Seeing like an API: Platform-mediated research and the politics of access

This study examines the challenges that researchers faced while accessing, interpreting, and assessing data accessed via Meta's now-sunset CrowdTangle (CT) application programming interface (API). Drawing on interviews with 20 academic users, we introduce the framework of Seeing like an API to illustrate how CT's technical design, governance rules, and platform-mediated knowledge networks shaped what researchers could see, ask, and conclude about platform activity. Participants adapted to limitations such as follower-count thresholds, the exclusion of comment data, and opaque content removal through three recurring strategies: optimizing the API's capabilities, extending data visibility with supplementary methods, and verifying outputs against external sources. These adaptations were developed in response to ongoing uncertainty about data completeness and provenance, which constrained both the scope of feasible research questions and the reliability of resulting analyses. Our findings show how platform-controlled data infrastructures shape research design, collaboration, and epistemic norms, and they inform emerging models for independent, third-party-regulated data access, such as those envisioned in the European Union's Digital Services Act.