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

TL;DR

This paper introduces Prompt-and-Align, a prompt-based method that combines pre-trained language models and social context graphs to improve few-shot fake news detection, achieving state-of-the-art results with limited labeled data.

Contribution

The paper proposes a novel prompt-based framework that leverages social context graphs to enhance few-shot fake news detection without extensive task-specific training.

Findings

P&A outperforms existing methods on three benchmarks.

The approach effectively utilizes social context to improve accuracy.

Significant performance gains in low-data scenarios.

Abstract

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.