Abstract
To build a predicting model for mental health status based on Web Usage Behavior, we collect data from 571 first-year graduate students using our own Internet Usage Behavior Check-List (IUBCL) and Psychological Health Inventory (PHI). We build six logistic regression models, in which Web usage behavior features are as independent variables while mental health status as dependent ones. We find that the accuracy is about 72.9% − 83.1%, which demonstrates it is applicable and feasible to identify each individual’s mental health status by analyzing his/her Web usage behaviors.
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Zhu, T., Li, A., Ning, Y., Guan, Z. (2011). Predicting Mental Health Status Based on Web Usage Behavior. In: Zhong, N., Callaghan, V., Ghorbani, A.A., Hu, B. (eds) Active Media Technology. AMT 2011. Lecture Notes in Computer Science, vol 6890. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23620-4_22
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DOI: https://doi.org/10.1007/978-3-642-23620-4_22
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