Cited by 10Spam and Phishing DetectionMisinformation and Its ImpactsAuthorship Attribution and Profiling

Detecting and Understanding Textual Deepfakes in Online Reviews

Open access
Kowalczyk, Peter, Röder, Marco, Dürr, Alexander, Thiesse, Frédéric·2022·Proceedings of the Annual Hawaii International Conference on System Sciences

Abstract

Deepfakes endanger business and society. Regarding fraudulent texts created with deep learning techniques, this may become particularly evident for online reviews. Here, customers naturally rely on truthful information about a product or service to adequately evaluate its worthiness. However, in the light of the proliferation of deepfakes, customers may increasingly harbour distrust and thereby affect a retailers business. To counteract this, we propose a novel IT artifact capable of detecting textual deepfakes to then explain their peculiarities by using explainable artificial intelligence. Finally, we demonstrate the utility of such explanations for the case of online reviews in e-commerce.

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Crossref · 10.24251/hicss.2022.184

Cites

47 of 47 references verified0 citation errors · 0 not checkable

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  • Kowalczyk et al. detected GPT-2-generated fake reviews using Shapley Additive Explanations (SHAP).

    “For instance, Kowalczyk et al. [2022] detected GPT-2-generated fake reviews using Shapley Additive Explanations (SHAP) [Lundberg and Lee, 2017].”

    From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Kowalczyk et al., 2022

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Authors
Kowalczyk, P., Röder, M., Dürr, A., & Thiesse, F.
Affiliations
University of Würzburg (DE)
Venue
Proceedings of the Annual Hawaii International Conference on System Sciences
Published
2022
ISSN
  • 1530-1605 (ISSN-L)
  • 2572-6862
OpenAlex
W4205705588
Cited by
9
References
47
Citations by year
2026: 1 · 2024: 4 · 2023: 3 · 2022: 1
FWCI
1.79
Type
conference paper
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Hawaii International Conference on System Sciences (HICSS) (publishedVersion, cc-by-nc-nd)
  • Journal of the Association for Information Systems (submittedVersion)
  • ScholarSpace (University of Hawaii at Manoa) (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
Keywords
deepfakes, online reviews, explainable AI, e-commerce, consumer trust, text forgery detection

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