University of Oulu

Moilanen Mikko, Østbye Stein & Simonen Jaakko (2022) Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia, Regional Studies, 56:9, 1429-1441, DOI: 10.1080/00343404.2021.1925237

Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia

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Author: Moilanen, Mikko1; Østbye, Stein1; Simonen, Jaakko2
Organizations: 1School of Business and Economics, UiT The Arctic University of Norway, Tromsø, Norway
2Department of Economics, Accounting and Finance, Oulu Business School, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
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Language: English
Published: Informa, 2022
Publish Date: 2022-10-25


The European Union (EU) has recognized that universities and research institutes play a critical role in regional Smart Specialisation processes. Our research aims to identify thematic cross-border research domains across space and disciplines in Arctic Scandinavia. We identify potential domains using an unsupervised machine-learning technique (topic modelling). We uncover latent topics based on similarities in the vocabulary of research papers. The proposed methodology can be utilized to identify common research domains across regions and disciplines in almost real time, thereby acting as a decision support system to facilitate cooperation among knowledge producers.

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Series: Regional studies
ISSN: 0034-3404
ISSN-E: 1360-0591
ISSN-L: 0034-3404
Volume: 56
Issue: 9
Pages: 1429 - 1441
DOI: 10.1080/00343404.2021.1925237
Type of Publication: A1 Journal article – refereed
Field of Science: 519 Social and economic geography
Funding: This research is connected to the GenZ project, a strategic profiling project in human sciences at the University of Oulu. The project is supported by the Academy of Finland [grant number Profi4 318930] and the University of Oulu.
Copyright information: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.