AVICO CNF: um suporte ferramental para a classificação de notícias falsas sobre vacinas da COVID

Abstract

The immeasurable growth of fake news on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to people’s physical and mental stability. To detect this false information, researchers have applied various techniques such as Web Mining, Machine Learning and Deep Learning. The aim of this study was to systematically review, evaluate and synthesize research articles that used different techniques to detect fake news about COVID-19. A literature search was performed on the relevant bibliographic databases to ensure that the search focused only on high integrity research. In particular, we identify several fake news datasets and review different data processing, feature extraction and classification techniques to detect fake news. At the end, we discuss the challenges and limitations found in our review. Keywords: machine learning, deep learning, natural language processing, fake news, literature survey, systematic literature review, systematic mapping study, systematic mapping review.