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 language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 437-442 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728111988 |
| DOIs | |
| State | Published - Apr 22 2019 |
| Event | 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 - San Jose, United States Duration: Mar 28 2019 → Mar 30 2019 |
Publication series
| Name | Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 |
|---|
Conference
| Conference | 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 |
|---|---|
| Country | United States |
| City | San Jose |
| Period | 3/28/19 → 3/30/19 |
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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
High Dimensional Latent Space Variational AutoEncoders for Fake News Detection. / Sadiq, Saad; Wagner, Nicolas; Shyu, Mei-Ling; Feaster, Daniel J.
Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 437-442 8695321 (Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - High Dimensional Latent Space Variational AutoEncoders for Fake News Detection
AU - Sadiq, Saad
AU - Wagner, Nicolas
AU - Shyu, Mei-Ling
AU - Feaster, Daniel J
PY - 2019/4/22
Y1 - 2019/4/22
N2 - 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.
AB - 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.
KW - Fake news
KW - latent representation
KW - LSTM (Long Short Term Memory)
KW - VAE (Variational AutoEncoder)
UR - http://www.scopus.com/inward/record.url?scp=85065603771&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065603771&partnerID=8YFLogxK
U2 - 10.1109/MIPR.2019.00088
DO - 10.1109/MIPR.2019.00088
M3 - Conference contribution
T3 - Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019
SP - 437
EP - 442
BT - Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -