Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

TL;DR

This paper introduces a framework that searches for fact-checking articles related to social media posts containing misinformation, aiming to reduce the spread of fake news by informing and discouraging users.

Contribution

It presents a novel multi-modal search framework combining text and image analysis to find relevant fact-checking articles for social media content.

Findings

Achieves promising results on real-world datasets

Effectively links misinformation to fact-checking content

Potential to scale up verified information on social media

Abstract

Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users' consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.