Published on

Automated Detection of Fake Reviews on E-commerce Platforms

Authors
  • avatar
    Name
    Project Mart
    Twitter

Introduction

The proliferation of fake reviews on e-commerce platforms poses a significant challenge to maintaining the credibility and reliability of online marketplaces. These fraudulent reviews can mislead consumers and damage the reputation of businesses. This project proposal presents a system designed to automatically detect fake reviews using machine learning techniques, drawing inspiration from recent advancements in the field.

Background

Recent studies have highlighted the effectiveness of machine learning and artificial intelligence in identifying fraudulent reviews. Techniques such as feature-based, behavior-based, and deep learning approaches have been employed to tackle this issue. These methods analyze various aspects of reviews, such as linguistic features, reviewer behavior, and sentiment analysis, to distinguish between genuine and fake reviews.

Project Objective

The primary objective of this project is to develop an automated system capable of accurately detecting fake reviews on e-commerce platforms. The system aims to enhance existing methods by integrating multiple machine learning algorithms and leveraging comprehensive datasets.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize publicly available datasets such as the Amazon Reviews dataset for training and evaluation.
  • Feature Extraction: Extract relevant features including linguistic characteristics, sentiment scores, and reviewer behavior patterns.

2. Model Architecture

  • Machine Learning Models: Implement a combination of models such as Support Vector Machines (SVM), XGBoost, and neural networks to classify reviews.
  • Ensemble Techniques: Use ensemble methods to improve classification accuracy by combining the predictions of multiple models.

3. Training and Evaluation

  • Training: Employ techniques such as cross-validation to train models on balanced and imbalanced datasets.
  • Evaluation Metrics: Measure performance using metrics like accuracy, precision, recall, and F1-score.

Expected Outcomes

The proposed system is expected to significantly improve the detection of fake reviews compared to traditional methods. By utilizing advanced machine learning techniques and ensemble methods, the system should effectively identify fraudulent reviews across various product categories.

Conclusion

This project aims to contribute to the field of fake review detection by developing a robust system capable of accurately identifying fraudulent reviews on e-commerce platforms. The integration of diverse machine learning models is anticipated to enhance detection capabilities and support the integrity of online marketplaces.

For further details on related research, please refer to the paper "Creating and Detecting Fake Reviews of Online Products," available at https://www.sciencedirect.com/science/article/pii/S0969698921003374.

The dataset used for this project can be accessed at https://osf.io/tyue9/.

Buy Project