Machine Learning-based Classification of Portuguese News Articles on Public Procurement Fraud - Assis, Paulo Marcos de; Castro, Márcio; Carvalho, Jônata Tyska (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16668, 2025)
Combating fraud in public procurement is a critical task for oversight agencies, and the news media offer a rich source to uncover irregularities. Yet, the sheer volume of daily news hampers the identification of relevant reports. We propose a text classification pipeline to flag news articles reporting procurement fraud. Contributions include a labeled dataset of 9,412 news articles, the optimization and evaluation of multiple machine learning methods, and the identification of an effective feature extractor-classifier combination. Using BERT embeddings and Support Vector Machines, we achieved an F1-Score of 100% on a test set of 8553 news articles with only 0.15% positive labels. Results offer a promising news-based methodology for evidence-based fraud detection.