Learning Domain-Invariant Features for Out-of-Context News Detection
Authors: Yimeng Gu, Mengqi Zhang, Ignacio Castro, Shu Wu, Gareth Tyson


Reviewer paper summaries: This paper proposed a domain adaptation method for out-of-context news detection, which learns domain-invariant features via contrastive learning and maximum mean discrepancy and further assists domain adaptation via test-time adaptation. 
The authors experimented with Twitter-COMMs and NewsCLIPpings to evaluate the model's effectiveness in target domains. 
Finally, they visualized with T-SNE to show that their learned features are more domain invariant.
Summary of strengths: The research question is important and promising, and the proposed method is easy to understand and reproduce.
The experimental results indicate some effectiveness of ConDA-TTA and its components.
Summary of weaknesses: 1. As far as I know, a research work named "Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer" has been missed for the related work of domain adaptive fake news detection, and the corresponding methods DITFEND should be included in baselines.

2. I think the proposed method can adapt to any multimodal domain adaptation problems, where is the special design for out-of-context news detection? 

3. From the results on NewsCLIPpings, the improvements are not stable, which shows a poor generalization ability of ConDA-TTA.
Comments, suggestions and typos: Many citations at the front of the sentence adopt wrong citation formats, mostly in Section 2 Related Work.
Overall assessment scores: 2