FN-Net: A Deep Convolutional Neural Network for Fake News Detection

Kian Long Tan, Chin Poo Lee, Kian Ming Lim

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Information and communication technology has evolved rapidly over the past decades, with a substantial development being the emergence of social media. It is the new norm that people share their information instantly and massively through social media platforms. The downside of this is that fake news also spread more rapidly and diffuse deeper than before. This has caused a devastating impact on people who are misled by fake news. In the interest of mitigating this problem, fake news detection is crucial to help people differentiate the authenticity of the news. In this research, an enhanced convolutional neural network (CNN) model, referred to as Fake News Net (FN-Net) is devised for fake news detection. The FN-Net consists of more pairs of convolution and max pooling layers to better encode the high-level features at different granularities. Besides that, two regularization techniques are incorporated into the FN-Net to address the overfitting problem. The gradient descent process of FN-Net is also accelerated by the Adam optimizer. The empirical studies on four datasets demonstrate that FN-Net outshines the original CNN model.

Original languageEnglish
Title of host publication2021 9th International Conference on Information and Communication Technology, ICoICT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages331-336
Number of pages6
ISBN (Electronic)9781665404471
DOIs
Publication statusPublished - 3 Aug 2021
Externally publishedYes
Event9th International Conference on Information and Communication Technology, ICoICT 2021 - Virtual, Yogyakarta, Indonesia
Duration: 3 Aug 20215 Aug 2021

Publication series

Name2021 9th International Conference on Information and Communication Technology, ICoICT 2021

Conference

Conference9th International Conference on Information and Communication Technology, ICoICT 2021
Country/TerritoryIndonesia
CityVirtual, Yogyakarta
Period3/08/215/08/21

Keywords

  • CNN
  • Fake news
  • machine learning
  • natural language processing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems

Fingerprint

Dive into the research topics of 'FN-Net: A Deep Convolutional Neural Network for Fake News Detection'. Together they form a unique fingerprint.

Cite this