Fake News Detection for Portuguese with Deep Learning

Lígia Venturott, Ruslan Mitkov

Abstract
The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading information. Since the 2016 US presidential election, the term “fake news” became increasingly popular and this phenomenon has received more attention. In the past years several fact-checking agencies were created, but due to the great number of daily posts on social media, manual checking is insufficient. Currently, there is a pressing need for automatic fake news detection tools, either to assist manual fact-checkers or to operate as standalone tools. There are several projects underway on this topic, but most of them focus on English. This research-in-progress paper discusses the employment of deep learning methods, and the development of a tool, for detecting false news in Portuguese. As a first step we shall compare well-established architectures that were tested in other languages and analyse their performance on our Portuguese data. Based on the preliminary results of these classifiers, we shall choose a deep learning model or combine several deep learning models which hold promise to enhance the performance of our fake news detection system.
Anthology ID:
2021.triton-1.16
Volume:
Proceedings of the Translation and Interpreting Technology Online Conference
Month:
July
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Vilelmini Sosoni, Julie Christine Giguère, Elena Murgolo, Elizabeth Deysel
Venue:
TRITON
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
149–153
Language:
URL:
https://aclanthology.org/2021.triton-1.16
DOI:
Bibkey:
Cite (ACL):
Lígia Venturott and Ruslan Mitkov. 2021. Fake News Detection for Portuguese with Deep Learning. In Proceedings of the Translation and Interpreting Technology Online Conference, pages 149–153, Held Online. INCOMA Ltd..
Cite (Informal):
Fake News Detection for Portuguese with Deep Learning (Venturott & Mitkov, TRITON 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2021.triton-1.16.pdf
PDF Search