# Two Americas of Well-Being: Divergent Rural-Urban Patterns of Life Satisfaction and Happiness from 2.6 B Social Media Posts

arXiv: https://arxiv.org/abs/2511.10542 · HTML: https://mlsanity.com/paper/2511.10542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 52 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research](https://mlsanity.com/paper/2411.00890/) (2024)
- [Geo-located Twitter as the proxy for global mobility patterns](https://mlsanity.com/paper/1311.0680/) (2013)
- [The Human Flourishing Geographic Index: A County-Level Dataset for the United States, 2013--2023](https://mlsanity.com/paper/2511.03915/) (2025)

## Abstract (arXiv)

Using 2.6 billion geolocated social-media posts (2014-2022) and a fine-tuned generative language model, we construct county-level indicators of life satisfaction and happiness for the United States. We document an apparent rural-urban paradox: rural counties express higher life satisfaction while urban counties exhibit greater happiness. We reconcile this by treating the two as distinct layers of subjective well-being, evaluative vs. hedonic, showing that each maps differently onto place, politics, and time. Republican-leaning areas appear more satisfied in evaluative terms, but partisan gaps in happiness largely flatten outside major metros, indicating context-dependent political effects. Temporal shocks dominate the hedonic layer: happiness falls sharply during 2020-2022, whereas life satisfaction moves more modestly. These patterns are robust across logistic and OLS specifications and align with well-being theory. Interpreted as associations for the population of social-media posts, the results show that large-scale, language-based indicators can resolve conflicting findings about the rural-urban divide by distinguishing the type of well-being expressed, offering a transparent, reproducible complement to traditional surveys.