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

arXiv: https://arxiv.org/abs/1302.3299 · HTML: https://mlsanity.com/paper/1302.3299/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2013.

## Built on by (most cited first, 4 of 4)

- [Robust language-based mental health assessments in time and space through social media](https://mlsanity.com/paper/2302.12952/) (2023)
- [Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Content](https://mlsanity.com/paper/2503.04773/) (2025)
- [Measuring daily-life fear perception change: a computational study in the context of COVID-19](https://mlsanity.com/paper/2107.12606/) (2021)
- [An assessment of measuring local levels of homelessness through proxy social media signals](https://mlsanity.com/paper/2305.08978/) (2023)

## Abstract (arXiv)

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.