"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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Journal ArticleDOI
Fake news detection: a survey of evaluation datasets
TL;DR: In this article, the authors systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them, along with a set of requirements for comparing and building new datasets.
Proceedings ArticleDOI
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
TL;DR: A data collection and linking system to build a public misinformation graph dataset (MuMiN), containing rich social media data spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages.
Proceedings ArticleDOI
F-NAD: An Application for Fake News Article Detection using Machine Learning Techniques
TL;DR: This work aims to identify a news articles whether it is real or misleading using an ensemble technique of state of the art recurrent neural networks (LSTM and GRU) and an android application for determining the sanctity of a news article.
Book ChapterDOI
Continual Learning for Fake News Detection from Social Media
TL;DR: In this article, the authors show that the performance of a model trained on one dataset degrades on another and potentially vastly different dataset, which is similar to the problem of catastrophic forgetting in the field of continual learning.
Journal ArticleDOI
The many dimensions of truthfulness: Crowdsourcing misinformation assessments on a multidimensional scale
Michael Soprano,Kevin Roitero,David La Barbera,Davide Ceolin,Damiano Spina,Stefano Mizzaro,Gianluca Demartini +6 more
TL;DR: This article proposed a multidimensional notion of truthfulness and asked the crowd workers to assess seven different dimensions based on existing literature: Correctness, Neutrality, Comprehensibility, Precision, Completeness, Speaker's Trustworthiness, and Informativeness.
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