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Despite recent progress in improving the performance of misinformationdetection systems, classifying misinformation in an unseen domain remains anelusive challenge. To address this issue, a common approach is to introduce adomain critic and encourage domain-invariant input features. However, earlymisinformation often demonstrates both conditional and label shifts againstexisting misinformation data (e.g., class imbalance in COVID-19 datasets),rendering such methods less effective for detecting early misinformation. Inthis paper, we propose contrastive adaptation network for early misinformationdetection (CANMD). Specifically, we leverage pseudo labeling to generatehigh-confidence target examples for joint training with source data. Weadditionally design a label correction component to estimate and correct thelabel shifts (i.e., class priors) between the source and target domains.Moreover, a contrastive adaptation loss is integrated in the objective functionto reduce the intra-class discrepancy and enlarge the inter-class discrepancy.As such, the adapted model learns corrected class priors and an invariantconditional distribution across both domains for improved estimation of thetarget data distribution. To demonstrate the effectiveness of the proposedCANMD, we study the case of COVID-19 early misinformation detection and performextensive experiments using multiple real-world datasets. The results suggestthat CANMD can effectively adapt misinformation detection systems to the unseenCOVID-19 target domain with significant improvements compared to thestate-of-the-art baselines.

Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19