Alt Text: Framework for Fake Review Detection
Title: Proposed Framework to Improve Fake Review Detection
Caption: Enhancing Fake Review Detection Through Systematic Methodologies
Description: Proposes a theoretical framework with six phases to enhance fake review detection using deep learning, machine learning, and time series analysis.

Proposed Framework to Improve Fake Review Detection

International Journal of Computer Techniques – Volume 12 Issue 2, 2025

By Mohammed Hitham M.H

PhD in Information Systems | Independent Researcher in Digital Transformation & Data Analysis

http://www.ijctjournal.org

Abstract

In recent years, people have increasingly turned to e-commerce for purchasing products and accessing services, moving away from traditional methods. Online platforms allow customers to share their feedback through reviews, which help companies understand customer needs and assist other consumers in making informed decisions. However, these reviews can be either genuine or fraudulent, making Fake Review Detection (FRD) a crucial research area.

This study presents a systematic review of existing literature on FRD and extends previous research to enhance detection methods. The paper serves two main purposes. First, it aims to support research by identifying future directions in FRD and facilitating access to relevant studies. The findings provide a taxonomy of research directions in fake review detection, highlighting the advantages and limitations of existing approaches in preprocessing, feature selection, and detection techniques.

Second, the paper proposes a theoretical framework for improving fake review detection. This framework consists of six phases: data collection, preprocessing, feature extraction and selection, handling data imbalance, future prediction (using a hybrid approach combining deep learning, machine learning, and time series analysis), and performance evaluation. By outlining these phases, this research aims to enhance the effectiveness of fake review detection and contribute to the ongoing development of FRD methodologies.

Keywords

Fake Review, Fake Review Detection, Machine Learning, Ensemble Classifiers, Deep Learning, Time Series

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