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 contribution

9 Citations (Scopus)

Abstract

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
DOIs
Publication statusPublished - 2013 Oct 7
Externally publishedYes
Event20th IEEE Virtual Reality Conference, VR 2013 - Orlando, FL, United States
Duration: 2013 Mar 162013 Mar 20

Publication series

NameProceedings - IEEE Virtual Reality

Other

Other20th IEEE Virtual Reality Conference, VR 2013
CountryUnited States
CityOrlando, FL
Period13/3/1613/3/20

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'On estimating depressive tendencies of Twitter users utilizing their tweet data'. Together they form a unique fingerprint.

  • Cite this

    Tsugawa, S., Mogi, Y., Kikuchi, Y., Kishino, F., Fujita, K., Itoh, Y., & Ohsaki, H. (2013). On estimating depressive tendencies of Twitter users utilizing their tweet data. In IEEE Virtual Reality Conference 2013, VR 2013 - Proceedings [6549431] (Proceedings - IEEE Virtual Reality). https://doi.org/10.1109/VR.2013.6549431