False News Recognition Model Based on Attention Mechanism and Multiple Features

As the prevalence of social media continues to grow, the rapid and wide dissemination of false news has become a critical societal challenge, undermining public trust, creating social unrest, and distorting political discourse. Traditional fake news detection methods often rely solely on linguistic cues or shallow semantic analysis, which leads to limited accuracy and poor robustness, particularly when addressing emotionally biased or contextually complex content. To overcome these limitations, this study proposes a novel fake news recognition model based on a bidirectional gated recurrent unit combined with a self-attention mechanism, further enhanced by integrating sentiment polarity, textual metadata, and contextual semantic features. Experimental results show that the proposed model achieves a recognition accuracy of ninety-seven per cent and an F1 score of ninety-seven. In addition, it demonstrates the lowest mean absolute error, which is zero point one nine, and the shortest recognition time, requiring only zero point eight seconds after eighty iterations. The model also maintains over ninety-three per cent accuracy across news content with active, negative, and neutral emotional tones. The model offers a scalable and reliable framework for detecting false news, with strong adaptability to diverse content types and emotional expressions, thereby contributing to the advancement of automated misinformation identification in real-world applications.

@article{Suo2025,
  title     = {False News Recognition Model Based on Attention Mechanism and Multiple Features},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Qiongyao Suo and Hongzhen Chang},
  doi       = {10.14569/IJACSA.2025.0160624},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160624}
}