lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Detecting Connectivity Patterns in Nordic Twittersphere by Cluster Analysis
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). University of Eastern Finland, Finland.ORCID iD: 0000-0002-3000-0381
University of Eastern Finland, Finland.
Linnaeus University, Faculty of Arts and Humanities, Department of Languages. University of Eastern Finland, Finland.ORCID iD: 0000-0003-3123-6932
University of Eastern Finland, Finland.
2025 (English)In: SN Computer Science, ISSN 2662-995X, Vol. 6, no 7, article id 815Article in journal (Refereed) Published
Abstract [en]

We analyze Nordic social media users by clustering them based on their connections on Twitter. The data consists of 15,794 users in the five Nordic countries: Finland, Sweden, Norway, Denmark, and Iceland. We first create an undirected graph from the friendship relations (mutually following each other), then divide the graph into five clusters using a recent M-algorithm, and finally compare the results to users’ locations. The results demonstrate that the users are strongly clustered according to their home country. There is surprisingly little interaction across the countries despite the fact that they are, except for Iceland, physically close to each other and have cultural and linguistic similarities. The main language of the four countries belongs to the Germanic languages, while Finnish is typologically distinct. We further explore content from users in each country, analyzing its alignment with connectivity patterns. Our findings reveal a discrepancy between user-generated content similarity in the Nordic region and their connectivity patterns.

Place, publisher, year, edition, pages
Springer Nature, 2025. Vol. 6, no 7, article id 815
Keywords [en]
Clustering, Community detection, Graph clustering, Nordic countries, Social networks, Twitter users
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-142850DOI: 10.1007/s42979-025-04353-yScopus ID: 2-s2.0-105015490229OAI: oai:DiVA.org:lnu-142850DiVA, id: diva2:2024396
Note

Correction published in: Fatemi, M., Sieranoja, S., Laitinen, M., & Fränti, P. (2025). Correction: Detecting Connectivity Patterns in Nordic Twittersphere by Cluster Analysis. SN Computer Science, 6(7), Article 882. https://doi.org/10.1007/s42979-025-04443-x

Available from: 2025-12-29 Created: 2025-12-29 Last updated: 2026-01-12Bibliographically approved

Open Access in DiVA

fulltext(3265 kB)34 downloads
File information
File name FULLTEXT01.pdfFile size 3265 kBChecksum SHA-512
d6770772bd12fdc7781ca54edb1511800fc3a80cf7bb50c37a57f81635cb7d8fd82ca376e178a3752f771871a6af09a8f5bea276d6d11c027b8daad32be9aefa
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Fatemi, MasoudLaitinen, Mikko

Search in DiVA

By author/editor
Fatemi, MasoudLaitinen, Mikko
By organisation
Department of computer science and media technology (CM)Department of Languages
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 683 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf