【学习】ACL 2017的短文本假新闻检测数据集及论文

“Liar,Liar Pants on Fire”:
ANew Benchmark Dataset for Fake News Detection

William Yang Wang

Department of Computer ScienceUniversity of California, Santa Barbara Santa Barbara, CA 93106 USA [email protected]

Abstract 

Automatic fake news detection is a chal- lenging problem in deception detection, and it has tremendous real-world politi- cal and social impacts. However, statis- tical 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 man- ually 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 re- search as well. Notably, this new dataset is an order of magnitude larger than pre- viously largest public fake news datasets of similar type. Empirically, we investi- gate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolu- tional neural network to integrate meta- data with text. We show that this hybrid approach can improve a text-only deep learning model. 

ACL 2017的短文本假新闻检测数据集及论文

链接：

https://www.cs.ucsb.edu/~william/papers/acl2017.pdf