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Fake news has been a noticeable issue in the last few decades after the presence of the Internet. Many news channels, networks, and social media platforms provide us with news from around the globe. These news resources can also be used to share malicious and fake news. Therefore, detecting and handling this fake news is crucial since the world’s view is based on this information. Verifying news individually by a human being is completely unfeasible. Thus, we proposed an artificially intelligent Naïve Bayes prediction model that can help classify news and detect if a given news is fake or real. The proposed model used the Naïve Bayes classifier, which gives great results in text classifications such as spam filtering. This paper also includes an analysis of results and performance measurement.

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Unmasking Fake News: A Naïve Bayes Classifier Approach to Combat Misinformation

  • 会议论文

  • 发表日期：

    2025年

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  • Aqeel Sahi,
  • Mustafa Hayawi,
  • Raid Lafta,
  • Shahab Abdulla,
  • Mohammed Diykh

摘要

Fake news has been a noticeable issue in the last few decades after the presence of the Internet. Many news channels, networks, and social media platforms provide us with news from around the globe. These news resources can also be used to share malicious and fake news. Therefore, detecting and handling this fake news is crucial since the world’s view is based on this information. Verifying news individually by a human being is completely unfeasible. Thus, we proposed an artificially intelligent Naïve Bayes prediction model that can help classify news and detect if a given news is fake or real. The proposed model used the Naïve Bayes classifier, which gives great results in text classifications such as spam filtering. This paper also includes an analysis of results and performance measurement.