Measuring Human Capital with Social Media Data and Machine Learning

Martina Jakob () and Sebastian Heinrich ()

No 46, University of Bern Social Sciences Working Papers from University of Bern, Department of Social Sciences

Abstract: In response to persistent gaps in the availability of survey data, a new strand of research leverages alternative data sources through machine learning to track global development. While previous applications have been successful at predicting outcomes such as wealth, poverty or population density, we show that educational outcomes can be accurately estimated using geo-coded Twitter data and machine learning. Based on various input features, including user and tweet characteristics, topics, spelling mistakes, and network indicators, we can account for ~70 percent of the variation in educational attainment in Mexican municipalities and US counties.

Keywords: machine learning; social media data; education; human capital; indicators; natural language processing (search for similar items in EconPapers)
JEL-codes: C53 C80 I21 I25 O11 O15 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2023-05-05
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (1)

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