, and widespread frontend–backend consent inconsistencies, with Effort Engineering emerging as the dominant manipulation strategy. Validation through reproducibility analysis, sensitivity analysis, statistical uncertainty assessment, and a human benchmark of 100 independently annotated websites demonstrated high inter-run consistency and moderate-to-substantial inter-rater agreement, supporting the framework’s reliability and validity. This work provides a reproducible foundation for large-scale privacy interaction analysis and evidence-based evaluation of dynamic consent ecosystems.