The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place

TL;DR

This study analyzes Twitter data and demographic information across the US to explore how social media expressions relate to happiness, health, and urban characteristics, revealing potential for real-time population health monitoring.

Contribution

It combines large-scale Twitter data with demographic surveys to create new taxonomies and correlations between language use, happiness, and urban health metrics.

Findings

Generated taxonomies of US states and cities based on language use

Estimated happiness levels of states and cities

Linked word choice and message length to urban health characteristics

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 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.