"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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Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media
Yida Mu,Pu Niu,Nikolaos Aletras +2 more
TL;DR: This paper develops and makes publicly available a new dataset of Weibo users mapped into one of the two categories (i.e., misinformation posters or active citizens), and presents an extensive analysis of the differences in language use between the two user categories.
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Transforming Fake News: Robust Generalisable News Classification Using Transformers.
TL;DR: In this article, the authors explore the idea that opinion-based news articles cannot be classified as real or fake due to their subjective nature and often sensationalised language, and propose a novel two-step classification pipeline to remove such articles from both model training and the final deployed inference system.
Proceedings ArticleDOI
Fake News Detection in Social Media: A Systematic Review
TL;DR: A systematic review of the literature that brings an overview of this research area and analyzes the the high-quality studies about fake news detection, showing that Twitter and Weibo1 are the social media platform most applied by selected studies, and deep learning algorithms given the best detection results, specially LSTM.
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Generating Fact Checking Briefs
Angela Fan,Aleksandra Piktus,Fabio Petroni,Guillaume Wenzek,Marzieh Saeidi,Andreas Vlachos,Antoine Bordes,Sebastian Riedel +7 more
TL;DR: This work investigates how to increase the accuracy and efficiency of fact checking by providing information about the claim before performing the check, in the form of natural language briefs, and develops QABriefer, a model that generates a set of questions conditioned on the claim, searches the web for evidence, and generates answers.
Proceedings ArticleDOI
Evidence-based Factual Error Correction
James Thorne,Andreas Vlachos +1 more
TL;DR: This article proposed a two-stage distant supervision approach that incorporates evidence into masked claims when generating corrections, which achieved better results than existing work which used a pointer copy network and gold evidence.
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.