"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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Integrating Machine Learning Techniques in Semantic Fake News Detection
TL;DR: A semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text is discussed, showing that adding semantic features improves accuracy significantly.
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DUAL: A Deep Unified Attention Model with Latent Relation Representations for Fake News Detection
TL;DR: This paper uses an attention-based bi-directional Gated Recurrent Units (GRU) to extract features from news content and a deep model to extract hidden representations of the side information and proposes a hybrid attention model to leverage these clues.
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Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background
TL;DR: In this paper, the authors present the results of an extensive study based on crowdsourcing: they collect thousands of truthfulness assessments over two datasets, and compare expert judgments with crowd judgments, expressed on scales with various granularity levels.
Journal ArticleDOI
A novel self-learning semi-supervised deep learning network to detect fake news on social media.
TL;DR: Li et al. as discussed by the authors designed a self-learning semi-supervised deep learning network by adding a confidence network layer, which made it possible to automatically return and add correct results to help the neural network to accumulate positive sample cases.
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