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Fact-checking databases, as important results of fact checkers’ epistemic work, are increasingly tied together in new overarching infrastructures, but these are understudied and lack transparency despite being an important societal baseline for whether claims are false. This article conceptualizes fact-checking as infrastructure and constructs a mixed-methods approach to examine overlaps and differences and thereby detect biases to increase transparency in COVID-19 misinformation infrastructure at scale. Analyzing Poynter and Google as such overarching infrastructures, we found only a small overlap. Fewer fact-checkers contribute to Google, with fewer stories than to Poynter. 75% of claims in Google are fact-checked by Asian and North American fact-checkers (44% for Poynter) but none by South Americans (20% for Poynter). More stories in Poynter originate from Facebook than outside social media (43% vs. 17%), while Google shows the opposite (16% vs. 38%). In Google, claims originate to a larger extent from public persons. We find similar large topics on “statistics” and “cures,” but also differences regarding smaller topics (e.g., “vaccines”) and types of misinformation (e.g., “virus characteristics”). Thus, the article shows that the infrastructures have inherent biases and argue that making visible such biases will increase transparency for stakeholders using it.

Digital Infrastructures of COVID-19 Misinformation: A New Conceptual and Analytical Perspective on Fact-Checking