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WhatsApp has introduced a novel avenue for smartphone users to engage withand disseminate news stories. The convenience of forming interest-based groupsand seamlessly sharing content has rendered WhatsApp susceptible to theexploitation of misinformation campaigns. While the process of fact-checkingremains a potent tool in identifying fabricated news, its efficacy falters inthe face of the unprecedented deluge of information generated on the Internettoday. In this work, we explore automatic ranking-based strategies to propose a"fakeness score" model as a means to help fact-checking agencies identify fakenews stories shared through images on WhatsApp. Based on the results, we designa tool and integrate it into a real system that has been used extensively formonitoring content during the 2018 Brazilian general election. Our experimentalevaluation shows that this tool can reduce by up to 40% the amount of effortrequired to identify 80% of the fake news in the data when compared to currentmechanisms practiced by the fact-checking agencies for the selection of newsstories to be checked.

Helping Fact-Checkers Identify Fake News Stories Shared through Images on WhatsApp