Advancement of Data Science in Economics Studies: A bibliometric analysis of the use of machine learning

Abstract

This paper aims to explore the results of a mapping of academic research focused on Data Science in economics studies, with a specific focus on the use of machine learning techniques. It seeks to identify how Data Science intercepts the economy and its various axes of action. This is a descriptive research, in that bibliometric analyzes were performed using the Bibliometrix package of the R software, extracted from the Scopus database (SCP), taking the period of 2013 as a time frame to 2023. To carry out the analyzes, the descriptors “machine” and “learning” were used, 1,415 works were found. Based on the results, it was possible to confirm the strong evolution in the publication of works that deal with machine learning applied to economics or economic factors, as well as the mapping of the main axes of discussions on the theme and the respective authorship networks.

DOI:https://doi.org/10.56238/sevenIVmulti2023-067