On estimating depressive tendencies of Twitter users utilizing their tweet data

In this paper, we investigate the effectiveness of the records of user's activities in Twitter, which is a popular microblogging site, for estimating his/her depressive tendency. We construct multiple regression model to estimate user's depressive tendency from the frequencies of words used by the user. We perform experiments to estimate participants' depressive tendencies using the constructed regression model. Our experimental results show that there exists medium positive correlation (correlation coefficient r ≃ 0.45) between the Zung's Self-rating Depression Scale, which is a popular measure for estimating depressive tendency, and estimated score obtained from the regression model.