PLoS ONE (Jan 2017)

Building a profile of subjective well-being for social media users.

  • Lushi Chen,
  • Tao Gong,
  • Michal Kosinski,
  • David Stillwell,
  • Robert L Davidson

DOI
https://doi.org/10.1371/journal.pone.0187278
Journal volume & issue
Vol. 12, no. 11
p. e0187278

Abstract

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Subjective well-being includes 'affect' and 'satisfaction with life' (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users' affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language.