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Related Experiment Video

Updated: Aug 11, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Fake review identification and utility evaluation model using machine learning.

Wonil Choi¹, Kyungmin Nam¹, Minwoo Park¹

¹Department of Business Administration, Sungkyunkwan University, Seoul, South Korea.

Frontiers in Artificial Intelligence

|February 6, 2023

Summary

This summary is machine-generated.

Fake online reviews harm e-commerce. This study develops a machine learning model to identify authentic and useful product reviews, enhancing platform credibility and user trust.

Keywords:

SVC e-commerce fake review fake review detection technique logistic regression machine learning useful reviews

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Published on: January 11, 2020

Area of Science:

E-commerce and Information Science
Machine Learning Applications

Background:

The proliferation of e-commerce platforms has led to a surge in online product reviews.
An increasing prevalence of fake reviews compromises the quality and reliability of online feedback.
Malicious false reviews cause significant harm to both retailers and consumers, eroding trust.

Purpose of the Study:

To address the challenge of discerning genuine and valuable reviews amidst a high volume of online feedback.
To develop a robust model for assessing the authenticity and usefulness of e-commerce product reviews.
To mitigate the negative impact of fake reviews on e-commerce platform credibility and user engagement.

Main Methods:

Construction of a machine learning model to evaluate online review authenticity.
Implementation of review filtering and classification algorithms.
Utilizing data from e-commerce platforms to train and validate the model.

Main Results:

The developed model demonstrates capability in distinguishing between authentic and fake reviews.
Effective classification of reviews based on their usefulness to potential buyers.
Potential for improved user decision-making and reduced uncertainty in online purchases.

Conclusions:

Machine learning offers a viable solution for combating fake reviews in e-commerce.
Accurate review authenticity and usefulness assessment can restore trust and traffic to online platforms.
The study provides a framework for enhancing the integrity of online review systems.