Date of Award
6-1-2020
Document Type
Thesis (Undergraduate)
Department or Program
Department of Computer Science
First Advisor
Soroush Vosoughi
Abstract
This paper proposes a novel framework of predicting future technological change. Using abstracts of academic publications available in the Microsoft Academic graph, co-occurrence matrices are generated to indicate how often occupation and technological terms are referenced together. This matrices are used in linear regression models to predict future co-occurrence of occupations and technologies with a relatively high degree of accuracy as measured through the mean squared error of the models. While this work is unable to link the co-occurrences found in academic publications to automation in the labor force due to a dearth of automation data, future work conducted when such data is available could apply a similar approach with the aim of predicting automation from trends in academic research and publications.
Recommended Citation
Doty, Elena A., "Mining Academic Publications to Predict Automation" (2020). Dartmouth College Undergraduate Theses. 158.
https://digitalcommons.dartmouth.edu/senior_theses/158
Comments
Originally posted in the Dartmouth College Computer Science Technical Report Series, number TR2020-892.