On estimating depressive tendencies of Twitter users utilizing their tweet data

Sho Tsugawa, Yukiko Mogi, Yusuke Kikuchi, Fumio Kishino, Kazuyuki Fujita, Yuichi Itoh, Hiroyuki Ohsaki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationIEEE Virtual Reality Conference 2013, VR 2013 - Proceedings
Publication statusPublished - 2013
Event20th IEEE Virtual Reality Conference, VR 2013 - Orlando, FL, United States
Duration: 2013 Mar 162013 Mar 20

Publication series

NameProceedings - IEEE Virtual Reality


Conference20th IEEE Virtual Reality Conference, VR 2013
Country/TerritoryUnited States
CityOrlando, FL


  • Depression
  • Multiple Regression Analysis
  • Twitter
  • Zung's Self-rating Depression Scale


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