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Open AccessProceedings ArticleDOI

"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection

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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.

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Citations
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Book ChapterDOI

Linguistic Cues of Deception in a Multilingual April Fools’ Day Context

TL;DR: In this article , a collection of deceptive April Fools' Day (AFD) news articles is presented as a useful addition in existing datasets for deception detection tasks, which are relatively easy to construct across languages.
Book ChapterDOI

Detection of Fake News by Machine Learning with Linear Classification Algorithms: A Comparative Study

TL;DR: In this paper , the authors compare linear classifiers in machine learning to see which ones are the most effective at detecting false news, and the proposed work is recognized the fake news and distinguishes it with the least amount of work and time, as well as the highest accuracy.
Book ChapterDOI

Detection of Fake News Based on Domain Analysis and Social Network Psychology

TL;DR: In this paper, the authors proposed a novel 3-phase approach for the identification of fake news, which extracted features like WHOIS and DNS from domain information and used for the model construction.
Book ChapterDOI

Efficient Prediction of Fake News Using Novel Ensemble Technique Based on Machine Learning Algorithm

TL;DR: In this paper , a novel machine learning algorithm was used to detect fake news from Twitter, Facebook, and other social media using logistic regression, SVM, and novel ensemble approach based on machine learning algorithms.
References
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Proceedings Article

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