High Dimensional Latent Space Variational AutoEncoders for Fake News Detection

Saad Sadiq, Nicolas Wagner, Mei-Ling Shyu, Daniel J Feaster

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

With the advent of social media and cell phones, news is now far more reaching and impactful than ever before. This comes with the exponential increase in fake news that blurs the lines of reality and holds the power to sway public opinion. To counter the impact of fake news, several research groups have developed novel algorithms that could fact check news as a human would do. Unfortunately, natural language processing (NLP) is a complicated task because of the underlying hidden meanings in human communication. In this paper, we propose a novel method that builds a latent representation of natural language to capture its underlying hidden meanings accurately and classify fake news. Our approach connects the high-level semantic concepts in the news content with their low-level deep representations so that the complex news text consisting of satire, sarcasm, and purposeful misleading content can be translated into quantifiable latent spaces. This allows us to achieve very high accuracy, surpassing the scores of all winners of the fake news challenge.

Original languageEnglish (US)
Title of host publicationProceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages437-442
Number of pages6
ISBN (Electronic)9781728111988
DOIs
StatePublished - Apr 22 2019
Event2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 - San Jose, United States
Duration: Mar 28 2019Mar 30 2019

Publication series

NameProceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019

Conference

Conference2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019
CountryUnited States
CitySan Jose
Period3/28/193/30/19

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Keywords

  • Fake news
  • latent representation
  • LSTM (Long Short Term Memory)
  • VAE (Variational AutoEncoder)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Media Technology

Cite this

Sadiq, S., Wagner, N., Shyu, M-L., & Feaster, D. J. (2019). High Dimensional Latent Space Variational AutoEncoders for Fake News Detection. In Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 (pp. 437-442). [8695321] (Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MIPR.2019.00088