A framework for Gaming Disorder Detection based on social media data using Large Language Model labeling

Internet Gaming Disorder (IGD) is officially recognized as a mental health condition by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), a standard for diagnosing mental health conditions. However, current diagnostic methods rely on subjective self-reports, which lack objectivity and consistency. This study proposes the Gaming Disorder Detection (GDD) framework, using data from Reddit’s StopGaming subreddit to provide an objective and scalable IGD detection approach. Sentences extracted from Reddit posts were labeled using a Large Language Model (LLM) based on DSM-5 and IGD-20 criteria, reducing bias and improving diagnostic consistency. The labeled dataset was then analyzed with a Graph Neural Network (GNN) to predict IGD patterns in new data. By combining LLM-based labeling and GNN-based analysis, the study shows the potential of integrating DSM-5 and IGD-20 criteria into a data-driven framework for IGD detection, offering a novel tool for clinicians and experts to objectively and systematically evaluate gaming disorder.