Economic history goes digital: topic modeling the Journal of Economic History

Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large coll...

Full description

Saved in:
Bibliographic Details
Published in:Cliometrica 2019-01, Vol.13 (1), p.83-125
Main Author: Wehrheim, Lino
Format: Article
Language:English
Subjects:
Algorithms
Data mining
Digitization
Econometrics
Economic history
Economic models
Economic statistics
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Finance
History
History of Economic Thought/Methodology
Insurance
Management
Original Paper
Statistics for Business
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large collections of texts. In this article, I present an introduction to topic modeling and give an initial review of the research using topic models. I illustrate their capacity by applying them to 2675 articles published in the Journal of Economic History between 1941 and 2016. By comparing the results to traditional research on the JEH and to recent studies on the cliometric revolution, I aim to demonstrate how topic models can enrich economic historians’ methodological toolboxes.
ISSN:1863-2505
1863-2513
DOI:10.1007/s11698-018-0171-7