Estudo comparativo de modelos de classificação textual aplicados na classificação de Fake News - Gusmão, Felipe dos Santos (2023)
The present work aims to analyze the performance of three text classification models for identifying fake news. A news classification system was developed using variations of the BERT model. The models used were: BERT, DistilBERT and BERTimbau. The defined scenario was to analyze 7200 samples of news in Portuguese that are pre-classified in the Fake.br corpus into 2 classes, true news and fake news, with 3600 samples for each class. The performance of the 3 models for classifying this corpus was compared using metrics of precision, accuracy, and F1 of each of the models. As expected, as it is a pre-trained model in portuguese, the BERTimbau model presented the best results within the evaluated metrics, getting 98% precision on the second experiment.