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

• URL: http://arxiv.org/abs/2308.14782v1
• Date: Mon, 28 Aug 2023 16:12:29 GMT
• Title: Helping Fact-Checkers Identify Fake News Stories Shared through Images on WhatsApp
• Authors: Julio C. S. Reis, Philipe Melo, Fabiano Bel\'em, Fabricio Murai, Jussara M. Almeida, Fabricio Benevenuto
• Abstract summary: We propose a "fakeness score" model as a means to help fact-checking agencies identify fake news stories shared through images on WhatsApp. 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.
• Score: 1.5678677448474552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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