# Text Analysis in Adversarial Settings: Does Deception Leave a Stylistic Trace?

arXiv: https://arxiv.org/abs/1902.08939 · HTML: https://mlsanity.com/paper/1902.08939/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing](https://mlsanity.com/paper/2205.10095/) (2022)
- [A comparison of several AI techniques for authorship attribution on Romanian texts](https://mlsanity.com/paper/2211.05180/) (2022)
- [Domain-Independent Deception: A New Taxonomy and Linguistic Analysis](https://mlsanity.com/paper/2402.01019/) (2024)
- [A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception](https://mlsanity.com/paper/2208.05757/) (2022)

## Abstract (arXiv)

Textual deception constitutes a major problem for online security. Many studies have argued that deceptiveness leaves traces in writing style, which could be detected using text classification techniques. By conducting an extensive literature review of existing empirical work, we demonstrate that while certain linguistic features have been indicative of deception in certain corpora, they fail to generalize across divergent semantic domains. We suggest that deceptiveness as such leaves no content-invariant stylistic trace, and textual similarity measures provide superior means of classifying texts as potentially deceptive. Additionally, we discuss forms of deception beyond semantic content, focusing on hiding author identity by writing style obfuscation. Surveying the literature on both author identification and obfuscation techniques, we conclude that current style transformation methods fail to achieve reliable obfuscation while simultaneously ensuring semantic faithfulness to the original text. We propose that future work in style transformation should pay particular attention to disallowing semantically drastic changes.