Abstract
OBJECTIVES: The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. METHODS: Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis. RESULTS: Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption. CONCLUSIONS: Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.