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

TL;DR

This paper presents an automatic 'fakeness score' model to assist fact-checkers in identifying fake news shared via images on WhatsApp, significantly reducing effort in detecting misinformation during elections.

Contribution

It introduces a novel ranking-based strategy and a practical tool for fact-checkers to efficiently identify fake news shared through images on WhatsApp.

Findings

The tool can reduce effort by up to 40% in identifying 80% of fake news.

The system was successfully used during the 2018 Brazilian general election.

Experimental results demonstrate improved efficiency over existing fact-checking methods.

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.