"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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Book ChapterDOI
Statistical Methods for Conducting the Ontology and Classifications of Fake News on Social Media
TL;DR: Thematic analysis of responses of certain respondents reveal three new classifications of fake news that people propagate on social media on the basis of mode of propagation, motives of perpetrators, and impacts on victims.
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From Documents to Data: A Framework for Total Corpus Quality
TL;DR: A conceptual framework for total corpus quality incorporating three important dimensions—total corpus error, corpus comparability, and corpus reproducibility—impacting the validity and reliability of inferences drawn from textual data is proposed.
Proceedings ArticleDOI
A Stylometric Approach for Reliable News Detection Using Machine Learning Methods
TL;DR: In this paper , the authors proposed a model that can distinguish between reliable and unreliable news articles using stylometric features, which can act as a filter in the fake news detection pipeline such that only the reliable articles are pushed to the endpoint of the pipeline.
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Intra-graph and Inter-graph joint information propagation network with third-order text graph tensor for fake news detection
TL;DR: IIJIPN as discussed by the authors proposed Intra-graph and Inter-graph Joint Information Propagation Network with Third-order Text Graph Tensor for fake news detection, where data augmentation is firstly utilized to solve the data imbalance and strengthen the small corpus.
Book ChapterDOI
Combating Fake News with Machine Learning and Deep Learning Methods
TL;DR: In this paper , the authors explore the route of applying machine learning and deep learning algorithms to identify fake news from real news, and they find that LSTM algorithm is able to give an accuracy of 61%.
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.