Fake News Detection Using Machine Learning Algorithms: A Review

Aminu Shehu Sharif, Vipin Borole, Vaibhav P. Sonaje, Fauziyya Umar Adamu

Abstract


Abstract— The spread of false news in the digital age poses challenges to society. This paper studied machine learning techniques for detecting fake news, focusing on the best-performing algorithms and also commonly used datasets. Evaluation metric such as F1 score, accuracy, precision, recall, and performance are reviewed, along with the examination of datasets. Through the analysis and comparison of existing studies, algorithms demonstrating superior performance across evaluation metrics can be identified. Furthermore, widely employed datasets that yield reliable results are highlighted. This review enables researchers to make informed decisions in selecting accurate algorithms and effective datasets, advancing the field of fake news detection.

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