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Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study.

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Social media language can predict psychological well-being (PWB). This study developed a reliable model using linguistic features from posts to assess PWB, showing potential for large-scale mental health monitoring.

Keywords:
domain knowledgeground truthlexiconlinguisticmachine learningmental healthmental well beingmental wellbeingmodelpredictpsychological well-beingsocial media

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Area of Science:

  • Computational Social Science
  • Psychological Measurement
  • Digital Health

Background:

  • Assessing psychological well-being (PWB) at scale is challenging.
  • Social media offers a nonintrusive method for monitoring user PWB.
  • This study explores social media language as a PWB predictor.

Purpose of the Study:

  • To investigate the predictive accuracy of social media language for psychological well-being.
  • To develop and validate a model for estimating PWB from online linguistic expressions.
  • To assess the model's reliability and validity using established psychometric criteria.

Main Methods:

  • Recruited 1427 participants, assessing their PWB across 6 dimensions.
  • Collected social media posts and extracted linguistic features using 6 psychological lexicons.
  • Developed a multiobjective prediction model and evaluated its discriminant, convergent, and criterion validity, alongside split-half reliability.

Main Results:

  • The linguistic prediction model demonstrated good criterion validity, with correlation coefficients between predicted and actual PWB scores ranging from 0.49 to 0.54 (P<.001).
  • The model showed excellent convergent validity but less satisfactory discriminant validity.
  • Good split-half reliability was observed across all PWB dimensions (0.65–0.85; P<.001).

Conclusions:

  • The study confirms the availability and stability of a linguistic prediction model for PWB.
  • Social media language effectively predicts psychological well-being, validating its use in nonprofessional settings.
  • Findings support the use of social media data for large-scale mental health assessment and self-testing.