Date
2020-08-05Author
Rehs, AndreasSubject
004 Data processing and computer science 370 Education SzientometrieWiedervereinigung <Deutschland>DissertationMaschinelles LernenMetadata
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Aufsatz
A structural topic model approach to scientific reorientation of economics and chemistry after German reunification
Abstract
The detection of differences or similarities in large numbers of scientific publications is an open problem in scientometric research. In this paper we therefore develop and apply a machine learning approach based on structural topic modelling in combination with cosine similarity and a linear regression framework in order to identify differences in dissertation titles written at East and West German universities before and after German reunification. German reunification and its surrounding time period is used because it provides a structure with both minor and major differences in research topics that could be detected by our approach. Our dataset is based on dissertation titles in economics and business administration and chemistry from 1980 to 2010. We use university affiliation and year of the dissertation to train a structural topic model and then test the model on a set of unseen dissertation titles. Subsequently, we compare the resulting topic distribution of each title to every other title with cosine similarity. The cosine similarities and the regional and temporal origin of the dissertation titles they come from are then used in a linear regression approach. Our results on research topics in economics and business administration suggest substantial differences between East and West Germany before the reunification and a rapid conformation thereafter. In chemistry we observe minor differences between East and West before the reunification and a slightly increased similarity thereafter.
Citation
In: Scientometrics Volume 125 / Issue 2 (2020-08-05) , S. 1229-1251 ; EISSN 1588-2861Sponsorship
Gefördert im Rahmen des Projekts DEALCitation
@article{doi:10.17170/kobra-202012082413,
author={Rehs, Andreas},
title={A structural topic model approach to scientific reorientation of economics and chemistry after German reunification},
journal={Scientometrics},
year={2020}
}
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2020-12-09T09:24:51Z 2020-12-09T09:24:51Z 2020-08-05 doi:10.17170/kobra-202012082413 http://hdl.handle.net/123456789/12131 Gefördert im Rahmen des Projekts DEAL eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ topic modelling german reunification dissertations structural topic modelling research field mapping 004 370 A structural topic model approach to scientific reorientation of economics and chemistry after German reunification Aufsatz The detection of differences or similarities in large numbers of scientific publications is an open problem in scientometric research. In this paper we therefore develop and apply a machine learning approach based on structural topic modelling in combination with cosine similarity and a linear regression framework in order to identify differences in dissertation titles written at East and West German universities before and after German reunification. German reunification and its surrounding time period is used because it provides a structure with both minor and major differences in research topics that could be detected by our approach. Our dataset is based on dissertation titles in economics and business administration and chemistry from 1980 to 2010. We use university affiliation and year of the dissertation to train a structural topic model and then test the model on a set of unseen dissertation titles. Subsequently, we compare the resulting topic distribution of each title to every other title with cosine similarity. The cosine similarities and the regional and temporal origin of the dissertation titles they come from are then used in a linear regression approach. Our results on research topics in economics and business administration suggest substantial differences between East and West Germany before the reunification and a rapid conformation thereafter. In chemistry we observe minor differences between East and West before the reunification and a slightly increased similarity thereafter. open access Rehs, Andreas doi:10.1007/s11192-020-03640-0 Szientometrie Wiedervereinigung <Deutschland> Dissertation Maschinelles Lernen publishedVersion EISSN 1588-2861 Issue 2 Scientometrics 1229-1251 Volume 125 false
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