Influencer marketing relies extensively on data-driven targeting mechanisms that collect, process, and monetize large volumes of user information, thereby raising significant concerns related to data privacy. This study undertakes a comparative and longitudinal assessment of relative data privacy exposure associated with influencer-targeting practices across four major social media platforms-Instagram, TikTok, YouTube, and Twitter (X). Using six years (2020–2025) of aggregated secondary data drawn from platform transparency reports, privacy policy audits, regulatory disclosures, academic literature, and cybersecurity firm assessments, the study quantifies platform-level privacy risks through the construction of a composite Privacy Exposure Index (PEI). The PEI integrates indicators relating to data scope and sensitivity, third-party data sharing, and the effectiveness of user privacy controls. The empirical results reveal statistically significant variation in PEI scores across platforms and over time (p < 0.001), leading to the rejection of the null hypothesis. Platforms characterized by algorithmic content discovery and AI-driven personalization exhibit consistently higher privacy exposure. TikTok records the highest average PEI across the study period, while YouTube demonstrates the steepest growth in exposure, indicating a rapid intensification of data-intensive targeting practices. Twitter/X maintains comparatively lower exposure levels, although its upward trend suggests gradual convergence toward more intrusive data usage. Instagram displays high but relatively stable exposure, reflecting mature and entrenched data monetization structures.
Data Privacy, Influencer Marketing, Privacy Exposure Index, Social Media Platforms, Comparative Analysis, Longitudinal Study.
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