Research on BBHL Model Based on Hybrid Loss Optimization for Fake News Detection

With the rapid development of social media, the spread of fake news has become a significant issue affecting social stability. To address the problems of incomplete feature extraction and simplistic loss function design in traditional fake news detection, this paper proposes a BBHL model based on hybrid loss optimization. The model achieves deep extraction of text features by integrating BERT, Bi-LSTM, and attention mechanisms, and innovatively fuses binary cross-entropy (BCE) loss with contrastive loss to enhance feature discriminability and the model’s generalization ability. Experiments on the Weibo, Twitter, and Pheme datasets demonstrate that the BBHL model significantly outperforms baseline models such as EANN and MCNN in metrics including accuracy and F1-score. Ablation experiments verify the effectiveness of contrastive loss, providing a robust and generalizable solution for fake news detection.