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Padashi, Mojtaba, Roostaee, Meysam, Zeynali, Hassan, Jafari, Alireza. (1404). Synergistic Content Understanding: Misinformation Detection through Contrastive Regularization and Embedding-Space Mixup. پژوهشنامه مدیریت اجرایی, 2(4), 59-68. doi: 10.22080/cste.2025.29798.1073
Mojtaba Padashi; Meysam Roostaee; Hassan Zeynali; Alireza Jafari. "Synergistic Content Understanding: Misinformation Detection through Contrastive Regularization and Embedding-Space Mixup". پژوهشنامه مدیریت اجرایی, 2, 4, 1404, 59-68. doi: 10.22080/cste.2025.29798.1073
Padashi, Mojtaba, Roostaee, Meysam, Zeynali, Hassan, Jafari, Alireza. (1404). 'Synergistic Content Understanding: Misinformation Detection through Contrastive Regularization and Embedding-Space Mixup', پژوهشنامه مدیریت اجرایی, 2(4), pp. 59-68. doi: 10.22080/cste.2025.29798.1073
Padashi, Mojtaba, Roostaee, Meysam, Zeynali, Hassan, Jafari, Alireza. Synergistic Content Understanding: Misinformation Detection through Contrastive Regularization and Embedding-Space Mixup. پژوهشنامه مدیریت اجرایی, 1404; 2(4): 59-68. doi: 10.22080/cste.2025.29798.1073

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

Contributions of Science and Technology for Engineering
مقاله 6، دوره 2، شماره 4، آذر 2025، صفحه 59-68    اصل مقاله (607.63 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/cste.2025.29798.1073
نویسندگان
Mojtaba Padashi؛ Meysam Roostaee* ؛ Hassan Zeynali؛ Alireza Jafari
Department of Computer Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
تاریخ دریافت: 16 مرداد 1404،  تاریخ بازنگری: 09 شهریور 1404،  تاریخ پذیرش: 10 شهریور 1404 
چکیده
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.
کلیدواژه‌ها
Misinformation Detection؛ Representation Learning؛ Supervised Contrastive Learning؛ Embedding-Space Mixup
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