  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.
