Research on BBHL Model Based on Hybrid Loss Optimization for Fake News Detection
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Ideological and Political Education
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Intelligent Science and Engineering, Qinghai Minzu University, Xining 810007, China
School of Cyber Science and Engineering, Qinghai Minzu University, Xining 810007, China
Joint Laboratory for Cyberspace Security, Qinghai Minzu University, Xining 810007, China
College of Computer Science and Technology, Qinghai Normal University, Xining 810016, China
Funding
Key R&D and Transformation Plan of Qinghai Province
References (36)
Show source-provided references
1. Early Detection of Multimodal Fake News via Reinforced Propagation Path Generation10.1109/TKDE.2024.3496701 ↗
2. Social media and fake news in the 2016 election10.1257/jep.31.2.211 ↗
3. Linguistic features based framework for automatic fake news detection10.1016/j.cie.2022.108432 ↗
4. Ge, W., Hong, Z., and Luo, Y. (2020, January 14–16). Online detection of Weibo rumors based on Naive Bayes algorithm. Proceedings of the 2020 Asia-Pacific Conference on Image Processing, Electronics and Computers, Dalian, China.
5. Liu, X., Pang, M., Li, Q., Zhou, J., Wang, H., and Yang, D. (2024). MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor Detection. Algorithms, 17.10.3390/a17050199 ↗
6. Rumor detection based on convolutional neural network
7. Weibo rumor event detection method based on C-GRU
8. GAMC: An Unsupervised Method for Fake News Detection Using Graph Autoencoder with Masking
9. Khattar, D., Goud, J.S., Gupta, M., and Varma, V. (2019, January 13–17). MVAE: Multimodal Variational Autoencoder for Fake News Detection. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.10.1145/3308558.3313552 ↗
10. Han, H., Ke, Z., Nie, X., Dai, L., and Slamu, W. (2023). Multimodal Fusion with Dual-Attention Based on Textual Double-Embedding Networks for Rumor Detection. Appl. Sci., 13.10.3390/app13084886 ↗
11. Multi-Modal Fake News Detection Enhanced by Fine-Grained Knowledge Graph10.1587/transinf.2024EDP7234 ↗
12. Multimodal fake news detection through data augmentation-based contrastive learning10.1016/j.asoc.2023.110125 ↗
13. Positive Unlabeled Fake News Detection via Multi-Modal Masked Transformer Network10.1109/TMM.2023.3263552 ↗
14. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv.
15. Deep learning for fake news detection: A survey10.1016/j.aiopen.2022.09.001 ↗
16. Detecting fake news by exploring the consistency of multimodal data10.1016/j.ipm.2021.102610 ↗
17. Zhou, X., Wu, J., and Zafarani, R. (2020). SAFE: Similarity-Aware MultiModal Fake News Detection. arXiv.10.1007/978-3-030-47436-2_27 ↗
18. Exploring deep neural networks for rumor detection10.1007/s12652-019-01527-4 ↗
19. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
20. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
21. External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection
22. Bootstrapping Multi-View Representations for Fake News Detection
23. Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-Modal Fake News Detection
24. HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection
25. Propagation2Vec: Embedding partial propagation networks for explainable fake news early detection10.1016/j.ipm.2021.102618 ↗
26. Su, P., Peng, Y., and Vijay-Shanker, K. (2021). Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction. arXiv.10.18653/v1/2021.bionlp-1.1 ↗
27. MIGCL: Fake news detection with multimodal interaction and graph contrastive learning networks10.1007/s10489-024-05883-3 ↗
28. MDF-FND: A dynamic fusion model for multimodal fake news detection10.1016/j.knosys.2025.113417 ↗
29. Supervised Contrastive Learning
30. Song, C., Tu, C., Yang, C., Chen, H., Liu, Z., and Sun, M. (2018). CED: Credible Early Detection of Social Media Rumors. arXiv.
31. Yuan, C., Ma, Q., Zhou, W., Han, J., and Hu, S. (2019, January 8–11). Jointly embedding the local and global relations of heterogeneous graph for rumor detection. Proceedings of the 19th IEEE International Conference on Data Mining, Beijing, China.10.1109/ICDM.2019.00090 ↗
32. Zubiaga, A., Liakata, M., Procter, R., Hoi, G.W.S., Tolmie, P., and Masuda, N. (2016). Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads. PLoS ONE, 11.10.1371/journal.pone.0150989 ↗
33. Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., and Gao, J. (2018, January 19–23). EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.10.1145/3219819.3219903 ↗
34. Chen, Y., Li, D., Zhang, P., Sui, J., Lv, Q., Tun, L., and Shang, L. (2022, January 25–29). Cross-modal ambiguity learning for multimodal fake news detection. Proceedings of the ACM Web Conference 2022, Lyon, France.10.1145/3485447.3511968 ↗
35. DAMMFND: Domain-Aware Multimodal Multi-view Fake News Detection
36. Visualizing data using t-SNE