Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, remains a serious problem. In this work, we focus on propagation-based fake news detection, as recent studies have demonstrated that fake news and real news spread differently online. Specifically, considering the capability of graph neural networks (GNNs) in dealing with non-Euclidean data, we use GNNs to differentiate between the propagation patterns of fake and real news on social media. In particular, we concentrate on two questions: (1) Without relying on any text information, e.g., tweet content, replies and user descriptions, how accurately can GNNs identify fake news? Machine learning models are known to be vulnerable to adversarial attacks, and avoiding the dependence on text-based features can make the model less susceptible to the manipulation of advanced fake news fabricators. (2) How to deal with new, unseen data? In other words, how does a GNN trained on a given dataset perform on a new and potentially vastly different dataset? If it achieves unsatisfactory performance, how do we solve the problem without re-training the model on the entire data from scratch? We study the above questions on two datasets with thousands of labelled news items, and our results show that: (1) GNNs can achieve comparable or superior performance without any text information to state-of-the-art methods. (2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection. In order to solve the problem, we propose a method that achieves balanced performance on both existing and new datasets, by using techniques from continual learning to train GNNs incrementally.

中文翻译：

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具有持续学习功能的图神经网络用于从社交媒体中检测假新闻

尽管在事实核查方面付出了巨大努力，但社交媒体上假新闻的盛行对正义、公众信任和我们的社会产生了深远的影响，仍然是一个严重的问题。在这项工作中，我们专注于基于传播的假新闻检测，因为最近的研究表明假新闻和真实新闻在网上的传播方式不同。具体来说，考虑到图神经网络 (GNN) 在处理非欧数据方面的能力，我们使用 GNN 来区分社交媒体上假新闻和真实新闻的传播模式。我们特别关注两个问题：（1）不依赖任何文本信息，例如推文内容、回复和用户描述，GNN 识别假新闻的准确度如何？众所周知，机器学习模型容易受到对抗性攻击，避免对基于文本的特征的依赖可以使模型不易受到高级假新闻制造者的操纵。(2) 如何处理新的、看不见的数据？换句话说，在给定数据集上训练的 GNN 在新的和可能截然不同的数据集上表现如何？如果它的性能不令人满意，我们如何在不从头开始对整个数据重新训练模型的情况下解决问题？我们在具有数千个标记新闻条目的两个数据集上研究了上述问题，我们的结果表明：（1）GNN 可以在没有任何文本信息的情况下实现与最先进方法相当或更好的性能。(2) 在给定数据集上训练的 GNN 在新的、看不见的数据上可能表现不佳，而直接增量训练并不能解决这个问题——这个问题在之前将 GNN 应用于假新闻检测的工作中没有得到解决。为了解决这个问题，我们提出了一种方法，通过使用持续学习技术逐步训练 GNN，在现有数据集和新数据集上实现平衡性能。