[论文解读] Fake News Detection using Semi-Supervised Graph Convolutional Network

Priyanka Meel, Dinesh Kumar Vishwakarma|arXiv (Cornell University)|Sep 28, 2021

Misinformation and Its Impacts参考文献 44被引用 7

一句话总结

本文提出了一种基于半监督图卷积网络（GCN）的框架，用于在有限标注数据下进行虚假新闻检测。该方法利用GloVe嵌入和词移距离（WMD）构建文本相似性图，并应用GCN进行二分类，在Real or Fake数据集上实现了95.27%的准确率，优于现有方法在低监督设置下的表现。

ABSTRACT

Social media becomes the central way for people to obtain and utilise news, due to its rapidness and inexpensive value of data distribution. Though, such features of social media platforms also present it a root cause of fake news distribution, causing adverse consequences on both people and culture. Hence, detecting fake news has become a significant research interest for bringing feasible real time solutions to the problem. Most current techniques of fake news disclosure are supervised, that need large cost in terms of time and effort to make a certainly interpreted dataset. The proposed framework concentrates on the text-based detection of fake news items while considering that only limited number of labels are available. Graphs are functioned extensively under several purposes of real-world problems on the strength of their property to structure things easily. Deep neural networks are used to generate great results within tasks that utilizes graph classification. The Graph Convolution Network works as a deep learning paradigm which works on graphs. Our proposed framework deals with limited amount of labelled data; we go for a semi-supervised learning method. We come up with a semi-supervised fake news detection technique based on GCN (Graph Convolutional Networks). The recommended architecture comprises of three basic components: collecting word embeddings from the news articles in datasets utilising GloVe, building similarity graph using Word Movers Distance (WMD) and finally applying Graph Convolution Network (GCN) for binary classification of news articles in semi-supervised paradigm. The implemented technique is validated on three different datasets by varying the volume of labelled data achieving 95.27 % highest accuracy on Real or Fake dataset. Comparison with other contemporary techniques also reinforced the supremacy of the proposed framework.

研究动机与目标

• 为应对社交媒体平台上虚假新闻泛滥的挑战。
• 减少对大规模人工标注数据集在虚假新闻检测中的依赖。
• 开发一种半监督学习框架，以有效利用有限的标注数据。
• 通过基于图的表示学习提升文本相似性检测性能。
• 在不同标签稀缺条件下，于多个基准数据集上验证所提方法的有效性。

提出的方法

• 使用预训练的GloVe模型从新闻文章中提取词嵌入。
• 利用词移距离（WMD）测量文章之间的语义相似性，构建文本相似性图。
• 在相似性图上应用图卷积网络（GCN）实现端到端的半监督分类。
• GCN模型仅使用少量标注样本，对新闻文章进行真实或虚假的二分类。
• 该框架以半监督方式端到端训练，同时利用标注数据和未标注数据。
• 在不同标签比例下，于三个基准数据集上评估模型性能。

实验结果

研究问题

• RQ1 基于半监督GCN的方法能否在极少标注数据下实现高准确率的虚假新闻检测？
• RQ2 基于WMD的图构建方法在捕捉新闻文章间语义关系方面效果如何？
• RQ3 在标签稀缺条件下，所提出的GCN框架是否优于现有的监督与半监督方法？
• RQ4 该模型在多样化的现实世界虚假新闻数据集上表现是否稳健？
• RQ5 不同标签比例对模型泛化性能有何影响？

主要发现

• 在低监督设置下，所提方法在Real or Fake数据集上达到了最高的95.27%准确率。
• 在所有评估数据集中，该模型在检测准确率方面均优于其他现有技术。
• 当标注数据有限时，半监督学习范式相比完全监督基线显著提升了性能。
• 使用WMD进行图构建增强了语义表征，使相似新闻文章聚类效果更优。
• 该框架在多个数据集上表现出强大的泛化能力，证实了其鲁棒性。
• 消融实验表明，GloVe、WMD和GCN各组件均对最终性能有显著贡献。