On Creation of Synthetic Samples from GANs for Fake News Identification Algorithms

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Vaz, B (Autor) Outra; Bernardes, V (Autor) Outra; Figueira, A (Autor) FCUP

Ata de Conferência Internacional

Título: INFORMATION SYSTEMS AND TECHNOLOGIES, WORLDCIST 2022, VOL 3

Páginas: 316-326

World Conference on Information Systems and Technologies (WorldCIST)

ELECTR NETWORK, APR 12-14, 2022

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00W-GTM

DOI: 10.1007/978-3-031-04829-6_28

Abstract (EN): The use of Generative Adversarial Networks is almost traditional in creating synthetic images for medical purposes. They are probably the best use of GANs until now, as their results can easily be checked by the eye of specialists. In fake news detection models, we have seen lately that neural models (and deep learning) can provide a considerable improvement from standard classifiers. Yet, the most problematic problem still is the lack of data, mostly fake news data to feed these models. In this paper, we address that by proposing the use of a GAN. Results show a better capacity to generalize when used for training an extended dataset based on synthetic samples created by this GAN.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11