Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/79516

Detecting and Understanding Textual Deepfakes in Online Reviews

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Title:Detecting and Understanding Textual Deepfakes in Online Reviews
Authors:Kowalczyk, Peter
Röder, Marco
Dürr, Alexander
Thiesse, Frédéric
Keywords:Explainable Artificial Intelligence (XAI)
deep fake detection
deep learning
fraud detection
online reviews
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Date Issued:04 Jan 2022
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/79516
ISBN:978-0-9981331-5-7
DOI:10.24251/HICSS.2022.184
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Explainable Artificial Intelligence (XAI)


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