  The proliferation of fake news has become a significant concern in recent
times due to its potential to spread misinformation and manipulate public
opinion. This paper presents a comprehensive study on detecting fake news in
Brazilian Portuguese, focusing on journalistic-type news. We propose a machine
learning-based approach that leverages natural language processing techniques,
including TF-IDF and Word2Vec, to extract features from textual data. We
evaluate the performance of various classification algorithms, such as logistic
regression, support vector machine, random forest, AdaBoost, and LightGBM, on a
dataset containing both true and fake news articles. The proposed approach
achieves high accuracy and F1-Score, demonstrating its effectiveness in
identifying fake news. Additionally, we developed a user-friendly web platform,
fakenewsbr.com, to facilitate the verification of news articles' veracity. Our
platform provides real-time analysis, allowing users to assess the likelihood
of fake news articles. Through empirical analysis and comparative studies, we
demonstrate the potential of our approach to contribute to the fight against
the spread of fake news and promote more informed media consumption.
