Research Article
Comparison of the Performance of Six Machine Learning Algorithms for Fake News
@ARTICLE{10.4108/airo.4153, author={Rafah H Al-Furaiji and Hasan Abdulkader}, title={Comparison of the Performance of Six Machine Learning Algorithms for Fake News}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={3}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2024}, month={12}, keywords={Machine learning, Natural Language Processing, Fake news, python, Scikit-Learn}, doi={10.4108/airo.4153} }- Rafah H Al-Furaiji
Hasan Abdulkader
Year: 2024
Comparison of the Performance of Six Machine Learning Algorithms for Fake News
AIRO
EAI
DOI: 10.4108/airo.4153
Abstract
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.
Copyright © 2024 R. H. Al-Furaiji et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
