# Deception Detection with Feature-Augmentation by soft Domain Transfer

arXiv: https://arxiv.org/abs/2305.01011 · HTML: https://mlsanity.com/paper/2305.01011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [SGG: Spinbot, Grammarly and GloVe based Fake News Detection](https://mlsanity.com/paper/2008.06854/) (2020)
- [Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification](https://mlsanity.com/paper/2009.01047/) (2020)
- [A Domain-Independent Holistic Approach to Deception Detection](https://mlsanity.com/paper/2111.10711/) (2021)

## Built on by (most cited first, 2 of 2)

- [Domain-Independent Deception: A New Taxonomy and Linguistic Analysis](https://mlsanity.com/paper/2402.01019/) (2024)
- [Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection](https://mlsanity.com/paper/2410.14814/) (2024)

## Abstract (arXiv)

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.