# Domain-Independent Deception: A New Taxonomy and Linguistic Analysis

arXiv: https://arxiv.org/abs/2402.01019 · HTML: https://mlsanity.com/paper/2402.01019/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 102 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Combating Fake News: A Survey on Identification and Mitigation Techniques](https://mlsanity.com/paper/1901.06437/) (2019)
- [Fake News Early Detection: An Interdisciplinary Study](https://mlsanity.com/paper/1904.11679/) (2019)
- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)
- [SOK: A Comprehensive Reexamination of Phishing Research from the Security Perspective](https://mlsanity.com/paper/1911.00953/) (2019)
- [Text Analysis in Adversarial Settings: Does Deception Leave a Stylistic Trace?](https://mlsanity.com/paper/1902.08939/) (2019)
- [A Domain-Independent Holistic Approach to Deception Detection](https://mlsanity.com/paper/2111.10711/) (2021)
- [Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages](https://mlsanity.com/paper/2104.11761/) (2021)
- [Deception Detection with Feature-Augmentation by soft Domain Transfer](https://mlsanity.com/paper/2305.01011/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Roadmap for Multilingual, Multimodal Domain Independent Deception Detection](https://mlsanity.com/paper/2405.03920/) (2024)
- [The Pitfalls of Publishing in the Age of LLMs: Strange and Surprising Adventures with a High-Impact NLP Journal](https://mlsanity.com/paper/2407.12026/) (2024)
- [Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection](https://mlsanity.com/paper/2410.14814/) (2024)

## Abstract (arXiv)

Internet-based economies and societies are drowning in deceptive attacks. These attacks take many forms, such as fake news, phishing, and job scams, which we call ``domains of deception.'' Machine-learning and natural-language-processing researchers have been attempting to ameliorate this precarious situation by designing domain-specific detectors. Only a few recent works have considered domain-independent deception. We collect these disparate threads of research and investigate domain-independent deception. First, we provide a new computational definition of deception and break down deception into a new taxonomy. Then, we analyze the debate on linguistic cues for deception and supply guidelines for systematic reviews. Finally, we investigate common linguistic features and give evidence for knowledge transfer across different forms of deception.