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Title: A machine learning approach to analyse fake news
Authors: Alves, Jairo
Weitzel, Leila
Quaresma, Paulo
Cardoso, Carlos
Cunha, Luan
Editors: Nystrom, Ingela
Heredia, Yanio
Nunez, Vladimir
Keywords: Fake News
Machine Learning
Issue Date: Oct-2019
Publisher: Spinger
Abstract: As Brazil faced one of its most important elections in recent times, the fact-checking agencies handled the same kind of misinformation that has attacked voting in the US. However, stopping fake content before it goes viral remains an intense challenge. This paper examines a sample database of the 2018 Brazilian election articles shared by Brazilians over social media platforms. We evaluated three different configuration of Long Short-Term Memory. Experiment results indicate that the 3-layer Deep BiLSTMs with trainable word embeddings configuration was the best structure for fake news detection. We noticed that the developments in deep learning could potentially benefit fake news research.
Type: article
Appears in Collections:INF - Artigos em Livros de Actas/Proceedings

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