An Important Digital Commons: The Predictive Power of Social Media Data to Estimate Population Well-Being in the US and UK

Measuring the subjective well-being of societies is important in its own right and as a determinant of health outcomes. Traditionally, self-report surveys such as Gallup’s daily poll have tracked well-being, but they are expensive and provide limited coverage of communities. Over the past decade, research has shown that social media (e.g., Twitter/X) offers a cost-effective alternative. Yet, through ownership and policy changes, access to these “digital commons” is increasingly restricted. What are we losing without access for researchers? We argue that well-being estimates derived from geolocated, demographically post-stratified Twitter language provided the most valid indicators of US population well-being available. We compare estimates for 1,208 US counties (~89% of the population) derived from 1.53 billion posts by 5.25 million users to Gallup estimates from 1.9 million survey responses. Twitter-based estimates were more predictive of external health and economic indicators and met a wide variety of validity criteria, including a convergent correlation of r = .70 with Gallup, high test-retest stability, linguistic face validity, and generalizability across US cities, states, and the UK. We further show that Gallup ground truth data is not required by building an independent language model on n = 9,419 Twitter users, which produced valid estimates that again outperformed Gallup in predicting external variables. These findings establish that social media can capture population well-being more robustly and with better coverage than even the largest survey efforts, and that ensuring researcher access to these data is essential for understanding and improving societies.