Building a profile of subjective well-being for social media users
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Chen, L., Gong, T., Koskinski, M., Stillwell, D., & Davidson, R. (2017). Building a profile of subjective well-being for social media users. PLoS ONE, 12 (11. e0187278)https://doi.org/10.1371/journal.pone.0187278
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.
Gong acknowledges support by the US National Institutes of Health Grant (HD-071988). The study is also supported in part by the MOE Project of the Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies. However, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
External DOI: https://doi.org/10.1371/journal.pone.0187278
This record's URL: https://www.repository.cam.ac.uk/handle/1810/271861
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/
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