فهرست نشریات علمی دسترسی آزاد ایران

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.