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.

本研究提出了基于社交上下文的图表达与学习框架——Factual News Graph (FANG)，用于解决假新闻检测问题。与以往的上下文模型不同，我们的重点在于表示学习。实验结果表明，FANG能够更好地捕捉社交上下文，并且针对有限的训练数据具有鲁棒性。此外，我们还证明FANG学习的表示能够推广到相关任务。

FANG：利用图表示法结合社交环境进行假新闻检测