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- W4312761775 abstract "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 Rumour detection. Our analysis provides a useful insight for domain knowledge transfer which can help build a stronger deception detection system than the existing literature." @default.
- W4312761775 created "2023-01-05" @default.
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- W4312761775 date "2022-01-01" @default.
- W4312761775 modified "2023-10-17" @default.
- W4312761775 title "Deception Detection with Feature-Augmentation by Soft Domain Transfer" @default.
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- W4312761775 doi "https://doi.org/10.1007/978-3-031-19097-1_23" @default.
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