"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
Posted Content
Suspicious News Detection Using Micro Blog Text.
Tsubasa Tagami,Hiroki Ouchi,Hiroki Asano,Kazuaki Hanawa,Kaori Uchiyama,Kaito Suzuki,Kentaro Inui,Atsushi Komiya,Atsuo Fujimura,Hitofumi Yanai,Ryo Yamashita,Akinori Machino +11 more
TL;DR: This task aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the articles.
DissertationDOI
A Study on the Improvement of Data Collection in Data Centers and Its Analysis on Deep Learning-based Applications
TL;DR: In this article , the authors present a survey of the state of the art in the field of artificial intelligence and artificial intelligence for the next few years, focusing on the following topics:
Journal ArticleDOI
A Unified Training Process for Fake News Detection Based on Finetuned Bidirectional Encoder Representation from Transformers Model.
TL;DR: In this paper , the authors proposed a unified training strategy to have a base structure for the classifier and all hyperparameters from individual models using a pretrained transformer model, which achieved 97% accuracy and achieved the F1 score of 0.97.
Proceedings ArticleDOI
EnFVe: An Ensemble Fact Verification Pipeline
John Joy Kurian,Deborah Zenobia Rachael Menezes,Avinash Ronanki,Gaurang Sharma,Sandeep Krishna Prasad,Ashish Chouhan,Ajinkya Prabhune +6 more
TL;DR: EnFVe as discussed by the authors is an end-to-end pipeline that integrates various components of fact verification and achieves an accuracy of 79.26%/73.28% on real-world data.
Posted Content
A Multi-Level Attention Model for Evidence-Based Fact Checking
TL;DR: The authors proposed a model that enables inter-sentence attentions at different levels and can benefit from joint training, which outperforms the graph-based approaches and yields 1.09% and 1.42% improvements in label accuracy and FEVER score.
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.