ORIGINAL RESEARCH article
Front. Big Data
Sec. Cybersecurity and Privacy
Volume 8 - 2025 | doi: 10.3389/fdata.2025.1581734
Domain-Independent Deception: A New Taxonomy and Linguistic Analysis *
Provisionally accepted
- 1University of Houston, Houston, United States
- 2Tel Aviv University, Tel Aviv, Tel Aviv, Israel
- 3University of California, Berkeley, Berkeley, California, United States
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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.
Keywords: Automatic/computational deception detection, Cross domain, Domain independent, email/message scams, fake news, Meta-analysis, opinion spam, phishing
Received: 23 Feb 2025; Accepted: 03 Sep 2025.
Copyright: © 2025 Verma, Dershowitz, Zeng, Boumber and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Rakesh M Verma, University of Houston, Houston, United States
Nachum Dershowitz, Tel Aviv University, Tel Aviv, 6997801, Tel Aviv, Israel
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.