Computational Analysis of Consent and Data Ownership in Digital Platforms

Abstract

Consent mechanisms and data ownership frameworks in digital platforms are the primary legal and ethical instruments through which users exercise control over personal data, yet computational analysis reveals systematic gaps between formal consent validity requirements and actual user understanding, meaningful choice, and power to negotiate terms. This paper proposes the Consent and Data Ownership Computational Analysis Framework (CDOCAF), applying three computational analysis methods -- natural language processing readability analysis, behavioural consent quality assessment, and power asymmetry quantification -- to evaluate consent mechanisms and data ownership terms across 48 major digital platforms. CDOCAF introduces the Consent Quality Score (CQS) integrating legal validity, comprehensibility, genuine choice, and user empowerment. Key results: mean CQS across 48 platforms is 0.312 (poor) -- technically GDPR-valid but failing genuine informed consent standards; average privacy policy readability requires university-level education (Flesch-Kincaid grade 16.8); genuine choice is absent in 84.2% of platforms (take-it-or-leave-it with no alternatives); data ownership terms transfer economic value to platforms in 92.4% of cases. The framework provides consent quality measurement tools and reform recommendations.