| dc.description.abstract | With the rise of the internet, e-commerce and social commerce have flourished. There is a significant number of fraudulent transactions. During COVID-19, many people became accustomed to online e-commerce, making it crucial to accurately assess merchants before engaging in transactions. Some fraudulent sellers use social influence to deceive potential customers by posting fake reviews to sway their choices. This research employs social network analysis and graph neural networks (GNNs) to detect fraudulent reviews. A social network graph is created to predict fraudulent transactions by analyzing the relationships among reviewers. The study uses review timing, star ratings, and reviewer information to construct the social network graph, applying multiple labeling methods to categorize e-commerce fraud types. The datasets used include reviews from Amazon and Yelp, totaling 2,338,637 and 67,395 reviews, respectively. The GNN model achieved 87% and 73% accuracy using two different labeling methods. By leveraging features from the Yelp dataset, the study evaluates the impact of social influence on social commerce, demonstrating that social influence affects 89% of social commerce decisions. In an era of rapid social media and internet growth, this research confirms the significant impact of social influence on social commerce. The fraudulent review detection system effectively identifies fake reviews, contributing to a safer e-commerce environment and reducing fraudulent transactions. | en_US |