Fake News Detection Using Traditional Machine Learning Approaches

Abstract: The rapid growth of digital media has made the detection of fake news an essential task, as misinformation can quickly spread online and influence public opinion, decision-making, and social trust. This study explores the effectiveness of traditional machine learning techniques in classifying news articles as fake or real. Using the WELFake dataset, which contains 72,134 news articles from the Kaggle platform, classifiers such as Support Vector Machine (SVM), Random Forest, Decision Tree, and Gradient Boosting were evaluated. Initial experiments achieved strong results, with several models reaching an F1-score of 0.90. Further improvements were obtained by engineering additional features, leading to an enhanced F1-score of 0.96. The findings highlight the capability of traditional machine learning approaches for fake news detection and provide insights into building effective models to mitigate the spread of misinformation.

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