earticle

영어

The escalating importance of online reviews in customers' purchasing behavior has led to growing concerns about the prevalence of manipulated reviews, causing confusion in their decision-making process. However, despite several efforts to develop models for manipulated review detection, a critical aspect that has not been addressed is examining the connection between manipulated review writer personality traits and manipulated review detection. In this study, we examine the role of reviewer personality traits among the factors related to review characteristics in manipulated review detection using deep learning and explainable AI. The purpose of this research is to answer the question are reviewer personality matter in manipulated review detection and what features have an impact on detection. To rich our purpose, we apply deep learning twice: 1) to infer reviewers' personality traits from review text (CNN), 2) to create manipulated review detection models (RNN and CNN). Furthermore, we equip manipulated reviews detection model with one of the XAI techniques - SHAP, that generates visual explanations of the model's decision-making process, thereby enhancing the model's interpretability and transparency. We conduct our experiment by utilizing real-world review data from Yelp.com.