There is increasing interest in the potential of artificial intelligence and Big Data (e.g., generated
via social media) to help understand economic outcomes and processes. But can artificial
intelligence models, solely based on publicly available Big Data (e.g., language patterns left on
social media), reliably identify geographical differences in entrepreneurial personality/culture
that are associated with entrepreneurial activity? Using a machine learning model processing 1.5
billion tweets by 5.25 million users, we estimate the Big Five personality traits and an
entrepreneurial personality profile for 1,772 U.S. counties. We find that these Twitter-based
personality estimates show substantial relationships to county-level entrepreneurship activity,
accounting for 24% (entrepreneurial personality profile) and 32% (all Big Five trait as separate
predictors in one model) of the variance in local entrepreneurship and are robust to the
introduction in the model of conventional economic factors that affect entrepreneurship. We
conclude that artificial intelligence methods, analysing publically available social media data, are
indeed able to detect entrepreneurial patterns, by measuring territorial differences in
entrepreneurial personality/culture that are valid markers of actual entrepreneurial behaviour.
More importantly, such social media datasets and artificial intelligence methods are able to
deliver similar (or even better) results than studies based on millions of personality tests (selfreport
studies). Our findings have a wide range of implications for research and practice
concerned with entrepreneurial regions and eco-systems, and regional economic outcomes
interacting with local culture.
