"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
Combining Conceptual Graphs and Sentiment Analysis for Fake News Detection
Walter Cuenca,César A. González-Fernández,Alberto Fernández-Isabel,Isaac Martín de Diego,Alejandro Martín +4 more
Dissertation
Automated false claims detection using deep neural networks
TL;DR: This thesis proposes deep neural models which avoid tedious feature engineering and strong assumptions and yet detect false claims with high accuracy, and proposes a hybrid model which combines textual content of the news articles as well as the reactions they receive in social media forums such as Reddit.
Journal ArticleDOI
Studying Effectiveness of Transformers Over FastText
TL;DR: In this article , the authors used various embeddings such as FastText, Bert Expert-MEDLINE/PubMed + CNN and Bert Expert Talking Head along with the presence of CNN and multi-layer perceptron (MLP) for detection of the fake new in the dataset.
Journal ArticleDOI
Analysis of the Impact of Age, Education and Gender on Individuals' Perception of Label Efficacy for Online Content
Matthew Spradling,Jeremy Straub +1 more
TL;DR: In this article , the use of labeling as a way to alert consumers of potential deceptive content has been proposed and factors which impact its perceived trustworthiness and potential use by Americans were analyzed based on age, education level and gender.
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.