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Integration of Inconsistency and Content Interaction with Deep Learning to Detect Fake Reviews

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Integration of Inconsistency and Content Interaction with Deep Learning to Detect Fake Reviews

Sharifpour, Kiana (2024) Integration of Inconsistency and Content Interaction with Deep Learning to Detect Fake Reviews. Masters thesis, Concordia University.

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Abstract

In this study, the challenge of detecting fake reviews in e-commerce is addressed through the application of natural language processing and deep learning techniques. The paper introduces two frameworks designed to identify fraudulent reviews, a critical concern due to their impact on consumer behavior and market dynamics. Central to this study is the exploitation of rating-sentiment inconsistency (RSI), a nuanced textual feature indicative of potential deception, aimed at enhancing the detection of fake content. Using a dataset of Amazon reviews, the paper evaluates two distinct approaches. The first method integrates RSI with word embeddings, specifically GloVe and Word2Vec, yielding an accuracy improvement of 3.07% for GloVe and 0.67% for Word2Vec, demonstrating the effectiveness of BiGRUs in capturing the sequential nature of textual data. The second method incorporates inconsistency features into Doc2Vec representations, achieving a 1.55% increase in accuracy compared to models without this feature. Both methodologies benefit from grid search optimization to fine-tune hyperparameters, enhancing model performance significantly. These combination methods not only underscore the importance of content-based feature integration but also demonstrate the practical application of inconsistency metrics in fake review detection. The observed improvements in model accuracy confirm the effectiveness of the proposed frameworks, providing new insights into enhancing online review system integrity and advancing natural language processing in commercial settings.

Divisions:Concordia University > John Molson School of Business > Supply Chain and Business Technology Management
Item Type:Thesis (Masters)
Authors:Sharifpour, Kiana
Institution:Concordia University
Degree Name:M. Sc.
Program:Business Administration (Supply Chain and Business Technology Management specialization)
Date:6 December 2024
Thesis Supervisor(s):Lahmiri, Salim
Keywords:Fake review detection, review inconsistency, deep learning (DL), word embeddings (WE), natural language processing (NLP), text classification
ID Code:994992
Deposited By: Kiana Sharif pour
Deposited On:17 Jun 2025 17:43
Last Modified:17 Jun 2025 17:43

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