Detecting and Understanding Textual Deepfakes in Online Reviews
Year: 2022
Type: conference-paper
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.
Authors: Peter Kowalczyk, Marco Röder, Alexander Dürr, Frédéric Thiesse
Institution: University of Würzburg
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4205705588