LAUSR

dataset, with an AUC of 75%. This result represents a cautionary tale for future research in this domain. While using a broad range of features and sophisticated techniques, FANG is far from reaching optimal performance, which certainly makes sense given the complexity of the task at hand. Furthermore, this relatively modest score was obtained on a highly curated dataset composed of two unambiguous classes only, while reality is unfortunately much more intricate: fact-checking websites such as Snopes typically consider a whole range of non-binary labels to classify the articles they investigate, leveraging finegrained ratings such as mostly false, satire, misattributed, or unproven for challenging cases. The paper was recognized with the Best Paper Award at CIKM in late 2020—no small feat considering that almost 1,000 papers were submitted to the conference’s main research track. Even if the performance of fully automated approaches like FANG on non-curated datasets remains largely unclear, the paper is a very compelling piece of work combining a new contextual graph model for social media with recent advances in representation learning to tackle an important and timely problem. I hope you enjoy reading it as much as I have.