"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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Machine Learning Approach to Fact-Checking in West Slavic Languages
TL;DR: This paper presented datasets for Czech, Polish, and Slovak for fake news detection and closely related fact-checking in the West Slavic languages, which set a baseline for further research into this area.
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Research status of deep learning methods for rumor detection
TL;DR: Wang et al. as mentioned in this paper divided deep learning models of rumor detection into CNN, RNN, GNN, and Transformer based on the model structure, which is convenient for comparison.
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Disinformation Detection: A review of linguistic feature selection and classification models in news veracity assessments.
TL;DR: This paper looks at the work that has already been done in applying machine learning approaches to detect deliberately deceptive news articles and examines the effects of this work during the 2016 United States Presidential Election.
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MetaICL: Learning to Learn In Context
TL;DR: Meta-training for In-Context Learning (MetaICL) as discussed by the authors is a meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learn-ing on a large set of training tasks.
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A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN
TL;DR: In this paper, a bi-directional recurrent neural network (RNN) classification model was trained on interpretable features derived from multi-disciplinary integrated approaches to language and applied to two benchmark datasets.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
Journal Article
Natural Language Processing (Almost) from Scratch
TL;DR: A unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling is proposed.
Proceedings Article
Character-level convolutional networks for text classification
TL;DR: In this paper, the use of character-level convolutional networks (ConvNets) for text classification has been explored and compared with traditional models such as bag of words, n-grams and their TFIDF variants.