MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning

TL;DR

MetaAdapt is a meta learning approach that effectively adapts misinformation detection models to new domains with limited data, outperforming existing methods and large language models in real-world scenarios.

Contribution

It introduces a novel meta learning framework that leverages source tasks and similarity scores to improve domain adaptation in few-shot misinformation detection.

Findings

MetaAdapt outperforms state-of-the-art baselines.

It achieves better results with fewer parameters.

Effective in real-world datasets for emerging misinformation topics.

Abstract

With emerging topics (e.g., COVID-19) on social media as a source for the spreading misinformation, overcoming the distributional shifts between the original training domain (i.e., source domain) and such target domains remains a non-trivial task for misinformation detection. This presents an elusive challenge for early-stage misinformation detection, where a good amount of data and annotations from the target domain is not available for training. To address the data scarcity issue, we propose MetaAdapt, a meta learning based approach for domain adaptive few-shot misinformation detection. MetaAdapt leverages limited target examples to provide feedback and guide the knowledge transfer from the source to the target domain (i.e., learn to adapt). In particular, we train the initial model with multiple source tasks and compute their similarity scores to the meta task. Based on the similarity scores, we rescale the meta gradients to adaptively learn from the source tasks. As such, MetaAdapt can learn how to adapt the misinformation detection model and exploit the source data for improved performance in the target domain. To demonstrate the efficiency and effectiveness of our method, we perform extensive experiments to compare MetaAdapt with state-of-the-art baselines and large language models (LLMs) such as LLaMA, where MetaAdapt achieves better performance in domain adaptive few-shot misinformation detection with substantially reduced parameters on real-world datasets.