"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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Linguistic Signals under Misinformation and Fact-Checking: Evidence from User Comments on Social Media
Shan Jiang,Christo Wilson +1 more
TL;DR: It is found that linguistic signals in user comments vary significantly with the veracity of posts, e.g., more misinformation-awareness signals and extensive emoji and swear word usage with falser posts, and that these signals can help to detect misinformation.
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GLTR: Statistical Detection and Visualization of Generated Text
TL;DR: GLTR as mentioned in this paper is a tool to support humans in detecting whether a text was generated by a model, using a suite of baseline statistical methods that can detect generation artifacts across multiple sampling schemes.
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Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims.
TL;DR: A thorough analysis of the dataset is provided to highlight key underlying language understanding challenges, and it is shown that human baselines across multiple subtasks far outperform ma-chine baselines built upon state-of-the-art NLP techniques.
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Beyond News Contents: The Role of Social Context for Fake News Detection
Kai Shu,Suhang Wang,Huan Liu +2 more
TL;DR: A tri-relationship embedding framework TriFN is proposed, which models publisher-news relations and user-news interactions simultaneously forfake news classification and significantly outperforms other baseline methods for fake news detection.
Proceedings ArticleDOI
Fake News Detection Using Machine Learning approaches: A systematic Review
TL;DR: Various Machine learning approaches in detection of fake and fabricated news are reviewed and the limitation of such and approaches and improvisation by way of implementing deep learning is also reviewed.
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