Differential Privacy-Enabled TextCNN for MOOCs Fake Review Detection

Abstract

The rapid development and widespread adoption of massive open online courses (MOOCs) have indeed had a significant impact on China’s education curriculum. However, the problem of fake reviews and ratings on the platform has seriously affected the authenticity of course evaluations and user trust, requiring effective anomaly detection techniques for screening. The textual characteristics of MOOCs reviews, such as varying lengths and diverse emotional tendencies, have brought complexity to text analysis. Traditional rule-based analysis methods are often inadequate in dealing with such unstructured data. We propose a Differential Privacy-Enabled Text Convolutional Neural Network (DP-TextCNN) framework, aiming to achieve high-precision identification of outliers in MOOCs course reviews and ratings while protecting user privacy. This framework leverages the advantages of Convolutional Neural Networks (CNN) in text feature extraction and combines differential privacy techniques. It balances data privacy protection with model performance by introducing controlled random noise during the data preprocessing stage. By embedding differential privacy into the model training process, we ensure the privacy security of the framework when handling sensitive data, while maintaining a high recognition accuracy. Experimental results indicate that the DP-TextCNN framework achieves an exceptional accuracy of over 95% in identifying fake reviews on the dataset, this outcome not only verifies the applicability of differential privacy techniques in TextCNN but also underscores its potential in handling sensitive educational data. Additionally, we analyze the specific impact of differential privacy parameters on framework performance, offering theoretical support and empirical analysis to strike an optimal balance between privacy protection and framework efficiency.