The Fake News Detection in Health Domain Using Transformer Models

Abstract

The rise of fake news in the health sector poses a serious threat to public well-being and accurate health communication. This study investigates the effectiveness of transformer models, particularly BERT (Bidirectional Encoder Representations from Transformers), in detecting fake news related to health. By leveraging the advanced contextual understanding of BERT, we aim to enhance the accuracy of fake news detection in this critical domain. Our approach involves training the BERT model on a curated dataset of health news articles, followed by rigorous evaluation on its ability to differentiate between genuine and misleading content. The results reveal that the transformer-based model significantly outperforms traditional methods, achieving high accuracy and robust performance metrics. This research underscores the potential of transformer models in combating health misinformation and provides a foundation for future improvements in automated fake news detection systems.