FakeNewsDetectioninHealthDomainUsing Transformer Models

FakeNewsDetectioninHealthDomainUsing Transformer Models

FakeNewsDetection,TransformerModels,HealthInformation

The rise of fake news in the health sector poses a serious threat to public well-being and accurate health communication.Thisstudyinvestigatestheeffectivenessof transformermodels, particularlyBERT(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, followedbyrigorousevaluationofitsabilitytodifferentiatebetweengenuineandmisleadingcontent.Theresults 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

Suwarto1,SriHastaMulyani2,Hamzah3, R.NurhadiWijayaS4,Rodiyah5,WitaAdelia

https://ijicom.respati.ac.id/index.php/ijicom/article/view/89/59

December 2024

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