论文标题
fang：利用社会环境使用图表来检测假新闻检测
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
论文作者
论文摘要
我们提出了事实新闻图（Fang），这是一个新颖的图形社会环境表示和假新闻检测的学习框架。与以前针对性能的上下文模型不同，我们的重点是表示学习。与转导模型相比，Fang在训练中是可扩展的，因为它不必保持所有节点，并且在推理时间有效，而无需重新处理整个图。我们的实验结果表明，与最近的图形和非图形模型相比，方更好地将社会环境捕获为高保真表示。尤其是，方对于虚假新闻检测的任务产生了重大改进，并且在培训数据有限的情况下，它是强大的。我们进一步证明，Fang学到的表示形式将其推广到相关任务，例如预测新闻媒介报告的事实。
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.
