The Great Data Standoff: Researchers vs. Platforms Under the Digital Services Act

Abstract

To facilitate accountability and transparency, the Digital Services Act (DSA) sets up a process through which Very Large Online Platforms (VLOPs) need to grant vetted researchers access to their internal data (Article 40(4)). Although expectations are high for this mechanism to provide unprecedented data access as part of the DSA compliance obligations, operationalising it is challenging for two reasons. First, data access is only available for research on systemic risks affecting European citizens, a concept that still raises high levels of legal uncertainty. Second, data access suffers from an inherent standoff problem. Researchers need to request specific data but are not in a position to know all internal data collected, processed and stored by VLOPs, who, in turn, expect and demand data specificity for potential access. To contribute to the discussion of how Article 40 can be interpreted and applied, we provide a concrete illustration of what data access can look like in a real-world systemic risk case study. Our goal is not only to share with the research community at large a scenario for reflection, but also to provide hands-on insights into what type of data platforms may be required to share through the DSA. To this end, we focus on the 2024 Romanian presidential election interference incident, as this event is the first of its kind to trigger systemic risk investigations by the European Commission. In the context of these elections, one candidate is said to have benefited from TikTok algorithmic amplification through a complex and multilayered dis- and misinformation campaign. By analysing this incident, we can concretely comprehend election-related systemic risk in order to explore practical research tasks and compare necessary data with available TikTok data. In particular, our study makes two contributions: (i) we combine insights from law, computer science and platform governance to shed light on the complexities of studying systemic risks in the context of election interference, focusing on two relevant factors: platform manipulation and hidden advertising; and (ii) we provide practical insights into various categories of available data for the study of TikTok, based on platform documentation, data donations and TikTok's Research API.