Date of Award


Document Type

Thesis (Undergraduate)

Department or Program

Department of Computer Science

First Advisor

Soroush Vosoughi


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.


Originally posted in the Dartmouth College Computer Science Technical Report Series, number TR2020-892.