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.