Fact-Check Your Information (FYI): A Design Probe to Understand How People Actually Fact-Check Data-Driven Articles
* Equal contribution
IEEE TVCG · 2027
Abstract
Data claims, statements grounded in numbers and statistics, are common in journalism and policy reports, yet verifying them requires connecting textual claims with structured data. Existing tools typically separate automated checking from manual exploration, leaving unclear how readers coordinate AI assistance and direct evidence inspection in practice. We design FYI, a browser extension that bridges this gap through four complementary tools within a unified side panel, spanning the spectrum from automation to user-driven exploration. In an exploratory study (N = 22), participants used FYI to fact-check claims in a data-driven article. We find that participants adopted three distinct workflow archetypes—AI-first with manual confirmation, manual-first with AI supplement, and parallel co-review—with visualization serving as the primary mechanism for auditing AI conclusions. Trust in AI shifted dynamically, growing when multiple tools converged and eroding when AI outputs were inconsistent. These findings suggest that fact-checking systems should treat AI as a starting point that human verification complements rather than a definitive authority, elevate visualization as a core verification capability, and support flexible, user-driven workflows. We contribute FYI to the community for further research as open-source software at github.com/DataVisards/FYI.
Citation
@article{truong2026fyi,
author = {Nguyen-Truong, Thinh and Du, Yuxuan and Konrad, Phongsakon Mark and Narechania, Arpit},
title = {{Fact-Check Your Information (FYI): A Design Probe to Understand How People Actually Fact-Check Data-Driven Articles}},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2027},
publisher = {IEEE},
note = {To appear. Presented at IEEE VIS 2026}
}