With the rapid growth of social media, fake news (rumors) are rampant online, seriously endangering the health of mainstream
social consciousness. Fake news detection (FEND), as a machine learning solution for automatically identifying fake news
on Internet, is increasingly gaining the attentions of academic community and researchers. Recently, the mainstream FEND
approaches relying on deep learning primarily involves fully supervised fine-tuning paradigms based on pre-trained language
models (PLMs), relying on large annotated datasets. In many real scenarios, obtaining high-quality annotated corpora are time consuming, expertise-required, labor-intensive, and expensive, which presents challenges in obtaining a competitive automatic
rumor detection system. Therefore, developing and enhancing FEND towards data-scarce scenarios is becoming increasingly
essential. In this work, inspired by the superiority of semi-/self- supervised learning, we propose a novel few-shot rumor
detection framework based on semi-supervised adversarial learning and self-supervised contrastive learning, named Detection
Yet See Few (DetectYSF). DetectYSF synergizes contrastive self-supervised learning and adversarial semi-supervised learning
to achieve accurate and efficient FEND capabilities with limited supervised data. DetectYSF uses Transformer-based PLMs
(e.g., BERT, RoBERTa) as its backbone and employs a Masked LM-based pseudo prompt learning paradigm for model tuning
(prompt-tuning). Specifically, during DetectYSF training, the enhancement measures for DetectYSF are as follows: 1). We
design a simple but efficient self-supervised contrastive learning strategy to optimize sentence-level semantic embedding
representations obtained from PLMs; 2). We construct a Generation Adversarial Network (GAN), utilizing random noises and
negative fake news samples as inputs, and employing Multi-Layer Perceptrons (MLPs) and an extra independent PLM encoder
to generate abundant adversarial embeddings. Then, incorporated with the adversarial embeddings, we utilize semi-supervised
adversarial learning to further optimize the output embeddings of DetectYSF during its prompt-tuning procedure. From
the news veracity dissemination perspective, we found that the authenticity of the news shared by these collectives tends
to remain consistent, either mostly genuine or predominantly fake, a theory we refer to as “news veracity dissemination
consistency”. By employing an adjacent sub-graph feature aggregation algorithm, we infuse the authenticity characteristics
from neighboring news nodes of the constructed veracity dissemination network during DetectYSF inference. It integrates the
external supervisory signals from “news veracity dissemination consistency” to further refine the news authenticity detection
results of PLM prompt-tuning, thereby enhancing the accuracy of fake news detection. Furthermore, extensive baseline
comparisons and ablated experiments on three widely-used benchmarks demonstrate the effectiveness and superiority of
DetectYSF for few-shot fake new detection under low-resource scenarios.