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 |
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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 |