An unobtrusive behavioral model of "gross national happiness"
Abstract
I analyze the use of emotion words for approximately 100 million Facebook users since September of 2007. "Gross national happiness" is operationalized as a standardized difference between the use of positive and negative words, aggregated across days, and present a graph of this metric. I begin to validate this metric by showing that positive and negative word use in status updates covaries with self-reported satisfaction with life (convergent validity), and also note that the graph shows peaks and valleys on days that are culturally and emotionally significant (face validity). I discuss the development and computation of this metric, argue that this metric and graph serves as a representation of the overall emotional health of the nation, and discuss the importance of tracking such metrics.
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Facebook status updates are self-descriptive, personal, and have emotional content.
“as “status” updates are self-descriptive, personal, and have emotional content [7].”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [7]
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Boutet, A., Kim, H., & Yoneki, E. (2012) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2012.32 · Cited by 28 · see citing works
An Analysis of Online Twitter Sentiment Surrounding the European Refugee Crisis
Pope, D., & Griffith, J. (2016) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0006051902990306 · Cited by 28 · see citing works
Oman, S. (2021) · New directions in cultural policy research · doi:10.1007/978-3-030-72937-0 · Cited by 27 · see citing works
An exploration of the use of social media by surgical colleges
Ralston, M. R., O’Neill, S., Wigmore, S. J., & Harrison, E. M. (2014) · International Journal of Surgery · doi:10.1016/j.ijsu.2014.10.035 · Cited by 27 · see citing works
Detecting sociosemantic communities by applying social network analysis in tweets
Abascal-Mena, R., Lema, R., & Sèdes, F. (2015) · Social Network Analysis and Mining · doi:10.1007/s13278-015-0280-2 · Cited by 27 · see citing works
The reflection of offline activities on users’ online social behavior: An observational study
Falavarjani, S. A. M., Zarrinkalam, F., Jovanović, J., Bagheri, E., & Ghorbani, A. A. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102070 · Cited by 27 · see citing works
Gallegos, L., Lerman, K., Huang, A., & García, D. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872518.2890084 · Cited by 25 · see citing works
- Authors
- Kramer, A. D. I.
- Affiliations
- University of Oregon (US)
- Corresponding author
- Kramer, Adam D.I.
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2010-04-10
- Pages
- 287-290
- OpenAlex
- W2150819813
- Cited by
- 247
- References
- 17
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 5 · 2023: 8 · 2022: 13 · 2021: 39 · 2020: 12 · 2019: 12 · 2018: 13 · 2017: 10 · 2016: 16 · 2015: 29 · 2014: 26 · 2013: 28 · 2012: 14
- FWCI
- 42.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Psychological Well-being and Life Satisfaction
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- Gross National Happiness, emotional words, Facebook status updates, life satisfaction, self-reported well-being, convergent validity, face validity, social media sentiment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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