Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News
  Detection
Jiaying Wu Shen Li Ailin Deng Miao Xiong Bryan Hooi
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.