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.

中文翻译：

---

经济史走向数字化：主题模型《经济史》

数字化和计算机科学已经建立了一套全新的方法来分析大量文本。这些方法之一对经济史学家特别有希望：主题模型，即从大量文本中自动推断内容的统计算法。在本文中，我介绍了主题建模，并使用主题模型对研究进行了初步回顾。我通过将它们应用到1941年至2016年间在《经济历史杂志》上发表的2675条文章中来说明它们的能力。通过将结果与JEH的传统研究以及最近的气候变化研究进行比较，我旨在证明主题模型如何丰富经济史学家的方法论工具箱。