  WhatsApp has introduced a novel avenue for smartphone users to engage with
and disseminate news stories. The convenience of forming interest-based groups
and seamlessly sharing content has rendered WhatsApp susceptible to the
exploitation of misinformation campaigns. While the process of fact-checking
remains a potent tool in identifying fabricated news, its efficacy falters in
the face of the unprecedented deluge of information generated on the Internet
today. In this work, we explore automatic ranking-based strategies to propose a
"fakeness score" model as a means to help fact-checking agencies identify fake
news stories shared through images on WhatsApp. Based on the results, we design
a tool and integrate it into a real system that has been used extensively for
monitoring content during the 2018 Brazilian general election. Our experimental
evaluation shows that this tool can reduce by up to 40% the amount of effort
required to identify 80% of the fake news in the data when compared to current
mechanisms practiced by the fact-checking agencies for the selection of news
stories to be checked.
