Measuring Human Capital with Social Media Data and Machine Learning
Official URL
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.
Date Issued
2023
Publication Type
Working Paper
Language(s)
en
Author(s)
Heinrich, Sebastian |
Additional Credits
Publisher
University of Bern, Department of Social Sciences
Access(Rights)
Unknown