"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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Fake News Detection Enhancement with Data Imputation
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TL;DR: A novel contrastive language adaptation approach applied to memory networks is introduced, which ensures accurate alignment of stances in the source and target languages, and can effectively deal with the challenge of limited labeled data in the target language.
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Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms
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Misinformation detection using multitask learning with mutual learning for novelty detection and emotion recognition
TL;DR: A deep multitask learning framework that jointly performs novelty detection, emotion recognition, and misinformation detection is proposed that achieves state-of-the-art (SOTA) performance for fake news detection on four benchmark datasets.
Book ChapterDOI
Semantic Fake News Detection: A Machine Learning Perspective
TL;DR: This work introduces a new semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text, showing that by adding semantic features the accuracy of fake news classification improves significantly.
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