Detecting Deception: Employing Deep Neural Networks for Fraudulent Review Detection on Amazon

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Thilini Jayasinghe, Sachith Dassanayaka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5214171/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the era of e-commerce dominance, an increase in fake reviews on online shopping platforms compromises the integrity of consumer feedback systems. This study focuses on Amazon, a leading e-commerce platform in the United States, where fake reviews have become a significant concern. Given the limited availability of authentic datasets for analysis, we propose a novel methodology to differentiate between genuine and fraudulent reviews across verified and non-verified purchases. Our approach utilizes the bootstrap distribution of cosine similarity values, providing a robust statistical foundation for review classification. We present a comprehensive framework integrating Convolutional Neural Networks with word embedding and emotion-mining techniques through Natural Language Processing. This multifaceted approach enhances detection accuracy and offers insights into the linguistic and emotional markers of fake reviews. Our method demonstrates exceptional performance, achieving an accuracy rate of over 96% in distinguishing fake reviews from user reviews. This study contributes to the growing research on online review authenticity and offers practical implications for e-commerce platforms, regulatory bodies, and consumers. This research aims to foster trust in online marketplaces and protect consumers from misleading information by providing a powerful tool for fake review detection. Convolutional Neural Network Natural Language Processing Bootstrap Cosine Similarity Amazon fake reviews Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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