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As AI-assisted text generators become available, the volume of false information created and disseminated has increased, making the identification of such information more important than ever. The sheer volume of automatically generated content poses a major challenge for fake news detection systems, which must sift through large amounts of information.This research has the objective to contribute to the enhancement of the fake news detection algorithms by fine-tuning their performance for a large amount of data that is handled by such tools. We have reconsidered applications like Naïve Bayes, and Random Forest, both of which have demonstrated their reliability in text classification. However, while the impact of AI-based text generators on the spread of false information is a growing field of study, there remains a lack of detailed research in this area. We aim to bridge this gap by focusing on this critical topic.

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Optimization of Fake News Detection Models: Random Forests and Naïve Bayes in Response to the Challenges of Automated Generators

  • 会议论文

  • 发表日期：

    2026年

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  • Loubna Salaheddinem,
  • Younes Chihab,
  • Rabiaa cheikh Maoulainine,
  • Soufian Hajbi

摘要

As AI-assisted text generators become available, the volume of false information created and disseminated has increased, making the identification of such information more important than ever. The sheer volume of automatically generated content poses a major challenge for fake news detection systems, which must sift through large amounts of information.This research has the objective to contribute to the enhancement of the fake news detection algorithms by fine-tuning their performance for a large amount of data that is handled by such tools. We have reconsidered applications like Naïve Bayes, and Random Forest, both of which have demonstrated their reliability in text classification. However, while the impact of AI-based text generators on the spread of false information is a growing field of study, there remains a lack of detailed research in this area. We aim to bridge this gap by focusing on this critical topic.