An unobtrusive behavioral model of "gross national happiness"

By Adam D. I. Kramer. 2010

270 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Health, Social Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

1. Martin E. P. Seligman
2. Margaret L. Kern
3. Isaac L. Johnson
4. David N. Milne
5. Lyle Ungar
6. Helen Christensen
7. Kevan Buckley
8. Paul Resnick

In Health

1. Martin E. P. Seligman
2. Isaac L. Johnson
3. David N. Milne
4. Lyle Ungar
5. Helen Christensen
6. Kevan Buckley
7. Paul Resnick
8. Rafael A. Calvo

In Social Psychology

1. Lyle Ungar
2. Margaret L. Kern
3. Martin E. P. Seligman
4. Johannes C. Eichstaedt
5. H. A. Schwartz
6. Michal Kosinski
7. David J. Stillwell
8. Isaac L. Johnson

Citations in the corpus

• Emotional States vs. Emotional Words in Social Media (2015, methodology): “Moreover, one other study that calculated the correlation between Facebook status updates and a survey scale related to emotional state [19] found similarly weak correlations, ρ = 0.”
• Can Facebook Predict Stock Market Activity (2013, unknown): “15Following the example of Kramer (2010), a status update of ‘Today was kinda good, kinda bad’ would have a positivity score of 0.17 because of the word ‘good’, and a negativity score of 0.17 because of the word ‘bad’.”
• Sentiment strength detection for the social web (2012, background): “Other examples include studies of trends in sentiment over time (Diakopoulos & Shamma, 2010; Kramer, 2010; O’Connor, Balasubramanyan, Routledge, & Smith, 2010; Thelwall, Buckley, & Paltoglou, 2011) or emotion contagion (Gruzd, Doiron, & Mai, 2011), which could potentially track trends in topics, suc”
• Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, background): “are self-descriptive, personal, and have emotional content [7].”
• Structural Causes of Bias in Crowd-derived Geographic Information : Towards a Holistic Understanding (2015, background): “For example, the mapped process of a study seeking to estimate the “Gross National Happiness” (Kramer 2010) of a region using social media is the geographic variation in the expressed sentiment in the region.”
• Divergence of Sentiment and Stock Market Trading (2017, unknown): “As explained by Kramer (2010), a status update of “I am happy today” would receive a positivity rating (p) of 0.25 (only the word “happy” is positive) and a negativity rating (n) of 0.”
• Managing Impressions and Relationships on Facebook (2013, background): “This reveals a moderating factor in context of the “gross national happiness” hypothesis on Facebook (Kramer, 2010), which suggests that the greater ratio of positive to negative emotions in status updates reflects the subjective well-being of Facebook users.”
• It's not that i don't have problems, i'm just not putting them on facebook: challenges and opportunities in using online social networks for health (2011, methodology): “As an example, to support motivation, the degree of positivity of a person’s messages or a group’s conversation could be estimated using automated methods, along the lines of [14].”
• VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text (2014, methodology): “For example, LIWC’s lexicon has been used to extract indications of political sentiment from tweets (Tumasjan, Sprenger, Sandner, & Welpe, 2010), predict the onset of depression in individuals based on text from social media (De Choudhury, Gamon, Counts, & Horvitz, 2013), characterize the emotional ”
• Natural language processing in mental health applications using non-clinical texts† (2017, methodology): “The valence of a post was again identified using a similar approach as in Kramer (2010).”
• Facebook as a toolkit: A uses and gratification approach to unbundling feature use (2011, background): “Research suggests that status updates can support offline student interactions (Barkhuus & Tashiro, 2010), be used to answer questions posed to the social network (Morris, Teevan, & Panovich, 2010), or can be mined to describe geographic ‘‘happiness’’ levels (Kramer, 2010).”
• Participant recruitment and data collection through Facebook: the role of personality factors1 (2016, background): “For example, Goel, Mason, and Watts (2010) have studied attitude similarity among Facebook network members, and Kramer (2010) has used textual analysis of Facebook postings to examine variations in mood between countries.”