Predicting well-being with geo-referenced data collected from social media platforms

This paper proposes a novel method that leverages georeferenced social media data, together with human assessments of particular words, to estimate population well-being across the U.S. territory. We specifically attempt to learn linear regression models that, by leveraging on simple features that essentially correspond to word counts in lexicons of emotionally-charged words, are capable of approximating a composite well-being index built through traditional surveying methods. Experiments with a large Twitter dataset collected within the year of 2012 attest for the feasibility of the proposed approach (i.e., we approximate the Gallup-Healthways composite well-being index with a mean absolute error of 0.91), and we then produced choropleth maps, either at a state- or at a county-level of detail, that show how well-being varies across the continental U.S. territory.