fakenewsbr: A Fake News Detection Platform for Brazilian Portuguese

TL;DR

This paper introduces fakenewsbr, a machine learning-based platform for detecting fake news in Brazilian Portuguese, utilizing NLP techniques and various classifiers, with high accuracy demonstrated through empirical evaluation.

Contribution

It presents a novel fake news detection platform tailored for Brazilian Portuguese, combining NLP feature extraction and multiple classifiers, and offering real-time verification via a user-friendly web interface.

Findings

High accuracy and F1-Score in fake news detection

Effective use of TF-IDF and Word2Vec features

Platform enables real-time news verification

Abstract

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.