MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning
This addresses the problem of early-stage misinformation detection on social media for platforms and users, where data from new domains is scarce, though it appears incremental as it builds on existing meta-learning and domain adaptation techniques.
The paper tackles domain adaptation for few-shot misinformation detection across emerging topics like COVID-19, proposing MetaAdapt, a meta-learning method that uses limited target examples to guide knowledge transfer from source domains, achieving better performance than state-of-the-art baselines and LLMs with substantially reduced parameters.
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.