"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection

Code

• github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR
  pytorch★ 152
• github.com/mansoor9743/Fake-News-Detection
  tf★ 0
• github.com/mikanikos/ADA_Project
  none★ 0
• github.com/JelenaBanjac/lies-have-short-legs
  none★ 0
• github.com/ExploringLies/lies-have-short-legs
  none★ 0
• github.com/bedarkarpriyanka/NLP-Project-Fake-News-Detection
  tf★ 0
• github.com/siddarthhari95/NLP-Fake-News_detection
  tf★ 0
• github.com/ekagra-ranjan/fake-news-detection-LIAR-pytorch
  pytorch★ 0
• github.com/kiranrawat/Detecting-Fake-News-On-Social-Media
  none★ 0
• github.com/SindhuMadi/FakeNewsDetection
  none★ 0

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.

Tasks

Benchmark Results

Reproductions