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With the rapid evolution of social media, fake news has become a significantsocial problem, which cannot be addressed in a timely manner using manualinvestigation. This has motivated numerous studies on automating fake newsdetection. Most studies explore supervised training models with differentmodalities (e.g., text, images, and propagation networks) of news records toidentify fake news. However, the performance of such techniques generally dropsif news records are coming from different domains (e.g., politics,entertainment), especially for domains that are unseen or rarely-seen duringtraining. As motivation, we empirically show that news records from differentdomains have significantly different word usage and propagation patterns.Furthermore, due to the sheer volume of unlabelled news records, it ischallenging to select news records for manual labelling so that thedomain-coverage of the labelled dataset is maximized. Hence, this work: (1)proposes a novel framework that jointly preserves domain-specific andcross-domain knowledge in news records to detect fake news from differentdomains; and (2) introduces an unsupervised technique to select a set ofunlabelled informative news records for manual labelling, which can beultimately used to train a fake news detection model that performs well formany domains while minimizing the labelling cost. Our experiments show that theintegration of the proposed fake news model and the selective annotationapproach achieves state-of-the-art performance for cross-domain news datasets,while yielding notable improvements for rarely-appearing domains in newsdatasets.

Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data