Deconstructing Review Deception: A Study on Counterfactual Explanation and XAI in Detecting Fake and GPT-Generated Reviews
Files
Date
2024-01-03
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
467
Ending Page
Alternative Title
Abstract
Our models not only deliver high-performing predictions but also illuminate the decision-making processes underlying these predictions. By experimenting with five datasets, we have showcased our framework's prowess in generating diverse and specific counterfactuals, thereby enhancing deception detection capabilities and supporting review authenticity assessments. The results demonstrate the significant contribution of our research in furthering the understanding of AI-generated review detection and, more broadly, AI interpretability. Experimentation on five datasets reveals our framework's ability to produce diverse and specific counterfactuals, significantly enriching deception detection capabilities and facilitating the evaluation of review authenticity. Our robust model offers a novel contribution to the understanding of AI applications, marking a significant step forward in both the detection of deceptive reviews and the broader field of AI interpretability.
Description
Keywords
Conversational AI and Ethical Issues, counterfactual explanation, fake review detection, generated reviews, gpt, xai
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.