Fake News Detection in Internet Using Deep Learning: A Review

  • Israel Barrutia-Barreto
  • , Renzo Seminario-Córdova
  • , Brian Chero-Arana

Producción científica: Capítulo del libro/informe/acta de congresoCapítulorevisión exhaustiva

3 Citas (Scopus)

Resumen

The main objective of this research was to explore, from a reflexivity approach, the current state of Deep learning techniques for automatic detection of fake news on the Internet, analyzing the most important Deep learning algorithms and studies on their effectiveness in detecting distrustful information. The research methodology employed was bibliographic, documentary and descriptive. The information was collected from several scientific articles provided by indexed journals and web platforms, using keywords such as “fake news”, “Deep learning” and “neural networks” for the compilation. As a result of this research, it was concluded that Deep learning techniques present a better performance than conventional methods and will be of great importance in the future of war against fake news due to their potential in automatic detection.

Idioma originalInglés
Título de la publicación alojadaStudies in Computational Intelligence
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas55-67
Número de páginas13
DOI
EstadoPublicada - 1 ene. 2022
Publicado de forma externa

Serie de la publicación

NombreStudies in Computational Intelligence
Volumen1001
ISSN (versión impresa)1860-949X
ISSN (versión digital)1860-9503

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