PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA
Open accessAbstract
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 userlevel 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.
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Knowledge acquisition and retention in long-term memory, connected to instructional practices drawing upon what is already stored in long-term memory, can enable the student to achieve greater fluency in the present, free up working memory, provide an enriched base for future learning, and accelerate developmental range and the path to mastery.
“In addition to supporting and enabling critical thinking, knowledge acquisition and retention in long-term memory-connected to instructional practices drawing upon what is already stored in long-term memorycan enable the student to achieve greater fluency in the present, free up working memory, provide an enriched base for future learning, and accelerate developmental range and the path to mastery”
From Malleability, plasticity, and individuality: How children learn and develop in context<sup>1</sup> (2019) · cites this paper as Schwartz et al., 2016
Knowledge acquisition and retention in long-term memory are important for the development of both critical thinking and mastery.
“This balance addresses knowledge acquisition and retention in long-term memory, which are important for the development of both critical thinking and mastery (Schwartz et al., 2016;Willingham, 2009).”
From Malleability, plasticity, and individuality: How children learn and develop in context<sup>1</sup> (2019) · cites this paper as Schwartz et al., 2016
Cites
An unobtrusive behavioral model of "gross national happiness"
Kramer, A. D. I. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753369 · Cited by 247 · see citing works
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Social Networking Sites, Depression, and Anxiety: A Systematic Review
Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016) · JMIR Mental Health · doi:10.2196/mental.5842 · Cited by 763 · see citing works
“While therapeutic writing can provide some benefits in reducing distress and improving well-being [30,31], online writing may serve a different function, with Web-based expressions reflecting the lived experience of the individual (eg, [91,130-132]), rather than providing a therapeutic outlet.”
Self-citationMalleability, plasticity, and individuality: How children learn and develop in context<sup>1</sup>
Cantor, P., Osher, D., Berg, J., Steyer, L., & Rose, T. (2019) · Applied Developmental Science · doi:10.1080/10888691.2017.1398649 · Cited by 328 · see citing works
“the present, free up working memory, provide an enriched base for future learning, and accelerate developmental range and the path to mastery (Schwartz et al., 2016; Willingham, 2009).”
BackgroundInferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments
Saha, K., Chan, L., De Barbaro, K., Abowd, G. D., & De Choudhury, M. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130960 · Cited by 77 · see citing works
Ebert, D. D., Harrer, M., Apolinário-Hagen, J., & Baumeister, H. (2019) · Advances in Experimental Medicine and Biology · doi:10.1007/978-981-32-9721-0_29 · Cited by 76 · see citing works
Foster, M. D. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.11.044 · Cited by 24 · see citing works
- Authors
- SCHWARTZ, H. A., SAP, M., KERN, M. L., EICHSTAEDT, J. C., KAPELNER, A., AGRAWAL, M., BLANCO, E., DZIURZYNSKI, L., PARK, G., STILLWELL, D., KOSINSKI, M., SELIGMAN, M. E. P., & UNGAR, L. H.
- Affiliations
- Stony Brook University (US)
- University of Pennsylvania (US)
- The University of Melbourne (AU)
- Queens College, Department of Mathematics, United Kingdom
- University of North Texas (US)
- University of Cambridge (GB)
- Psychometrics Centre, University of Cambridge, United Kingdom
- ORCID
- Venue
- Biocomputing 2016
- Publisher
- WORLD SCIENTIFIC
- Published
- 2015-11-18
- Volume
- 21
- Pages
- 516-527
- OpenAlex
- W2316993542
- Cited by
- 148
- References
- 1
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 17 · 2023: 13 · 2022: 12 · 2021: 24 · 2020: 17 · 2019: 19 · 2018: 17 · 2017: 11 · 2016: 4 · 2015: 1 · 2013: 1
- FWCI
- 19.37
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Pacific Symposium on Biocomputing (PSB) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Social Psychology › Psychological Well-being and Life Satisfaction
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- personal well-being, life satisfaction, social media language, sentiment analysis, Facebook status updates, cascade model, mental health
- MeSH terms
- Humans, Language, Models, Psychological, Personal Satisfaction, Models, Statistical, Computational Biology, Social Media
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