Fake News Classification and Topic Modeling in Brazilian Portuguese

All over the world, people receive daily news on many subjects through web-based information sharing platforms such as social networks. However, some of such news are false (fake) with the potential to deceive them. Thus, the automatic detection of false news is a major issue and is gaining careful attention from the scientific community. In this paper, we present experimental analysis using both supervised and unsupervised learning on the Fake.Br corpus, a fake news dataset in Brazilian Portuguese. We propose a classification method for fake news detection based on distinct types of features, and deep learning supervised algorithms. Our best classification model achieved F1 scores up to 96% and was compared with other non-deep learning classifiers. Furthermore, we provide a complementary analysis of the same dataset by performing topic modeling based on both uni-grams and bi-grams.