Designing a Deceptive Comment Detection Platform with a Rule-based Artificial Intelligent Architecture
Publication Date
1-1-2021
Document Type
Conference Proceeding
Publication Title
2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021
DOI
10.1109/IEEM50564.2021.9672994
First Page
1442
Last Page
1445
Abstract
One of the most important factors in a purchasing decision nowadays is the evaluation of comments online. Businesses or individuals use deceptive comments to mislead people for the sake of economic gain and thus hurt the benefit of the customers and the welfare of society. In this research, we propose a rule-based artificial intelligence (AI) machine learning (ML) architecture for online review fraud detection. The proposed methodology features an AI and ML hybrid architecture, where ML refers to the common well-developed machine learning models, and the AI part features a rules-based controller that prioritizes and customizes the fraud detection rules based on human intelligence to improve the accuracy of the result and computational efficiency.
Keywords
Deceptive comment detection, Fake review, Machine learning, Rule-based system
Department
Industrial and Systems Engineering
Recommended Citation
A. Toplu and H. Liu. "Designing a Deceptive Comment Detection Platform with a Rule-based Artificial Intelligent Architecture" 2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021 (2021): 1442-1445. https://doi.org/10.1109/IEEM50564.2021.9672994
