AI-based analysis of social network data provides an effective, non-invasive method for assessing college students' mental health status, offering significant potential for improving mental health monitoring and prevention strategies in educational settings.
Abstract
This study aims to develop and validate an artificial intelligence (AI) algorithm for assessing college students' mental health status by analyzing their social network data, with the goal of establishing an effective early warning system for psychological issues.
The research involved 1,500 college students from three universities. Social network data including posting frequency, content themes, interaction patterns, and behavioral characteristics were collected over six months. Machine learning algorithms, particularly natural language processing (NLP) and deep learning models, were employed to analyze the data. The participants also completed standardized psychological assessments (SCL-90, PHQ-9, GAD-7) to validate the AI algorithm's accuracy.
The AI model achieved an overall accuracy of 86.4% in identifying students with potential mental health issues. Specific indicators such as changes in posting frequency (p < 0.01), negative emotional vocabulary usage (p < 0.001), and social withdrawal patterns (p < 0.05) were significant predictors. The algorithm demonstrated strong performance in detecting depression (AUC = 0.89) and anxiety (AUC = 0.87) symptoms, with early detection capability averaging 4-6 weeks before clinical manifestation.
AI-based analysis of social network data provides an effective, non-invasive method for assessing college students' mental health status. This approach enables timely intervention and support, offering significant potential for improving mental health monitoring and prevention strategies in educational settings.
This work was supported by the NIT Science and Technology Innovation Fund under Grants No.XYJS202501; the National Science Foundation of China under Grant No. 62172229,62471258; The Natural Science Fund of Jiangsu Province under Grants No. BK20211295; Jiangsu Provincial Natural Science Research General Project for Colleges and Universities in 2024 (24KJB520034); Key Cultivation Project for Scientific Research of Nantong Institute of Technology (XKYPY202304).
Yingya Zhi, School of Information Engineering, Nantong Institute of Technology, Nantong, 226000, China.
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