"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
Deception Detection Within and Across Domains: Identifying and Understanding the Performance Gap
TL;DR: In this article , the authors conduct empirical studies of cross-domain deception detection in five domains to understand how current models perform when evaluated on new deception domains, and propose methods to understand the differences in performances across domains.
Posted Content
Hidden Biases in Unreliable News Detection Datasets
TL;DR: In this paper, the authors show that selection bias during data collection leads to undesired artifacts in the datasets and propose to use a simple model as a difficulty/bias probe and future model development use a clean nonoverlapping site and date split.
Book ChapterDOI
A Hierarchical Learning Model for Claim Validation
TL;DR: This work augments the LIAR dataset’s claim statements and the speakers’ profile features with the evidence retrieved from the Politifact.com dataset to design a transfer learning-based claim verification model, TLCV: Transfer Learning based Claim Validation.
Book ChapterDOI
FakeTouch: Machine Learning Based Framework for Detecting Fake News
Abu Bakkar Siddikk,Rifat Jahan Lia,Md. Fahim Muntasir,Sheikh Shah Mohammad Motiur Rahman,Md. Shohel Arman,Mahmuda Rawnak Jahan +5 more
TL;DR: In this article , a machine learning approach has been proposed named FakeTouch starting with Natural Language Processing based concept by applying text processing, cleaning and extraction techniques, this approach aim to arrange the information to be “obeyed” into each classification model for training and tuning parameters for every model to bring out the optimized and best prediction to find out the Fake news.
Journal ArticleDOI
A Framework for Predicting and Analyzing Fake News Using Machine Learning
TL;DR: It is vital in today's world to have some instruments that can verify any news, whether it is factual or not, and I'd like to accomplish the same thing with this algorithm.
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