A Comparison of the Performance of Six Machine Learning Algorithms for Fake News - Al-Furaiji, Rafah H; Abdulkader, Hasan (EAI Endorsed Transactions on AI and Robotics, 2024)
INTRODUCTION: This research focuses on the increasing importance of social media websites as versatile platforms for entertainment, work, communication, commerce, and accessing global news. However, it emphasizes the need to use this power responsibly.OBJECTIVES: The objective of the study is to evaluate the performance of artificial intelligence algorithms in detecting fake news.METHODS: Through a comparison of six machine learning algorithms and the use of natural language processing techniques,RESULTS: The study identifies four algorithms with a 99% accuracy rate in detecting fake news.CONCLUSION: The results demonstrate the effectiveness of the proposed method in enhancing the performance of artificial intelligence algorithms in addressing the problem of fake news detection.
Keywords: Machine learning; Natural Language Processing; Fake news; python; Scikit-Learn