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.JEL-Classification: A12, C18, N01 as well as the participants in the research seminar in economic and social history at University of Regensburg for invaluable advice. This paper was presented in the lecture series on digital humanities at University of Regensburg.

PurposeThe purpose of this paper is to identify and analyse the news coverage and sentiment of real estate-related trends in Germany. Trends are considered as being stable and long-term. If the news coverage and sentiment of trends underlie cyclicity, this could impact investors’ behaviour. For instance, in the case of increased reporting on sustainability issues, investors may be inclined to invest more in sustainable buildings, assuming that this is of growing importance to their clients. Hence, investors could expect higher returns when a trend topic goes viral.Design/methodology/approachWith the help of topic modelling, incorporating seed words partially generated via word embeddings, almost 170,000 newspaper articles published between 1999 and 2019 by a major German real estate news provider are analysed and assigned to real estate-related trends. Through applying a dictionary-based approach, this dataset is then analysed based on whether the tone of the news coverage of a specific trend is subject to change.FindingsThe articles concerning urbanisation and globalisation account for the largest shares of reporting. However, the shares are subject to change over time, both in terms of news coverage and sentiment. In particular, the topic of sustainability illustrates a clearly increasing trend with cyclical movements throughout the examined period. Overall, the digitalisation trend has a highly positive connotation within the analysed articles, while regulation displays the most negative sentiment.Originality/valueTo the best of the authors’ knowledge, this is the first application to explore German real estate newspaper articles regarding the methodologies of word representation and seeded topic modelling. The integration of topic modelling into real estate analysis provides a means through which to extract information in a standardised and replicable way. The methodology can be applied to several further fields like analysing market reports, company statements or social media comments on real estate topics. Finally, this is also the first study to measure the cyclicity of real estate-related trends by means of textual analysis.

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Zusammenfassung Auf Anregung einer (nichtrepräsentativen) Twitterumfrage unter Historikerinnen und Historikern möchten wir in diesem Beitrag ein von uns zusammengetragenes Korpus an Aufsätzen aus elf geschichtswissenschaftlichen Fachjournalen vorstellen und anhand einiger Beispiele illustrieren, wie sich der Gebrauch verschiedener prägnanter geschichtswissenschaftlicher (Leit‑) Begriffe seit 1950 verändert hat. Das Ziel ist nicht, eine „digitale Begriffsgeschichte“ verschiedener Begriffe vorzulegen oder gar Rückschlüsse auf die Entwicklung des Fachs zu ziehen. Vielmehr geht es uns darum, die vorgeschlagenen und um eigene Kandidaten ergänzten Begriffe und deren Konjunkturen zu präsentieren, um so eine Grundlage für eine weitergehende Diskussion digitalhistorischer Methoden und des Nutzens eines „Clio Viewers“ zu legen. Besonderes Augenmerk richten wir auf die Erörterung der mit dem Einsatz einfacher Stichwortsuchen verbundenen methodischen Fallstricke.

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