"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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Detection of Online Fake News Using Blending Ensemble Learning
TL;DR: In this article, a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression was used to predict if a news report is true or not.
Proceedings ArticleDOI
Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media
TL;DR: A fake news detection model named Post-User Interaction Network (PSIN) is proposed, which adopts a divide-and-conquer strategy to model the post-post, user-user and post-user interactions in social context effectively while maintaining their intrinsic characteristics.
Proceedings ArticleDOI
Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification
Shyam Subramanian,Kyumin Lee +1 more
TL;DR: Hierarchical Evidence Set Modeling (HESM) as discussed by the authors is a framework to extract evidence sets (each of which may contain multiple evidence sentences), and verify a claim to be supported, refuted or not enough info, by encoding and attending the claim and evidence sets at different levels of hierarchy.
Detecting COVID-19 Misinformation on Social Media
Tamanna Hossain,Robert L. Logan,Arjuna Ugarte,Yoshitomo Matsubara,Sameer Singh,Sean D. Young +5 more
TL;DR: COVID-Lies1, a dataset of 5K expert-annotated tweets to evaluate the performance of misinformation detection systems on 86 different pieces of COVID-19 related misinformation, is released, providing first benchmarks and identifying key challenges for future models to improve upon.
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