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Fake News Detection: A Machine Learning Approach

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  • Moses Daniel Kwaknat

    (Department of Computer Science University of Jos, Nigeria)

  • Nentawe Gurumdimma

    (Department of Computer Science University of Jos, Nigeria)

Abstract

Fake news has become one of the most discussed topics around the world today because of the influence it has on the decision making of individuals and even top government bodies around the world. It has become a real menace to society especially as it is easily disseminated on the internet through the use of social media. Many algorithms and models have been developed over the years to help checkmate the spread of fake news on the internet. However, there has been no real implementation of these algorithms online for the public to use. This research built a fake news detection model using three different binary classifiers: naive Bayes, logistic regression, and random forest. The logistic regression classifier was found to be the most accurate with an accuracy of 96%. The dataset was sourced from Kaggle and comprises labeled real and fake news articles. Evaluation was extended to include precision, recall, and F1-score for better performance insight. The model was deployed as a web application.

Suggested Citation

  • Moses Daniel Kwaknat & Nentawe Gurumdimma, 2025. "Fake News Detection: A Machine Learning Approach," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(5), pages 454-468, May.
    • Handle: RePEc:bjf:journl:v:10:y:2025:i:5:p:454-468
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