With the rapid development of the Internet, traditional news channels are being supplanted, leading to an increased prevalence of fake news. Mainstream pre-trained language models (PLMs)-based fake news detection methods follow the ‘pre-training and fine-tuning’ paradigm, relying on full supervision and heavily dependent on large, high-quality datasets. In contrast to these methods, “pre-trained and prompt-tuning” offers more efficient learning, especially in data-scarce scenarios. Meanwhile, extensive analysis of social patterns reveals a tendency driven by user psychology and behavior: users often disseminate information that aligns with their pre-existing beliefs, thereby reinforcing and solidifying their convictions. This phenomenon is termed “social context veracity dissemination consistency”. Inspired by this phenomenon, we propose DisCo-FEND, A social context veracity Dissemination Consistency-guided case reasoning augmentation for the Fake News Detection (FEND) task. During model inference, we adopt a novel strategy that enhances reasoning by using multiple FEND cases. It leverages multiple news cases with higher dissemination consistency to refine predictions. Additionally, a high-quality label words acquisition approach and an adaptive weight allocation-based multi-label words mapping strategy improves the convergence and generalization of DisCo-FEND.