#GAMEADDICTED: A Machine Learning Framework for Digital Game Addiction Detection and Early Intervention

This study addresses the growing concern of digital game addiction (DGA) by developing an information system for detection and early intervention, to predict an individual’s propensity toward DGA. The study employed machine learning models to process unstructured data collected from online game platforms between May 1, 2023, and June 1, 2023. A total of 660 comments were automatically extracted. Following an expert review process, 245 ambiguous or inconsistent entries were removed, resulting in a final dataset of 415 textual comments for model training. The comments were manually annotated into two categories: addiction-related (1) and non-addiction-related (0) to ensure data consistency and reliability. Data augmentation techniques, such as BERT-based augmentation, deep translation, and synonym replacement, were applied to increase dataset diversity and enhance the model’s generalization capability. After extensive preprocessing, we trained and evaluated six machine learning models: XGBoost, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, and Random Forests. XGBoost consistently outperformed all other models, achieving the highest accuracy of 93.06% on the binary-labeled dataset and 92.49% on the expanded dataset. Random Forests and Logistic Regression also exhibited strong classification performance, making them viable alternatives depending on the requirements. In contrast, SVM and Decision Trees showed lower predictive reliability, indicating potential challenges in classifying text-based addiction indicators effectively. As a practical outcome, we developed an interface using the best-performing model, capable of performing preliminary diagnoses based on user-provided sentences. This innovative approach introduces a new dimension in addiction studies by presenting a practical tool for early detection and intervention in DGA.