Predicting mental health status based on web usage behavior

Authors:
Tingshao Zhu;Ang Li;Yue Ning;Zengda Guan
Affiliations:
School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing, China;School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing, China;School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing, China;School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing, China
Venue:
AMT'11 Proceedings of the 7th international conference on Active media technology
Year:
2011

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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.