  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.
