"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
William Yang Wang
- Vol. 2, pp 422-426
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
A Deep Transfer Learning Approach for Fake News Detection
TL;DR: Evaluation on the existing benchmark datasets, namely Fake News Challenge (FNC) dataset and FNC-I: Stance Detection show the efficacy of the proposed approach in comparison to the state-of-the-art systems.
Proceedings ArticleDOI
DialFact: A Benchmark for Fact-Checking in Dialogue
TL;DR: In this paper , the authors introduce the task of fact-checking in dialogue, which is a relatively unexplored area, and construct DialFact, a testing benchmark dataset of 22,245 annotated conversational claims, paired with pieces of evidence from Wikipedia.
Book ChapterDOI
Fake News Detection Techniques for Social Media
TL;DR: In this article, the authors discuss the features that are used to identify fake news and different categories of fake news detection techniques and outline the datasets available for fake news Detection and provide the directions for further reading.
Posted Content
Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task
TL;DR: The authors proposed an approach to combine topical distributions from Latent Dirichlet Allocation (LDA) with contextualized representations from XLNet to detect fake news in English, achieving an F1-score of 0.967.
Proceedings ArticleDOI
Factuality Checking in News Headlines with Eye Tracking
Christian Hansen,Casper Hansen,Jakob Grue Simonsen,Birger Larsen,Stephen Alstrup,Christina Lioma +5 more
TL;DR: An ensemble learner is built that predicts news headline factuality using only eye-tracking measurements and is better at detecting false than true headlines.
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.