Learning Domain-Invariant Features for Out-of-Context News Detection
CoRR(2024)
摘要
Multimodal out-of-context news is a common type of misinformation on online
media platforms. This involves posting a caption, alongside an invalid
out-of-context news image. Reflecting its importance, researchers have
developed models to detect such misinformation. However, a common limitation of
these models is that they 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., unverified news on new 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 the domain-invariant feature. In addition, it leverages target
domain statistics during test-time to further assist domain adaptation.
Experimental results show that our approach outperforms baselines in 5 out of 7
domain adaptation settings on two public datasets, by as much as 2.93
and 2.08
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