  In this era of information explosion, deceivers use different domains or
mediums of information to exploit the users, such as News, Emails, and Tweets.
Although numerous research has been done to detect deception in all these
domains, information shortage in a new event necessitates these domains to
associate with each other to battle deception. To form this association, we
propose a feature augmentation method by harnessing the intermediate layer
representation of neural models. Our approaches provide an improvement over the
self-domain baseline models by up to 6.60%. We find Tweets to be the most
helpful information provider for Fake News and Phishing Email detection,
whereas News helps most in Tweet Rumor detection. Our analysis provides a
useful insight for domain knowledge transfer which can help build a stronger
deception detection system than the existing literature.
