Text Analysis in Adversarial Settings: Does Deception Leave a Stylistic Trace?
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2019.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 1.
4 papers.
- 2022How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processingcited by 16
- 2022A comparison of several AI techniques for authorship attribution on Romanian textscited by 4
- 2024Domain-Independent Deception: A New Taxonomy and Linguistic Analysiscited by 3
- 2022A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deceptioncited by 1
- 2024Domain-Independent Deception: A New Taxonomy and Linguistic Analysis1 Feb 2024
- 2022A comparison of several AI techniques for authorship attribution on Romanian texts9 Nov 2022
- 2022A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception11 Aug 2022
- 2022How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing20 May 2022