International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Systematic Literature Review on Fake Review Detection Using Machine Learning Techniques

Author(s) Mr. Ruman Maharjan, Dr.Bhoj Raj
Country Nepal
Abstract The rapid expansion of e-commerce has intensified consumer dependence on online reviews for making informed purchasing decisions. However, the widespread occurrence of fabricated reviews created to artificially inflate or deflate product and service ratings has emerged as a significant threat to digital trust and market fairness. This systematic literature review synthesizes contemporary research on fake review detection utilizing machine learning and deep learning methodologies. Through rigorous analysis of 31 peer-reviewed publications spanning 2019–2024, this study examines feature engineering approaches, algorithmic frameworks, validation protocols, and performance benchmarks. The findings reveal that hybrid models integrating behavioral and linguistic characteristics consistently outperform unimodal approaches. Ensemble learning techniques and deep neural architectures demonstrate superior detection capabilities compared to conventional classifiers, with Support Vector Machines and ensemble methods such as XGBoost and AdaBoost exhibiting robust performance across heterogeneous datasets. However, these advancements, persistent challenges including concept drift, data imbalance, domain generalization limitations, and adversarial vulnerabilities remain unresolved. This review outlines critical research gaps and proposes actionable directions for future inquiry, emphasizing the imperative for real-time detection architectures, cross-domain adaptability, and resilient countermeasures against evolving deceptive strategies.
Keywords Fake Review Detection, Machine Learning, Deep Learning, Natural Language Processing, Feature Engineering, Opinion Spam Detection
Field Computer
Published In Volume 8, Issue 4, July-August 2026
Published On 2026-07-16
DOI https://doi.org/10.36948/ijfmr.2026.v08i04.83835
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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