The value of social media language for the assessment of wellbeing: A systematic review and meta-analysis

ABSTRACT

Wellbeing is predominantly measured through self-reports, which is time-consuming and costly. It can also be measured by automatically analysing language expressed on social media platforms, through social media text mining (SMTM). We present a systematic review based on 45 studies, and a meta-analysis of 32 convergent validities from 18 studies reporting correlations between SMTM and survey-based wellbeing. We find that (1) studies were mostly limited to the English language, (2) Twitter was predominantly used for data collection, (3) word-level and data-driven methods were similarly prominent, and (4) life satisfaction was the most common outcome studied. We found that SMTM-based estimates of wellbeing correlated with survey-reported scores across studies at a meta-analytic average of r = .33(95% CI [.25, .40]) for individual-level assessments of wellbeing, and at r = .54(95% CI [.37, .67]) for regional measures of well-being. We provide recommendations for future SMTM wellbeing studies.

KEYWORDS:

• Wellbeing
• well-being
• social media
• text mining
• validity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, S. Sametoğlu, upon reasonable request.

Additional information

Funding

This work is supported by a European Research Council Consolidator Grant (WELL-BEING 771057, PI Bartels)