Comparative Evaluation of Traditional Machine Learning and Transformer Models for Fake News Detection
This study systematically benchmarks four traditional machine learning models against four transformer-based models across five public datasets, demonstrating that while transformer architectures like RoBERTa significantly outperform traditional methods in fake news detection accuracy, they incur substantially higher computational costs, thereby offering critical trade-off insights for model selection under varying resource constraints.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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