The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place

PLoS One. 2013 May 29;8(5):e64417. doi: 10.1371/journal.pone.0064417. Print 2013.

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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Cluster Analysis
  • Emotions*
  • Geography
  • Happiness*
  • Health Status
  • Humans
  • Internet / classification
  • Internet / statistics & numerical data*
  • Socioeconomic Factors
  • United States
  • Urban Population / classification
  • Urban Population / statistics & numerical data*

Grant support

The authors are grateful for the computational resources provided by the Vermont Advanced Computing Core which is supported by NASA (NNX 08A096G), and the Vermont Complex Systems Center. CMD and LM were supported by National Science Foundation (NSF) grant DMS-0940271 and PSD was supported by NSF CAREER Award #0846668. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.