“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection
William Yang Wang Department of Computer Science University of California, Santa Barbara Santa Barbara, CA 93106 USA
Abstract
Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present liar: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.
中文速览
虚假新闻的自动检测长期受制于缺乏大规模标注数据集，为此研究者从PolitiFact.com收集了跨越十年、共1.28万条人工标注的短陈述，构建了名为LIAR的公开基准数据集，每条记录不仅有六级细粒度真实性标签，还附带发言者身份、党派、历史信用记录等丰富元数据。在此基础上，研究者设计了一种混合卷积神经网络，将文本特征与元数据融合建模，实验结果显示该方案比纯文本深度学习模型取得了更高的分类准确率。LIAR比此前同类最大公开数据集大一个数量级，填补了该领域的数据空白，为假新闻检测、自动事实核查及政治语言分析等下游任务提供了可靠的研究基础。
原文 arXiv:1705.00648;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1705.00648v1