Measuring Human Capital with Social Media Data and Machine Learning

Jakob, Martina Saskia; Heinrich, Sebastian (2023). Measuring Human Capital with Social Media Data and Machine Learning (University of Bern Social Sciences Working Papers 46). Bern: University of Bern, Department of Social Sciences

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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.

Item Type:

Working Paper

Division/Institute:

03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Sociology

UniBE Contributor:

Jakob, Martina Saskia

Subjects:

300 Social sciences, sociology & anthropology

Series:

University of Bern Social Sciences Working Papers

Publisher:

University of Bern, Department of Social Sciences

Language:

English

Submitter:

Ben Jann

Date Deposited:

05 May 2023 17:27

Last Modified:

05 Aug 2023 20:01

JEL Classification:

C53, C80, O11, O15, I21, I25

BORIS DOI:

10.48350/182366

URI:

https://boris.unibe.ch/id/eprint/182366

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