[论文解读] Graph Neural Networks with Continual Learning for Fake News Detection from Social Media

Yi Han, Shanika Karunasekera+1 more|arXiv (Cornell University)|Jul 7, 2020

Misinformation and Its Impacts参考文献 60被引用 77

一句话总结

该论文使用基于传播的图神经网络在不使用文本内容的情况下检测假新闻，并引入持续学习以在面对新、未见数据时在不完全再训练的情况下维持性能。

ABSTRACT

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.

研究动机与目标

• 以非文本社交上下文特征为基础，推动基于传播的假新闻检测。
• 评估GNNs在不使用推文内容的传播模式下如何区分假新闻与真实新闻。
• 研究模型在未见数据集上的性能以及朴素增量训练的局限性。
• 提出持续学习技术，以在现有数据和新数据之间实现平衡性能。

提出的方法

• 将新闻的传播模式建模为图，其中节点是推文/用户，额外的节点表示新闻条目。
• 使用 DiffPool，一种基于 GraphSage 的图分类GNN，对传播图进行分类。
• 从非文本特征构建邻接矩阵和特征矩阵，例如用户个人资料属性和时间线派生指标。
• 尝试不同的特征集（用户资料、时间线特征，或两者）以及有/无关注者关系边的情况。
• 在 PolitiFact 和 GossipCop 数据集上使用准确率、精确率、召回率和F1，在多个随机分割下进行评估。
• 应用持续学习方法（GEM 和 EWC）以减轻在学习多个数据集时的灾难性遗忘。

实验结果

研究问题

• RQ1 GNNs 是否能仅使用非文本传播模式来识别假新闻，而不使用推文内容？
• RQ2 在一个数据集上训练的 GNN 在不同的、未见的数据集上如何表现，持续学习是否能改善跨数据集的泛化？

主要发现

• 使用非文本传播特征的GNN在所评估的数据集上实现与基于文本的最新方法相当甚至更优的性能。
• 在单一数据集上训练的模型在另一数据集上表现较差，朴素的增量训练无法在数据集之间实现平衡。
• 结合持续学习技术（GEM、EWC）在数据集之间获得更均衡的性能，其中在实验中 GEM 通常优于 EWC。
• 在所考虑的设置中，添加关注/被关注关系对性能没有显著提升。
• 训练收敛迅速，大多数模型在适度的轮数内达到稳定性能。