title: The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place.
abstract: 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 in 2011 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-scale measures such as obesity rates.
authors.affiliation: Computational Story Lab, Department of Mathematics and Statistics, Vermont Complex Systems Center, The University of Vermont, Burlington, Vermont, USA. lewis.mitchell@uvm.edu
authors.forename: Lewis
authors.initials: L
authors.lastname: Mitchell
authors.forename: Morgan R
authors.initials: MR
authors.lastname: Frank
authors.forename: Kameron Decker
authors.initials: KD
authors.lastname: Harris
authors.forename: Peter Sheridan
authors.initials: PS
authors.lastname: Dodds
authors.forename: Christopher M
authors.initials: CM
authors.lastname: Danforth
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004644:Emotions; D005843:Geography; D006240:Happiness; D006304:Health Status; D006801:Humans; D020407:Internet / Q000145:classification / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D014481:United States; D014505:Urban Population / Q000145:classification / Q000706:statistics & numerical data*