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Melhorar a previsão da autenticidade das notÃcias através de uma abordagem de aprendizagem automática
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ISBN10: 6630225260
ISBN13: 9786630225266
Publisher: Edicoes Nosso Conhecimento
Published: Jul 1 2026
Pages: 56
Weight: 0.19
Height: 0.13 Width: 6.00 Depth: 9.00
Language: Portuguese
ISBN13: 9786630225266
Publisher: Edicoes Nosso Conhecimento
Published: Jul 1 2026
Pages: 56
Weight: 0.19
Height: 0.13 Width: 6.00 Depth: 9.00
Language: Portuguese
A rápida proliferação de desinformação em plataformas digitais representa uma ameaça crÃtica ao discurso público, aos processos democráticos e à confiança da sociedade. Os sistemas de verificação manual não conseguem acompanhar o volume e a velocidade de geração de notÃcias falsas, tornando necessário o desenvolvimento de mecanismos automatizados de detecção. Este trabalho apresenta uma abordagem de aprendizado de máquina leve para a detecção de notÃcias falsas, utilizando um classificador *Passive-Aggressive* combinado com a vetorização TF-IDF; o modelo foi treinado e avaliado com o conjunto de dados de referência WELFake, composto por 72.119 artigos de notÃcias provenientes de diversas plataformas e abrangendo o perÃodo de 2016 a 2020. O sistema proposto alcança uma acurácia de classificação de 96,19% em 14.424 artigos de teste, com Ãndices de precisão e *recall* entre 0,96 e 0,97 para ambas as classes e um *F1-score* balanceado de 0,96, validado por meio de um *wrapper* CalibratedClassifierCV que fornece saÃdas de probabilidade calibradas. Além da classificação, o sistema incorpora explicabilidade baseada em LIME para justificar previsões em nÃvel de palavra, verificação de URLs em tempo real com análise de credibilidade da fonte, detecção de *clickbait* e geração de vereditos fundamentados em evidências utilizando a API Gemini.
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