# Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection

arXiv: https://arxiv.org/abs/2410.14814 · HTML: https://mlsanity.com/paper/2410.14814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media](https://mlsanity.com/paper/1610.07363/) (2016)
- [DQI: Measuring Data Quality in NLP](https://mlsanity.com/paper/2005.00816/) (2020)
- [Universal Spam Detection using Transfer Learning of BERT Model](https://mlsanity.com/paper/2202.03480/) (2022)
- [Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification](https://mlsanity.com/paper/2009.01047/) (2020)
- [Domain-Independent Deception: A New Taxonomy and Linguistic Analysis](https://mlsanity.com/paper/2402.01019/) (2024)
- [Deception Detection with Feature-Augmentation by soft Domain Transfer](https://mlsanity.com/paper/2305.01011/) (2023)

## Abstract (arXiv)

In the modern age an enormous amount of communication occurs online, and it is difficult to know when something written is genuine or deceitful. There are many reasons for someone to deceive online (e.g., monetary gain, political gain) and detecting this behavior without any physical interaction is a difficult task. Additionally, deception occurs in several text-only domains and it is unclear if these various sources can be leveraged to improve detection. To address this, eight datasets were utilized from various domains to evaluate their effect on classifier performance when combined with transfer learning via intermediate layer concatenation of fine-tuned BERT models. We find improvements in accuracy over the baseline. Furthermore, we evaluate multiple distance measurements between datasets and find that Jensen-Shannon distance correlates moderately with transfer learning performance. Finally, the impact was evaluated of multiple methods, which produce additional information in a dataset's text via named entities, on BERT performance and we find notable improvement in accuracy of up to 11.2%.