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.

翻译：在信息爆炸的时代，欺骗者利用不同领域或信息媒介（如新闻、电子邮件和推文）来利用用户。尽管已有大量研究针对所有这些领域进行欺骗检测，但新事件中的信息短缺要求这些领域相互关联以对抗欺骗。为建立这种关联，我们提出了一种特征增强方法，通过利用神经模型的中间层表示。我们的方法相比自领域基线模型实现了高达6.60%的提升。我们发现，推文是假新闻和钓鱼邮件检测中最有效的信息提供者，而新闻在推文谣言检测中帮助最大。我们的分析为领域知识迁移提供了有价值的见解，有助于构建比现有文献更强大的欺骗检测系统。