In the real-world application of COVID-19 misinformation detection, a
fundamental challenge is the lack of the labeled COVID data to enable
supervised end-to-end training of the models, especially at the early stage of
the pandemic. To address this challenge, we propose an unsupervised domain
adaptation framework using contrastive learning and adversarial domain mixup to
transfer the knowledge from an existing source data domain to the target
COVID-19 data domain. In particular, to bridge the gap between the source
domain and the target domain, our method reduces a radial basis function (RBF)
based discrepancy between these two domains. Moreover, we leverage the power of
domain adversarial examples to establish an intermediate domain mixup, where
the latent representations of the input text from both domains could be mixed
during the training process. Extensive experiments on multiple real-world
datasets suggest that our method can effectively adapt misinformation detection
systems to the unseen COVID-19 target domain with significant improvements
compared to the state-of-the-art baselines