"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
Citations
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Modeling Entity Knowledge for Fact Verification
TL;DR: Li et al. as discussed by the authors propose a novel fact verification model using entity knowledge to enhance its performance, which retrieves descriptive text from Wikipedia for each entity, and then encode these descriptions by a smaller lightweight network to be fed into the main verification model.
Posted Content
SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?.
TL;DR: In this paper, the authors extensively analyze a wide range of different solutions for the early detection of fake news in the existing literature and evaluate the online web browsing tools available for detecting and mitigating fake news and present some open research challenges.
Proceedings ArticleDOI
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines
TL;DR: Gabriel, Skyler, Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi, and Eun Choi as discussed by the authors presented a paper at the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Book ChapterDOI
FNH—A Data Repository for Studying Fake News in Healthcare Domain
TL;DR: Fake News on Health Care (FNH) as mentioned in this paperNH is an assembled dataset on fake news in the healthcare domain, which comprises labeled news items, the publishing date of news, source URL, and dynamic information to facilitate fake news-related research in the Healthcare area.
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