survey

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

Authors:

Tommi Gröndahl,

N. AsokanAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 3

Article No.: 45, Pages 1 - 36

https://doi.org/10.1145/3310331

Published: 18 June 2019 Publication History

Abstract

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 a 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.

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Alhayan FHimdi HAlharbi B(2024)Unveiling Deception in Arabic: Optimization of Deceptive Text Detection Across Formal and Informal GenresIEEE Access10.1109/ACCESS.2024.342453112(94216-94230)Online publication date: 2024
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Index Terms

Text Analysis in Adversarial Settings: Does Deception Leave a Stylistic Trace?
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Machine translation
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections
  2. Security services
    1. Pseudonymity, anonymity and untraceability

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 3

May 2020

734 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3341324

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Publication History

Published: 18 June 2019

Accepted: 01 January 2019

Revised: 01 January 2019

Received: 01 September 2017

Published in CSUR Volume 52, Issue 3

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Alhayan FHimdi HAlharbi B(2024)Unveiling Deception in Arabic: Optimization of Deceptive Text Detection Across Formal and Informal GenresIEEE Access10.1109/ACCESS.2024.342453112(94216-94230)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3424531
Guarasci RCatelli REsposito M(2024)Classifying deceptive reviews for the cultural heritage domain: A lexicon-based approach for the Italian languageExpert Systems with Applications10.1016/j.eswa.2024.124131252(124131)Online publication date: Oct-2024
https://doi.org/10.1016/j.eswa.2024.124131
Carmichael J(2024)Reframing and Broadening Adversarial Stylometry for Academic IntegritySecond Handbook of Academic Integrity10.1007/978-3-031-54144-5_148(1467-1485)Online publication date: 18-Feb-2024
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Bauer TGribble LClifford JJones AMurchison NWisniewski K(2023)Use of Compression Analytics to Detect Deception2023 10th International Conference on Behavioural and Social Computing (BESC)10.1109/BESC59560.2023.10386884(1-7)Online publication date: 30-Oct-2023
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