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

Catalina Goanta; Savvas Zannettou; Rishabh Kaushal; Jacob van de Kerkhof; Thales Bertaglia; Taylor Annabell; Haoyang Gui; Gerasimos Spanakis; Adriana Iamnitchi

arXiv · 2505.01122

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

Catalina Goanta·Savvas Zannettou·Rishabh Kaushal·Jacob van de Kerkhof·Thales Bertaglia·Taylor Annabell·Haoyang Gui·Gerasimos Spanakis·Adriana Iamnitchi

View original source ↗Read source document ↗Export metadata ↓

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)). Operationalising such access is challenging for at least two reasons. First, data access is only available for research on systemic risks affecting European citizens, a concept with 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 processed by VLOPs, who, in turn, expect data specificity for potential access. In light of these limitations, data access under the DSA remains a mystery. 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. We focus on the 2024 Romanian presidential election interference incident, the first event of its kind to trigger systemic risk investigations by the European Commission. During the elections, one candidate is said to have benefited from TikTok algorithmic amplification through a complex dis- and misinformation campaign. By analysing this incident, we can comprehend election-related systemic risk to explore practical research tasks and compare necessary data with available TikTok data. In particular, we make 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 the Research API.

Explore related subjects

cs.CY

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Catalina Goanta, Savvas Zannettou, Rishabh Kaushal, Jacob van de Kerkhof, Thales Bertaglia, Taylor Annabell, Haoyang Gui, Gerasimos Spanakis, Adriana Iamnitchi. 2025-05-02. The Great Data Standoff: Researchers vs. Platforms Under the Digital Services Act. https://arxiv.org/abs/2505.01122

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Work, Wellbeing, and Choice: Empirical Lessons for AI Futures

Advances in AI-driven automation have raised questions about how humans might find wellbeing in a world where paid employment is less necessary or less available than before. Paid work has been variously characterized as both a contributor and an impediment to human wellbeing. What is already known about the relationship between paid work and wellbeing? What factors influence wellbeing among people who do not work---or who do not need to work? And how might these factors bear upon prospective AI-induced economic transformations? To help provide empirical grounding for these questions, we survey the psychological, sociological, and economic literature that investigates the relationship between wellbeing and work. We draw on evidence from multiple populations, including the unemployed, retirees, lottery winners, and financially dependent spouses. This comparative review draws from studies across OECD countries, China, India, and Gulf states. We identify three key factors that mediate the relationship between work status and wellbeing: (1) agency and choice---whether the exit from work is voluntary or involuntary, as well as long-term agency; (2) the availability of alternative sources of work's latent benefits---such as volunteering, hobbies, or state-provisioned employment; and (3) social and systemic context---including cultural norms around work and the robustness of social safety nets. We draw on these three factors to derive specific implications for different AI automation scenarios, connecting the empirical evidence to concrete policy considerations.

cs.CY↗

arXiv2026

From Digital Accountability to Accountable Digitality Through Needs-Aware Information Systems: The Case of Auditable Child-Welfare Judgments

Digital accountability research asks how digital systems can, among other aims, be made transparent, explainable, auditable, contestable, and supportive of ongoing learning and improvement. This paper reverses the question: how can digital transformation make established human institutions more accountable? It theorizes this reversal as accountable digitality and specifies needs-aware information systems as the mediating mechanism. The hard and paradigmatic case is child-welfare judgment, where best-interest procedures must protect children, preserve confidentiality, and respect judicial independence while enabling aggregate learning about needs, reasons, exceptions, and disparities. The case is used diagnostically and illustratively to derive and examine the design logic, not as empirical evidence or validation. Conceptual design-oriented analysis decomposes and recombines digital and legal accountability under child-rights constraints, deriving a canonical theory-to-design chain, contingent mechanisms, implications, and safeguards. It advances IS responsibility and ethics research by showing how privacy-preserving, co-created, needs-aware information systems can support institutional self-knowledge and auditable justice.

cs.CY↗

arXiv2026

(Whose defaults?) Is artificial intelligence reorienting archaeological methods?

Generative AI and the practice of "vibe coding" are changing how archaeologists carry out computational research, but their effects on the discipline's range of methods is still understudied. In this paper, we evaluate whether large language models (LLMs) are narrowing the variety of methods archaeologists use. We first analysed approximately 119,000 archaeology abstracts from Scopus, covering publications from 2010 to 2025. Using a locally run LLM, we identified the computational methods reported in each abstract and organised them into 25 broad categories (L2) and 241 finer clusters (L3). A Bayesian Dirichlet-multinomial model of method composition within sub-disciplines found a small but credible shift in method use after 2023. However, this shift was smaller than the variation already present across the full study period. No individual technique showed a significant change, and overall methodological diversity increased rather than declined. We then ran a controlled experiment to see whether LLMs recommend a narrower set of methods than archaeologists have used in practice. Two different open-weight models were asked to suggest methods for 28 archaeological research problems, with prompts providing three levels of methodological guidance: novice, intermediate, and expert. Recommendation diversity was much lower than in the published literature, particularly without methodological guidance. The models also tended to favour methods that were widely used before 2023, and their recommendations more closely resembled the post-2023 literature. Taken together, these results are consistent with LLMs pushing methodological choice towards convergence, although our study cannot establish a causal effect. They raise a broader question: how can archaeology retain methodological diversity as LLMs become more involved in research?

cs.CY↗