Deception Detection with Feature-Augmentation by soft Domain Transfer
This work addresses the challenge of information shortage in new events for deception detection, which is incremental as it builds on existing domain-specific methods.
The paper tackled the problem of deception detection across different domains (News, Emails, Tweets) by proposing a feature augmentation method using intermediate neural representations, resulting in an improvement of up to 6.60% over baseline models.
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.