Predicting psychological features based on web behavioral data: Mental health status and subjective well-being

Abstract

To improve social harmony and stability, it is essential to acquire public psychological profiles in real time. However, traditional methods of psychological assessment have failed to meet the requirement. This paper proposes a novel method for predicting psychological features based on web behavioral data. Using a microblogging platform, we built predicting models for identifying mental health status and subjective well-being. The correlation between the predicted and actual values of depression can reach 0.41, and the highest correlation on subjective well-being is 0.6. The results indicate an effective overall performance of the established predicting models. This study demonstrates that, based on web data analysis, it is possible to efficiently predict psychological features and to update the predicted outcomes in real time.