"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
Book ChapterDOI
On Unsupervised Methods for Fake News Detection
TL;DR: In this article, the authors provide an overview of unsupervised learning methods with a focus on their conceptual foundations, and analyze the conceptual bases with a critical eye and outline other kinds of conceptual building blocks that could be used in devising un-supervised fake news detection methods.
Proceedings ArticleDOI
Cardiff University at SemEval-2019 Task 4: Linguistic Features for Hyperpartisan News Detection
TL;DR: In this article, two different approaches are compared: a SVM classifier based on word vector averages and hand-crafted linguistic features, and a BiLSTM-based neural text classifier trained on a filtered training set.
Journal ArticleDOI
Automating the Evaluation of Education Apps With App Store Data
TL;DR: In this article, an approach to automate the identification and comparison of iPAC relevant apps is presented. But the authors focus on the identification of the relevant apps and do not discuss how their findings can be useful for teachers, students, and app vendors.
Posted Content
A Review on Fact Extraction and Verification
TL;DR: In this paper, the authors focus on the task of Fact Extraction and Verification (FEVER) and its accompanied dataset, which consists of the subtasks of retrieving the relevant documents (and sentences) from Wikipedia and validating whether the information in the documents supports or refutes a given claim.
A Survey on Role of Machine Learning and NLP in Fake News Detection on Social Media
TL;DR: In this article, the role of NLP and machine learning in the fake news detection process, and various detection techniques based on these are systematically discussed and discussed the future trends, open issues, challenges, and potential research oriented toward NLP-based approaches.
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.