  Out-of-context news is a common type of misinformation on online media
platforms. This involves posting a caption, alongside a mismatched news image.
Existing out-of-context news detection models only consider the scenario where
pre-labeled data is available for each domain, failing to address the
out-of-context news detection on unlabeled domains (e.g. news topics or
agencies). In this work, we therefore focus on domain adaptive out-of-context
news detection. In order to effectively adapt the detection model to unlabeled
news topics or agencies, we propose ConDA-TTA (Contrastive Domain Adaptation
with Test-Time Adaptation) which applies contrastive learning and maximum mean
discrepancy (MMD) to learn domain-invariant features. In addition, we leverage
test-time target domain statistics to further assist domain adaptation.
Experimental results show that our approach outperforms baselines in most
domain adaptation settings on two public datasets, by as much as 2.93% in F1
and 2.08% in accuracy.
