Platforms’ Research API Data Access: What Users See vs. What Researchers can Retrieve

Article 40(12) of the Digital Services Act (DSA) requires Very Large Online Platforms (VLOPs) to provide vetted researchers with access to publicly accessible data. While prior work has identified shortcomings of platform-provided data access mechanisms, existing research has not quantitatively assessed data quality and completeness in Research APIs across platforms, nor systematically mapped how exactly current access provisions fall short. This paper presents a systematic audit of research-access modalities by comparing data obtained through platforms’ Research APIs with data collected about the same platforms’ user-visible public information environment (PIE). Focusing on two major platform APIs—the TikTok Research API and the Meta Content Library—we reconstruct the full information feeds for two controlled sockpuppet accounts during two election periods, and benchmark these against the data retrievable for the same posts through the corresponding Research APIs. Our findings show systematic data loss through three classes of platform-imposed mechanisms: scope narrowing, metadata stripping, and operational restrictions. Together, these mechanisms implement overlapping filters that exclude large portions of the platforms PIE (up to ~50%), strip essential contextual metadata (up to ~83%), and impose severe technical constraints for researchers (down to ~1,000 requests per week). Viewed through a data quality lens, these filters primarily undermine the completeness of research-accessible data, resulting in a structurally biased representation. We conclude that, in their current form, Meta & TikTok Research APIs fall short of supporting meaningful, independent auditing of systemic risks as envisioned under the DSA.