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Related Experiment Video

Updated: Jun 10, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Enhancing fake news detection with transformer-based deep learning: A multidisciplinary approach.

Nabeel Raza1, Said Jadid Abdulkadir2,3,4, Yawar Abbas Abid5,6

  • 1Science Island Branch Graduate School University of Science and Technology Hefei, Anhui, China.

Plos One
|September 9, 2025
PubMed
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This study introduces an enhanced Bidirectional Encoder Representations from Transformers (BERT) model for detecting fake news. The advanced framework achieves high accuracy, offering a robust solution against digital misinformation.

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Science

Background:

  • Fake news dissemination challenges digital information integrity and public trust.
  • Automated detection mechanisms are crucial for combating misinformation.
  • Existing methods may not fully capture the nuances of fabricated content.

Purpose of the Study:

  • To propose a robust fake news detection framework using a transformer-based architecture.
  • To enhance the Bidirectional Encoder Representations from Transformers (BERT) model with progressive training.
  • To improve the automated identification of linguistic indicators differentiating real news from fake news.

Main Methods:

  • Developed a fake news detection framework utilizing a transformer-based architecture.

Related Experiment Videos

Last Updated: Jun 10, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

  • Applied and enhanced the Bidirectional Encoder Representations from Transformers (BERT) model.
  • Implemented a progressive training methodology for incremental learning.
  • Evaluated the framework on the large-scale WELFake dataset (72,134 articles).

    • Main Results:

    • The enhanced BERT framework achieved high performance metrics: 95.3% accuracy, 0.953 F1-score, 0.952 precision, and 0.954 recall.
    • The model demonstrated superior performance compared to traditional machine learning classifiers.
    • The approach significantly outperformed other standard transformer-based implementations.
    • The progressive training effectively captured complex contextual dependencies in text.

    Conclusions:

    • The enhanced BERT framework presents a powerful and scalable solution for fake news detection.
    • The progressive training methodology enhances the model's ability to discern subtle linguistic differences.
    • This approach offers a significant advancement in the automated fight against digital misinformation.
    • The findings highlight the potential of advanced transformer models in maintaining information integrity.