fakenewsbr: A Fake News Detection Platform for Brazilian Portuguese
This addresses the problem of misinformation in Brazilian Portuguese news for the public, but it is incremental as it applies existing NLP methods to a new language domain.
The paper tackles fake news detection in Brazilian Portuguese by proposing a machine learning approach using TF-IDF and Word2Vec features with various classifiers, achieving high accuracy and F1-Score, and develops a web platform for real-time verification.
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.