Open AccessPosted Content
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
TLDR
Liar as mentioned in this paper is a large dataset of 12.8k manually labeled short statements in various contexts from this http URL, which provides detailed analysis report and links to source documents for each case.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 this http URL, 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
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Fake News Detection on Social Media: A Data Mining Perspective
TL;DR: This survey presents a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets, and future research directions for fake news detection on socialMedia.
Proceedings ArticleDOI
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
TL;DR: Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.
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FEVER: a large-scale dataset for Fact Extraction and VERification
TL;DR: This paper introduces a new publicly available dataset for verification against textual sources, FEVER, which consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from.
Proceedings Article
Defending Against Neural Fake News
Rowan Zellers,Ari Holtzman,Hannah Rashkin,Yonatan Bisk,Ali Farhadi,Franziska Roesner,Yejin Choi +6 more
TL;DR: A model for controllable text generation called Grover, found that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data, and the best defense against Grover turns out to be Grover itself, with 92% accuracy.
Journal ArticleDOI
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media
TL;DR: A fake news data repository FakeNewsNet is presented, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information, and is discussed for potential applications on fake news study on social media.
References
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