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Computer Science, Computers and Society, cs.CY,cs.SI
Abstract:
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 this paper, we propose a framework to study fact checking
efforts using Google Trends, a signal that captures search interest over topics
on the world's largest search engine. Our framework consists of extracting
claims from fact checking efforts, linking such claims with knowledge graph
entities, and estimating the online attention they receive. We use this
framework to study 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 where 10 or more claims were fact checked, half or more
than half of the top 10 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 big 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 better assess and prioritize their fact checking
efforts. 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 different
countries.
