Title

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

COinS