"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
Journal ArticleDOI
The Opportunities and Limitations of Using Artificial Neural Networks in Social Science Research
TL;DR: This paper argues that the social science community can benefit from using ANNs in a number of ways, especially by outsourcing laborious data coding and pre-processing tasks to machines in the early stages of analysis.
Proceedings ArticleDOI
Exploring the Impact of Machine Translation on Fake News Detection: A Case Study on Persian Tweets about COVID-19
TL;DR: In this paper, the authors explored the impacts of machine translation on fake news detection in low resource languages like Persian and found that machine translation has a 4 % negative impact on binary classification accuracy and a 23% negative effect on multiclass classification.
Journal ArticleDOI
Fake news detection on social media using a natural language inference approach
TL;DR: Zhang et al. as mentioned in this paper proposed a novel fake news detection method based on Natural Language Inference (NLI) approach which exploits a human-like approach, which is based on inferring veracity using a set of reliable news.
Proceedings ArticleDOI
Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification
Bibek Upadhayay,Vahid Behzadan +1 more
TL;DR: This paper proposed a deep learning approach for automated detection of false short-text claims on social media by extending the LIAR dataset of short claims by adding features based on sentiment and emotion analysis of claims.
Journal ArticleDOI
Combating multimodal fake news on social media: methods, datasets, and future perspective
Sakshini Hangloo,Bhavna Arora +1 more
TL;DR: A comprehensive overview of the state-of-the-art techniques for combating fake news on online media with the prime focus on deep learning (DL) techniques keeping multimodality under consideration is presented in this article .
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.