Detecting Non-Suicidal Self-Injury Risk Group on SNS: Application of Machine Learning and Text Mining

This study was intended to rapidly and accurately detect non-suicidal self-injury(NSSI) risk groups on social networking services (SNS) using machine learning and text mining methods. Data were collected from the textbased SNS platform, X, using the keyword “self-injury” over a one-year period. Following preprocessing and cross-validation by three researchers, 18,758 posts were labeled (1: NSSI risk posts, 0: non-risk posts). Users were placed in the NSSI risk group if they had at least one NSSI risk post. A transformer-embedding method for natural language processing was then used, followed by supervised machine learning algorithms to validate the detection of NSSI risk and non-risk groups.After the collected SNS posts were labeled, 6,613 posts (35.25%) were identified as NSSI risk posts, while 12,147 posts (64.76%) were identified as non-risk posts. Users who posted risk-related content even once were placed in the at-risk group. Consequently, among the 10,524 participants included in the study, 1,733 users (16.47%) were identified as belonging to the NSSI risk group, and 8,791 users (83.53%) as belonging to the non-risk group. Furthermore, the development of a supervised machine learning model to detect NSSI risk groups on SNS demonstrated excellent performance, achieving an accuracy of approximately 85%. This result highlights the high precision of the model in distinguishing between NSSI risk and non-risk groups on SNS.These results indicate the NSSI risk groups on SNS can be efficiently and accurately identified using machine learning, thereby providing a cost-effective and timely approach for early detection. This study advances previous research by expanding the analytical focus from individual posts to user-level accounts. This approach provides a more comprehensive understanding of self-harm risk groups, which may be challenging to identify at the postlevel, and enhances the feasibility of connecting insights to practical interventions.