Investigating the Performance of the GPT-3.5 Model in Fake News Detection: An Experimental Analysis

  • Lucas S. Anjos UFU
  • Silvio E. Quincozes UFU / UNIPAMPA
  • Juliano F. Kazienko UFSM
  • Vagner E. Quincozes UFF


The dissemination of fake news has become a significant concern in the current society. This problem is evident on social media platforms, where the spread of misinformation has become a constant presence in the daily lives of many individuals. In this work, we investigate the performance of the GPT-3.5 model in classifying fake and real news, considering 200 newspaper articles and two strategies for question formulation. Our results reveal that using a well-formulated question is crucial to obtain more precise responses. In particular, we observed an improvement of 21.1% in the F1-Score metric by directing the question to focus on the characteristics of a fake text.


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ANJOS, Lucas S.; QUINCOZES, Silvio E.; KAZIENKO, Juliano F.; QUINCOZES, Vagner E.. Investigating the Performance of the GPT-3.5 Model in Fake News Detection: An Experimental Analysis. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 23. , 2023, Juiz de Fora/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 552-557. DOI:

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