Synergistic Content Understanding: Misinformation Detection through Contrastive Regularization and Embedding-Space Mixup

Document Type : Original Article

Authors

Department of Computer Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran

Abstract

Automated fake-news detection is a critical challenge for preserving the integrity of the online information ecosystem. Current state-of-the-art systems increasingly depend on external context, such as social propagation graphs, which fundamentally limits their applicability in real-time or “cold-start” scenarios where such signals are unavailable. We challenge the prevailing assumption that this external context is indispensable for top-tier performance. Instead, we argue that the primary bottleneck is the brittle and poorly structured content representations learned via standard model fine-tuning. To address this, we propose a synergistic training framework that sculpts a more robust and discriminative embedding space. Our method harmonizes two complementary and powerful techniques: (1) supervised contrastive regularization, which explicitly structures the feature space by enforcing tight intra-class clustering and clear inter-class separation, and (2) embedding-space mixup, a regularization strategy that creates smoother, more generalizable decision boundaries. On two widely used public benchmarks, Twitter15 and Twitter16, our purely content-only framework establishes a new state-of-the-art achieving Weighted F1-scores of 94.2% and 94.7%, respectively, significantly outperforming not only other text-based models but also leading context-aware methods. Our results demonstrate that, with a sufficiently rigorous training regimen, the intrinsic signals within text alone can drive superior veracity assessment.

Keywords

References

  1. Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S. A., Sunstein, C. R., Thorson, E. A., Watts, D. J., & Zittrain, J. L. (2018). The science of fake news: Addressing fake news requires a multidisciplinary effort. Science, 359(6380), 1094–1096. doi:10.1126/science.aao2998.
  2. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. doi:10.1126/science.aap9559.
  3. Guo, Y., Ji, S., Fang, X., Chiu, D. K. W., Cao, N., & Leung, H. (2025). An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning. Cybersecurity, 8(1). doi:10.1186/s42400-024-00342-5.
  4. Xie, B., Ma, X., Wu, J., Yang, J., Xue, S., & Fan, H. (2023). Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection. 35th International Conference on Scientific and Statistical Database Management, 1–11. doi:10.1145/3603719.3603736.
  5. Kuntur, S., Krzywda, M., Wróblewska, A., Paprzycki, M., & Ganzha, M. (2024). Comparative Analysis of Graph Neural Networks and Transformers for Robust Fake News Detection: A Verification and Reimplementation Study. Electronics (Switzerland), 13(23), 4784. doi:10.3390/electronics13234784.
  6. Mridha, M. F., Keya, A. J., Hamid, Md. A., Monowar, M. M., & Rahman, Md. S. (2021). A Comprehensive Review on Fake News Detection With Deep Learning. IEEE Access, 9, 156151–156170. doi:10.1109/access.2021.3129329.
  7. Aïmeur, E., Amri, S., & Brassard, G. (2023). Fake news, disinformation and misinformation in social media: a review. Social Network Analysis and Mining, 13(1), 1–36. doi:10.1007/s13278-023-01028-5.
  8. Farhangian, F., Cruz, R. M. O., & Cavalcanti, G. D. C. (2024). Fake news detection: Taxonomy and comparative study. Information Fusion, 103, 102140. doi:10.1016/j.inffus.2023.102140.
  9. Ma, J., Gao, W., & Wong, K. F. (2018). Rumor detection on twitter with tree-structured recursive neural networks. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 1, 1980–1989. doi:10.18653/v1/p18-1184.
  10. Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., & Huang, J. (2020). Rumor detection on social media with bi-directional graph convolutional networks. AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 34(1), 549–556. doi:10.1609/aaai.v34i01.5393.
  11. Ozcelik, O., Toraman, C., & Can, F. (2025). Detecting Misinformation on Social Media using Community Insights and Contrastive Learning. ACM Transactions on Intelligent Systems and Technology, 16(2). doi:10.1145/3709009.
  12. Truică, C. O., Apostol, E. S., & Karras, P. (2024). DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection. Knowledge-Based Systems, 294, 111715. doi:10.1016/j.knosys.2024.111715.
  13. Jovanović, A., & Ross, B. (2023). Rumour Detection in the Wild: A Browser Extension for Twitter. 3rd Workshop for Natural Language Processing Open Source Software, NLP-OSS 2023, Proceedings of the Workshop, 130–140. doi:10.18653/v1/2023.nlposs-1.15.
  14. Koloski, B., Stepišnik Perdih, T., Robnik-Šikonja, M., Pollak, S., & Škrlj, B. (2022). Knowledge graph informed fake news classification via heterogeneous representation ensembles. Neurocomputing, 496, 208–226. doi:10.1016/j.neucom.2022.01.096.
  15. Shen, X., Huang, M., Hu, Z., Cai, S., & Zhou, T. (2024). Multimodal Fake News Detection with Contrastive Learning and Optimal Transport. Frontiers in Computer Science, 6. doi:10.3389/fcomp.2024.1473457.
  16. Mu, G., Chen, C., Li, X., Chen, Y., Dai, J., & Li, J. (2025). CLAAF: Multimodal fake information detection based on contrastive learning and adaptive Agg-modality fusion. PLoS ONE, 20(5 MAY), 322556. doi:10.1371/journal.pone.0322556.
  17. Liu, H., Wang, W., & Li, H. (2023). Interpretable Multimodal Misinformation Detection with Logic Reasoning. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 9781–9796. doi:10.18653/v1/2023.findings-acl.620.
  18. Ahmad, P. N., Guo, J., AboElenein, N. M., Haq, Q. M. ul, Ahmad, S., Algarni, A. D., & A. Ateya, A. (2025). Hierarchical graph-based integration network for propaganda detection in textual news articles on social media. Scientific Reports, 15(1). doi:10.1038/s41598-024-74126-9.
  19. Farhoudinia, B., Ozturkcan, S., & Kasap, N. (2024). Emotions unveiled: detecting COVID-19 fake news on social media. Humanities and Social Sciences Communications, 11(1), 504. doi:10.1057/s41599-024-03083-5.
  20. Wu, X., Huang, K.-H., Fung, Y., & Ji, H. (2022). Cross-document Misinformation Detection based on Event Graph Reasoning. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. doi:10.18653/v1/2022.naacl-main.40.
  21. Roostaee, M. (2022). Citation Worthiness Identification for Fine-Grained Citation Recommendation Systems. Iranian Journal of Science and Technology - Transactions of Electrical Engineering, 46(2), 353–365. doi:10.1007/s40998-021-00472-3.
  22. Giachanou, A., Ghanem, B., Ríssola, E. A., Rosso, P., Crestani, F., & Oberski, D. (2022). The impact of psycholinguistic patterns in discriminating between fake news spreaders and fact checkers. Data & Knowledge Engineering, 138, 101960. doi:10.1016/j.datak.2021.101960.
  23. Pillai, S. E. V. S., & Hu, W.-C. (2023). Misinformation Detection Using an Ensemble Method with Emphasis on Sentiment and Emotional Analyses. 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA), 295–300. doi:10.1109/sera57763.2023.10197706.
  24. Abdali, S., Shaham, S., & Krishnamachari, B. (2024). Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities. ACM Computing Surveys, 57(3). doi:10.1145/3697349.
  25. Yue, Z., Zeng, H., Kou, Z., Shang, L., & Wang, D. (2022). Contrastive Domain Adaptation for Early Misinformation Detection. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2423–2433. doi:10.1145/3511808.3557263.
  26. Nan, Q., Cao, J., Zhu, Y., Wang, Y., & Li, J. (2021). MDFEND: Multi-domain Fake News Detection. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 3343–3347. doi:10.1145/3459637.3482139.
  27. Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., & Krishnan, D. (2020). Supervised contrastive learning. 34rd Conference on Neural Information Processing Systems (NeurIPS 2020), 33, 18661-18673, 6-12, December, 2020, Vancouver, Canada.
  28. Huang, L., Zhang, C., & Zhang, H. (2020). Self-adaptive training: beyond empirical risk minimization. Advances in neural information processing systems, 33, 19365-19376.
  29. Cavus, N., Goksu, M., & Oktekin, B. (2024). Real-time fake news detection in online social networks: FANDC Cloud-based system. Scientific Reports, 14(1). doi:10.1038/s41598-024-76102-9.
  30. Wan, H., Feng, S., Tan, Z., Wang, H., Tsvetkov, Y., & Luo, M. (2024). DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection. Findings of the Association for Computational Linguistics ACL 2024, 2637–2667. doi:10.18653/v1/2024.findings-acl.155.
  31. Ma, J., Gao, W., & Wong, K.-F. (2017). Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. doi:10.18653/v1/p17-1066.
  32. Liu, Y., & Wu, Y. F. B. (2018). Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 32(1), 354–361. doi:10.1609/aaai.v32i1.11268.
  33. He, Z., Li, C., Zhou, F., & Yang, Y. (2021). Rumor Detection on Social Media with Event Augmentations. SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020–2024. doi:10.1145/3404835.3463001.
  34. Zhou, X., Wu, J., & Zafarani, R. (2020). SAFE: Similarity-Aware Multi-modal Fake News Detection. Advances in Knowledge Discovery and Data Mining. PAKDD 2020. Lecture Notes in Computer Science, vol 12085. Springer, Cham, Switzerland. doi:10.1007/978-3-030-47436-2_27.
  35. Alghamdi, J., Lin, Y., & Luo, S. (2023). Towards COVID-19 fake news detection using transformer-based models. Knowledge-Based Systems, 274, 110642. doi:10.1016/j.knosys.2023.110642.
  36. Yue, Z., Zeng, H., Zhang, Y., Shang, L., & Wang, D. (2023). MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. doi:10.18653/v1/2023.acl-long.286.
  37. D'Angelo, F., Andriushchenko, M., Varre, A. V., & Flammarion, N. (2024). Why do we need weight decay in modern deep learning?. Advances in Neural Information Processing Systems, 37, 23191-23223.
  38. Roostaee, M. (2024). Language-independent Profile-based Tag Recommendation for Community Question Answering Systems. International Journal of Engineering Transactions C: Aspects, 37(12), 2547–2559. doi:10.5829/ije.2024.37.12c.13.
  39. Dorrani, Z. (2025). Anomaly Detection in Emerging Crimes with Deep Autoencoder Contributions of Science and Technology for Engineering, 2(3), 45-56. doi:10.22080/cste.2025.28900.1023.
  40. Rasouli, A., Mikaeil, R., Atalou, S., & Esmaeilzadeh, A. (2025). Optimizing Traditional Clustering Methods Using Meta-Heuristic Algorithms for Joint Set Identification in Copper Mines. Contributions of Science and Technology for Engineering, 2(3), 57-72. doi:10.22080/cste.2025.29024.1032.
Contributions of Science and Technology for Engineering
Volume 2, Issue 4
September 2025
Pages 59-68

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History

  • Receive Date: 07 August 2025
  • Revise Date: 31 August 2025
  • Accept Date: 01 September 2025
  • First Publish Date: 06 September 2025
  • Publish Date: 28 September 2025

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