"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
William Yang Wang
- Vol. 2, pp 422-426
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TLDR
Li et al. as discussed by the authors designed a hybrid convolutional neural network to integrate meta-data with text and showed that this hybrid approach can improve a text-only deep learning model.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.read more
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Exploiting Tri-Relationship for Fake News Detection.
Kai Shu,Suhang Wang,Huan Liu +2 more
TL;DR: This paper explores the correlations of publisher bias, news stance, and relevant user engagements simultaneously, and proposes a Tri-Relationship Fake News detection framework (TriFN), and provides two comprehensive real-world fake news datasets to facilitate fake news research.
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TL;DR: In this paper, a theory-driven model is proposed for fake news detection, which represents news at each level, relying on well-established theories in social and forensic psychology, and then conducts real-world data mining to detect fake news.
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The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
TL;DR: In this article, the authors provide a typology of the Web's false-information ecosystem, composed of various types of false information, actors, and their motives, which can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections.
Proceedings ArticleDOI
Fake News vs Satire: A Dataset and Analysis
Jennifer Golbeck,Matthew Louis Mauriello,Brooke E. Auxier,Keval H. Bhanushali,Christopher Bonk,Mohamed Amine Bouzaghrane,Cody Buntain,Riya Chanduka,Paul Cheakalos,Jennine B. Everett,Waleed Falak,Carl Gieringer,Jack Graney,Kelly M. Hoffman,Lindsay Huth,Zhenya Ma,Mayanka Jha,Misbah Khan,Varsha Kori,Elo Lewis,George Mirano,William T. Mohn,Sean Mussenden,Tammie M. Nelson,Sean Mcwillie,Akshat Pant,Priya Shetye,Rusha Shrestha,Alexandra Steinheimer,Aditya Subramanian,Gina Visnansky +30 more
TL;DR: This work presents a dataset of fake news and satire stories that are hand coded, verifiable, and, in the case offake news, include rebutting stories, and includes a thematic content analysis of the articles, identifying major themes that include hyperbolic support or con- demnation of a gure, conspiracy theories, racist themes, and dis- crediting of reliable sources.
Proceedings ArticleDOI
That is a Known Lie: Detecting Previously Fact-Checked Claims
TL;DR: Learning-to-rank experiments that demonstrate sizable improvements over state-of-the-art retrieval and textual similarity approaches are presented that are largely ignored by the research community so far.
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