How Data Journalists Visualize Uncertainty: Editorial Constraints and Design Heuristics in Newsroom Practice

Abstract

Communicating uncertainty is increasingly recognized as important in data journalism, yet in practice it is often oversimplified, downplayed, or omitted. To understand how uncertainty is handled in newsroom visualization practice, we conducted a full-day workshop with 11 data journalists from major German-speaking newsrooms. The workshop combined presentations of newsroom examples, collaborative scenario classification, World Café discussions on authoring hurdles, and group reflections on audience needs and future directions. Our findings suggest that uncertainty communication is primarily a workflow- and context-dependent editorial decision, constrained by production overhead, anticipated audience misinterpretation, publication formats, and the need to preserve a clear takeaway. Rather than relying on advanced visualization techniques, practitioners favor pragmatic defaults, layered communication, and explanatory guidance tailored to audience and medium. The scenario-based framework we provided is valued less as a strict taxonomy for classifying finished visualizations than as a planning prompt for early design reasoning. Based on these insights, we contribute (i) practitioner-informed findings on the challenges and strategies of visualizing uncertainty in journalistic practice, (ii) a refined scenario-based framework to support communication about uncertainty, and (iii) practice-oriented heuristics for designing uncertainty visualizations in newsrooms. Our work contributes to bridging visualization research and newsroom practice by grounding uncertainty communication in the realities of journalistic production. Supplemental material is available at https://osf.io/8j4ep.