  Recent research suggests that not all fact-checking efforts are equal: when
and what is fact-checked plays a pivotal role in effectively correcting
misconceptions. In that context, signals capturing how much attention specific
topics receive on the Internet have the potential to study (and possibly
support) fact-checking efforts. This paper proposes a framework to study
fact-checking with online attention signals. The framework consists of: 1)
extracting claims from fact-checking efforts; 2) linking such claims with
knowledge graph entities; and 3) estimating the online attention these entities
receive. We use this framework to conduct a preliminary study of a dataset of
879 COVID-19-related fact-checks done in 2020 by 81 international
organizations. Our findings suggest that there is often a disconnect between
online attention and fact-checking efforts. For example, in around 40% of
countries that fact-checked ten or more claims, half or more than half of the
ten most popular claims were not fact-checked. Our analysis also shows that
claims are first fact-checked after receiving, on average, 35% of the total
online attention they would eventually receive in 2020. Yet, there is a
considerable variation among claims: some were fact-checked before receiving a
surge of misinformation-induced online attention; others are fact-checked much
later. Overall, our work suggests that the incorporation of online attention
signals may help organizations assess their fact-checking efforts and choose
what and when to fact-check claims or stories. Also, in the context of
international collaboration, where claims are fact-checked multiple times
across different countries, online attention could help organizations keep
track of which claims are "migrating" between countries.
