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Despite considerable advances in automated fake news detection, due to thetimely nature of news, it remains a critical open question how to effectivelypredict the veracity of news articles based on limited fact-checks. Existingapproaches typically follow a "Train-from-Scratch" paradigm, which isfundamentally bounded by the availability of large-scale annotated data. Whileexpressive pre-trained language models (PLMs) have been adapted in a"Pre-Train-and-Fine-Tune" manner, the inconsistency between pre-training anddownstream objectives also requires costly task-specific supervision. In thispaper, we propose "Prompt-and-Align" (P&A), a novel prompt-based paradigm forfew-shot fake news detection that jointly leverages the pre-trained knowledgein PLMs and the social context topology. Our approach mitigates label scarcityby wrapping the news article in a task-related textual prompt, which is thenprocessed by the PLM to directly elicit task-specific knowledge. To supplementthe PLM with social context without inducing additional training overheads,motivated by empirical observation on user veracity consistency (i.e., socialusers tend to consume news of the same veracity type), we further construct anews proximity graph among news articles to capture the veracity-consistentsignals in shared readerships, and align the prompting predictions along thegraph edges in a confidence-informed manner. Extensive experiments on threereal-world benchmarks demonstrate that P&A sets new states-of-the-art forfew-shot fake news detection performance by significant margins.

Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection