The paper "Exploring the dynamic changes of key concepts of the Hungarian socialist era with natural language processing methods" has been published in Historical Methods:  A Journal of Quantitative and Interdisciplinary History (Q1, 2019 Impact Factor: 0.813)

The Authors of this collaboration by CSS-RECENS and RGAI (Research Group on Artificial Intelligence) of University of Szeged: Martina Katalin Szabó, Orsolya Ring, Balázs Nagy, László Kiss, Júlia Koltai, Gábor Berend, László Vidács, Attila Gulyás & Zoltán Kmetty

Available here: https://doi.org/10.1080/01615440.2020.1823289

Abstract

The analysis of social discourses from the perspective of historical changes deserves special attention. Such a study could play a key role in revealing social changes and latent narrative of those in power; and understanding the underlying social dynamic in a given period. Until the recent years, such issues were analyzed mainly in a qualitative approach. In our paper we present a new way of revealing/discovering and interpreting social discourses using an advanced NLP method called word embedding. Based on word similarities we can understand the main structural frames of a given system and using a dynamic approach we can reveal the social changes in a historical period. In our study we created a large corpus from the Hungarian “Pártélet” journal (1956–89). This was the official journal of the governing party, hence it represents not just a media discourse of the era, but the official discourse of the government, too. One of the main focal points of our research is to study the evolution of the semantic content of some of the concepts related to the topics of agriculture and industry, which are two central notions of the examined era.