Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of news articles based on limited fact-checks. Existing approaches typically follow a "Train-from-Scratch" paradigm, which is fundamentally bounded by the availability of large-scale annotated data. While expressive pre-trained language models (PLMs) have been adapted in a "Pre-Train-and-Fine-Tune" manner, the inconsistency between pre-training and downstream objectives also requires costly task-specific supervision. In this paper, we propose "Prompt-and-Align" (P&A), a novel prompt-based paradigm for few-shot fake news detection that jointly leverages the pre-trained knowledge in PLMs and the social context topology. Our approach mitigates label scarcity by wrapping the news article in a task-related textual prompt, which is then processed by the PLM to directly elicit task-specific knowledge. To supplement the PLM with social context without inducing additional training overheads, motivated by empirical observation on user veracity consistency (i.e., social users tend to consume news of the same veracity type), we further construct a news proximity graph among news articles to capture the veracity-consistent signals in shared readerships, and align the prompting predictions along the graph edges in a confidence-informed manner. Extensive experiments on three real-world benchmarks demonstrate that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.

翻译：尽管自动虚假新闻检测取得了显著进展，但由于新闻的时效性，如何基于有限的事实核查有效预测新闻文章的真实性仍是一个关键开放性问题。现有方法通常遵循“从头训练”范式，该范式从根本上受限于大规模标注数据的可用性。虽然表达能力强的预训练语言模型已以“预训练-微调”的方式被采用，但预训练目标与下游目标之间的不一致性仍需昂贵的任务特定监督。本文提出“Prompt-and-Align”（P&A），一种新颖的基于提示的少样本虚假新闻检测范式，该范式联合利用了预训练语言模型中的预训练知识与社交上下文拓扑。我们的方法通过将新闻文章封装至任务相关的文本提示中，并由预训练语言模型处理以直接激发任务特定知识，从而缓解标签稀缺问题。为了在无需引入额外训练开销的情况下为预训练语言模型补充社交上下文，基于对用户真实性一致性（即社交用户倾向于消费相同真实性类型的新闻）的实证观察，我们进一步在新闻文章间构建新闻邻近图以捕获共享读者群中的真实性一致信号，并沿图边以置信度感知的方式对齐提示预测结果。在三个真实世界基准上的大量实验表明，P&A以显著优势刷新了少样本虚假新闻检测性能的最新水平。