Predicting individual well-being through the language of social media

H. Andrew Schwartz, Maarten Sap, Margaret L. Kern, Johannes C. Eichstaedt, Adam Kapelner, Megha Agrawal, Eduardo Blanco, Lukasz Dziurzynski, Gregory Park, David Stillwell, Michal Kosinski, Martin E.P. Seligman, Lyle H. Ungar

Research output: Contribution to journalConference articlepeer-review

63 Scopus citations

Abstract

We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded model, using both message-level predictions and user-level features, performs best and outperforms popular lexicon-based happiness models. Finally, we suggest that analyses of language go beyond prediction by identifying the language that characterizes well-being.

Original languageEnglish (US)
Pages (from-to)516-527
Number of pages12
JournalPacific Symposium on Biocomputing
DOIs
StatePublished - 2016
Externally publishedYes
Event21st Pacific Symposium on Biocomputing, PSB 2016 - Big Island, United States
Duration: Jan 4 2016Jan 8 2016

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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