"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
William Yang Wang
- Vol. 2, pp 422-426
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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
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Journal ArticleDOI
A comprehensive Benchmark for fake news detection
TL;DR: In this paper , the authors provided a benchmark framework in order to analyze and discuss the most widely used and promising machine/deep learning techniques for fake news detection, also exploiting different features combinations w.r.t.
Proceedings ArticleDOI
A machine-learning based framework for detection of fake political speech
TL;DR: An automated framework for the detection of fake political speech that uses different classification methods for extracting features from political speech statement and its metadata including speech subject, location, speaker's profile, speaker’s credibility, and speech context information is proposed.
Posted Content
Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification
Bibek Upadhayay,Vahid Behzadan +1 more
TL;DR: This paper introduces Sentimental LIAR, which extends the LIAR dataset of short claims by adding features based on sentiment and emotion analysis of claims, and proposes a novel deep learning architecture based on the BERT-Base language model for classification of claims as genuine or fake.
Proceedings ArticleDOI
Role of Contextual Features in Fake News Detection: A Review
TL;DR: The influence of linguistic characteristics and contextual features in fake news detection are analyzed and certain techniques like Naive Bayes, KNN, SVM, Decision tree, Hybrid CNN, CMS etc. are compared.
Dissertation
Machine learning for detection of fake news
TL;DR: The goal of this project was to create a tool for detecting the language patterns that characterize fake and real news through the use of machine learning and natural language processing techniques and the results demonstrate the ability for machine learning to be useful in this task.
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