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% in F1 and 2.08% in accuracy.

翻译：多模态上下文外新闻是在线媒体平台上常见的一类虚假信息。这涉及发布一个标题，并配上一张无效的上下文外新闻图片。鉴于其重要性，研究人员已开发出检测此类虚假信息的模型。然而，这些模型的一个普遍局限是，它们仅考虑每个领域都有预标记数据可用的场景，未能解决未标记领域（例如，关于新主题或新机构的未经核实的新闻）上的上下文外新闻检测问题。因此，在本工作中，我们专注于领域自适应的上下文外新闻检测。为了有效地使检测模型适应未标记的新闻主题或机构，我们提出了ConDA-TTA（基于测试时自适应的对比领域自适应），该方法应用对比学习和最大均值差异（MMD）来学习领域不变特征。此外，它利用测试时的目标领域统计信息来进一步辅助领域自适应。实验结果表明，在两个公共数据集的7种领域自适应设置中，我们的方法在5种设置上优于基线模型，F1分数最高提升2.93%，准确率最高提升2.08%。