Auditing Meta and TikTok Research API Data Access under Article 40(12) of the Digital Services Act

2026-01-18

AI Summary

This study evaluates whether Meta and TikTok’s research APIs meet the integrity and quality requirements for public data access stipulated under Article 40(12) of the Digital Services Act (DSA) to enable independent risk audits. By constructing controlled virtual accounts to reconstruct user information feeds during election periods and systematically comparing API-retrieved data against the actual public information environment (PIE), the research quantitatively demonstrates— for the first time—that platform-imposed mechanisms, including scope narrowing, metadata stripping, and operational constraints, result in up to 50% missing content and 83% loss of critical metadata. These findings reveal structural biases in current APIs that significantly undermine the feasibility of independent auditing and fall short of DSA compliance standards.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance

Abstract

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 current access provisions fall short. This paper presents a systematic audit of research access modalities by comparing data obtained through platform 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 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 platform PIE (up to approximately 50 percent), strip essential contextual metadata (up to approximately 83 percent), and impose severe technical constraints for researchers (down to approximately 1000 requests per day). Viewed through a data quality lens, these filters primarily undermine completeness, resulting in a structurally biased representation of platform activity. We conclude that, in their current form, the Meta and TikTok Research APIs fall short of supporting meaningful, independent auditing of systemic risks as envisioned under the DSA.

Problem

• Digital Services Act
• Research API
• data completeness
• platform auditing
• systemic risk

Innovation

• Research API auditing
• data completeness
• Digital Services Act
• platform transparency
• systematic bias