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.

通过对美国各地实时表达和生活相关数据的大规模分析，探索了社交媒体作为估测肥胖率等人口水平的工具的潜力。