In this paper, we present LIAR: a new, publicly available dataset for fake news detection. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. Publisher = "Association for Computational Linguistics",Ībstract = "Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts.
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Anthology ID: P17-2067 Volume: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) Month: July Year: 2017 Address: Vancouver, Canada Venue: ACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 422–426 Language: URL: DOI: 10.18653/v1/P17-2067 Bibkey: wang-2017-liar Copy Citation: BibTeX MODS XML Endnote More options… PDF: = ": A New Benchmark Dataset for Fake News Detection",īooktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", We show that this hybrid approach can improve a text-only deep learning model. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. This dataset can be used for fact-checking research as well. We collected a decade-long, 12.8K manually labeled short statements in various contexts from, which provides detailed analysis report and links to source documents for each case. Abstract Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts.
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