  We propose Factual News Graph (FANG), a novel graphical social context
representation and learning framework for fake news detection. Unlike previous
contextual models that have targeted performance, our focus is on
representation learning. Compared to transductive models, FANG is scalable in
training as it does not have to maintain all nodes, and it is efficient at
inference time, without the need to re-process the entire graph. Our
experimental results show that FANG is better at capturing the social context
into a high fidelity representation, compared to recent graphical and
non-graphical models. In particular, FANG yields significant improvements for
the task of fake news detection, and it is robust in the case of limited
training data. We further demonstrate that the representations learned by FANG
generalize to related tasks, such as predicting the factuality of reporting of
a news medium.
