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

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Vaz, B (Author) Other; Bernardes, V (Author) Other; Figueira, A (Author) FCUP

Conference proceedings International

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

Pages: 316-326

World Conference on Information Systems and Technologies (WorldCIST)

ELECTR NETWORK, APR 12-14, 2022

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 1 Citation

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11