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Economic History Goes Digital: Topic Modeling the Journal of Economic History

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  • Lino Wehrheim

Abstract

Digitization and computer science have established a whole new set of methods to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, statistical algorithms that automatically infer themes from large collections of texts. In this article, I present an introduction to topic modeling and give a very first review on the research using topic models. I illustrate their capacity by applying them on 2.675 articles published in the Journal of Economic History between 1941 and 2016. This contributes to traditional research on the JEH and to current research on the cliometric revolution.

Suggested Citation

  • Lino Wehrheim, 2017. "Economic History Goes Digital: Topic Modeling the Journal of Economic History," Working Papers 177, Bavarian Graduate Program in Economics (BGPE).
    • Handle: RePEc:bav:wpaper:177_wehrheim
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    File URL: https://bgpe.cms.rrze.uni-erlangen.de/files/2023/07/177_Economic-History-Goes-Digital_Topic-Modeling-the-Journal-of-Economic-History.pdf
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    References listed on IDEAS

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    Cited by:

    1. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.

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    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • N01 - Economic History - - General - - - Development of the Discipline: Historiographical; Sources and Methods

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