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The design and reporting of data-driven studies seeking to measure misinformation are patchy and inconsistent, and these studies rarely measure associations with, or effects on, behaviour. The consequence is that data-driven misinformation studies are not yet useful as an empirical basis for guiding when to act on emerging misinformation threats, or for deciding when it is more appropriate to do nothing to avoid inadvertently amplifying misinformation. In a narrative review focused on examples of health-related misinformation, we take a critical perspective of data-driven misinformation studies. To address this problem, we propose a curated and open library of misinformation examples and describe its structure and how it might be used to support actionable surveillance. We draw on experiences with other curated repositories to speculate on the likely challenges related to achieving critical mass and maintaining data consistency. We conclude that an open library of misinformation could help improve the consistency of data-driven misinformation study design and reporting, as well as provide an empirical basis from which to make decisions about how to act on new and emerging misinformation threats.

Knowing when to act: A call for an open misinformation library to guide actionable surveillance