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Self-Similarity of Twitter Users
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). University of Eastern Finland, Finland. (DISA-DH)ORCID iD: 0000-0002-3000-0381
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Linköping University, Sweden. (ISOVIS;DISA-DH)ORCID iD: 0000-0002-1907-7820
Linnaeus University, Faculty of Arts and Humanities, Department of Languages. University of Eastern Finland, Finland. (DISA-DH)ORCID iD: 0000-0003-3123-6932
University of Eastern Finland, Finland.ORCID iD: 0000-0002-9554-2827
2021 (English)In: Proceedings of the 2021 Swedish Workshop on Data Science (SweDS) / [ed] Rafael M. Martins, Morgan Ericsson, Danny Weyns, Kostiantyn Kucher, IEEE, 2021, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

Earlier studies have established that the (perceived) similarity of users is highly subjective and reflects more on how people respect/admire others rather than their characteristics or behavioral similarities. We study this phenomenon among Twitter users, and while confirm that it is indeed the case, we further explore the components of similarity by investigating it using data from three categories (interactions between egos and alters, profile-based activity history, and linguistic content in the messages). We use interactions as estimation for admiration and observe that it has more impact and a higher correlation to the perceived similarity than other objective measures, including similarity based on user profiles and their use of hashtags.

Place, publisher, year, edition, pages
IEEE, 2021. p. 1-7
Keywords [en]
social network analysis, ego network, user similarity, users interactions, activity history
National Category
Computer Sciences Languages and Literature
Research subject
Computer and Information Sciences Computer Science, Computer Science; Humanities, English
Identifiers
URN: urn:nbn:se:lnu:diva-108363DOI: 10.1109/SweDS53855.2021.9638288ISI: 000833296400007Scopus ID: 2-s2.0-85123842650ISBN: 9781665418300 (electronic)OAI: oai:DiVA.org:lnu-108363DiVA, id: diva2:1616683
Conference
2021 Swedish Workshop on Data Science (SweDS), Växjö, Sweden, December 2-3, 2021
Projects
DISAAvailable from: 2021-12-03 Created: 2021-12-03 Last updated: 2022-11-03Bibliographically approved

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Fatemi, MasoudKucher, KostiantynLaitinen, Mikko

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Fatemi, MasoudKucher, KostiantynLaitinen, MikkoFränti, Pasi
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Citation style
  • apa
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Output format
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