The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place
[PLoS ONE, 8(5): e64417] [arxiv link]

Lewis Mitchell*, Morgan R. Frank, Kameron D. Harris, Peter Sheridan Dodds, and Christopher M. Danforth


We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated over the course of several recent years on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-level measures such as obesity rates.

Online Appendices

Appendix A (see paper)
Appendix B - Word shift plots for all states
Appendix C - Happiness maps and word shift plots for all cities
Appendix D - Correlations of happiness and word use with all demographic attributes
Appendix E - Correlating happiness and word use with obesity
Appendix F - Daily updating happiness map of the lower 48 United States

Blog posts about this work:

Part 1: Where is the happiest city in the USA?
Part 2: What makes a city happy?
Part 3: The Twitter Diet

Official Twitter companion, @geographyofhapp:

Tweets by @geographyofhapp
Changes to these Appendices

*Corresponding author: email

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