International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 17, No. 3, 8 Jun. 2025
Cover page and Table of Contents: PDF (size: 598KB)
Cross-platform Fake Review Detection: A Comparative Analysis of Supervised and Deep Learning Models
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Author(s)
Faryad Bigdeli
1,*
Index Terms
Fake Review Detection, Supervised Learning, Deep Learning, Feature Engineering, Cross-platform Analysis
Abstract
This project addresses the growing issue of fake reviews by developing models capable of detecting them across different platforms. By merging five distinct datasets, a comprehensive dataset was created, and various features were added to improve accuracy. The study compared traditional supervised models like Logistic Regression and SVM with deep learning models. Notably, simpler supervised models consistently outperformed deep learning approaches in identifying fake reviews. The findings highlight the importance of choosing the right model and feature engineering approach, with results showing that additional features don’t always improve model performance.
Cite This Paper
Faryad Bigdeli, "Cross-platform Fake Review Detection: A Comparative Analysis of Supervised and Deep Learning Models", International Journal of Information Technology and Computer Science(IJITCS), Vol.17, No.3, pp.52-60, 2025. DOI:10.5815/ijitcs.2025.03.04
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