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.
翻译:通过使用26亿条地理定位的社交媒体帖子(2014-2022年)以及一个经过微调的生成式语言模型,我们构建了美国县级层面的生活满意度与幸福感指标。我们记录了一个明显的城乡悖论:农村县表现出更高的生活满意度,而城市县则展现出更强的幸福感。我们通过将这两者视为主观幸福感的两个不同层面——评估性层面与享乐性层面——来调和这一矛盾,并证明它们各自在地理空间、政治倾向和时间维度上呈现出不同的映射关系。共和党倾向地区在评估性层面显得更为满足,但幸福感的党派差距在主要大都市区之外基本趋于平缓,这表明政治效应具有情境依赖性。时间性冲击主导了享乐性层面:2020-2022年期间幸福感急剧下降,而生活满意度的变动则相对温和。这些模式在逻辑回归和普通最小二乘法(OLS)设定中均保持稳健,并与幸福感理论相一致。若将结果解释为社交媒体帖子总体的关联性表征,研究表明:通过区分所表达的幸福类型,基于大规模语言分析的指标能够化解关于城乡分化的矛盾发现,为传统调查提供了一种透明、可复现的补充方法。