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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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Journal ArticleDOI

Arabic Fake News Detection Based on Textual Analysis

TL;DR: In this paper , a supervised machine learning model that classifies Arabic news articles based on their context's credibility was introduced, and the first dataset of Arabic fake news articles composed through crowdsourcing was also introduced.
Journal ArticleDOI

DTN: Deep triple network for topic specific fake news detection

TL;DR: A deep triple network (DTN) is proposed that leverages knowledge graphs to facilitate fake news detection with triple-enhanced explanations and results show that DTN outperforms conventionalfake news detection methods from different aspects, including the provision of factual evidence supporting the decision of fake news Detection.
Journal ArticleDOI

Combating the infodemic: COVID-19 induced fake news recognition in social media networks

TL;DR: The authors presented an early fusion-based method for combining key features extracted from context-based embeddings such as BERT, XLNet, and ELMo to enhance context and semantic information collection from social media posts and achieve higher accuracy for false news identification.
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Stance Prediction and Claim Verification: An Arabic Perspective

TL;DR: Results hint that while the linguistic features and world knowledge learned during pretraining are useful for stance prediction, such learned representations are insufficient for verifying claims without access to context or evidence.
Proceedings ArticleDOI

Five shades of untruth: finer-grained classification of fake news

TL;DR: This paper systematically explore a variety of signals from both news and social media, and gives an analysis of the underlying features of news articles and claims.
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