TY - GEN
T1 - Towards Assessing Changes in Degree of Depression through Facebook
AU - Schwartz, H. Andrew
AU - Eichstaedt, Johannes
AU - Kern, Margaret L.
AU - Park, Gregory
AU - Sap, Maarten
AU - Stillwell, David
AU - Kosinski, Michal
AU - Ungar, Lyle
N1 - Publisher Copyright:
© 2014 Association for Computational Linguistics
PY - 2014
Y1 - 2014
N2 - Depression is typically diagnosed as being present or absent. However, depression severity is believed to be continuously distributed rather than dichotomous. Severity may vary for a given patient daily and seasonally as a function of many variables ranging from life events to environmental factors. Repeated population-scale assessment of depression through questionnaires is expensive. In this paper we use survey responses and status updates from 28,749 Facebook users to develop a regression model that predicts users' degree of depression based on their Facebook status updates. Our user-level predictive accuracy is modest, significantly outperforming a baseline of average user sentiment. We use our model to estimate user changes in depression across seasons, and find, consistent with literature, users' degree of depression most often increases from summer to winter. We then show the potential to study factors driving individuals' level of depression by looking at its most highly correlated language features.
AB - Depression is typically diagnosed as being present or absent. However, depression severity is believed to be continuously distributed rather than dichotomous. Severity may vary for a given patient daily and seasonally as a function of many variables ranging from life events to environmental factors. Repeated population-scale assessment of depression through questionnaires is expensive. In this paper we use survey responses and status updates from 28,749 Facebook users to develop a regression model that predicts users' degree of depression based on their Facebook status updates. Our user-level predictive accuracy is modest, significantly outperforming a baseline of average user sentiment. We use our model to estimate user changes in depression across seasons, and find, consistent with literature, users' degree of depression most often increases from summer to winter. We then show the potential to study factors driving individuals' level of depression by looking at its most highly correlated language features.
UR - https://www.scopus.com/pages/publications/85130793558
M3 - Conference contribution
AN - SCOPUS:85130793558
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 118
EP - 125
BT - Workshop on Computational Linguistics and Clinical Psychology From Linguistic Signal to Clinical Reality, CLPsych 2014 at the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings
A2 - Resnik, Philip
A2 - Resnik, Rebecca
A2 - Mitchell, Margaret
PB - Association for Computational Linguistics (ACL)
T2 - 2014 Workshop on Computational Linguistics and Clinical Psychology From Linguistic Signal to Clinical Reality, CLPsych 2014 at the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
Y2 - 27 June 2014
ER -