Predicting perceived ethnicity with data on personal names in Russia

Bessudnov, A; Tarasov, D; Panasovets, V; Kostenko, V; Smirnov, I; Uspenskiy, V

Predicting perceived ethnicity with data on personal names in Russia

Bessudnov, A Tarasov, D Panasovets, V Kostenko, V Smirnov, I

Uspenskiy, V

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Journal of Computational Social Science, 2023, 6, (2), pp. 589-608
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Bessudnov, A
dc.contributor.author	Tarasov, D
dc.contributor.author	Panasovets, V
dc.contributor.author	Kostenko, V
dc.contributor.author	Smirnov, I https://orcid.org/0000-0002-8347-6703
dc.contributor.author	Uspenskiy, V
dc.date.accessioned	2024-03-14T20:10:37Z
dc.date.available	2024-03-14T20:10:37Z
dc.date.issued	2023-10-01
dc.identifier.citation	Journal of Computational Social Science, 2023, 6, (2), pp. 589-608
dc.identifier.issn	2432-2717
dc.identifier.issn	2432-2725
dc.identifier.uri	http://hdl.handle.net/10453/176720
dc.description.abstract	In this paper, we develop a machine learning classifier that predicts perceived ethnicity from data on personal names for major ethnic groups populating Russia. We collect data from VK, the largest Russian social media website. Ethnicity was coded from languages spoken by users and their geographical location, with the data manually cleaned by crowd workers. The classifier shows the accuracy of 0.82 for a scheme with 24 ethnic groups and 0.92 for 15 aggregated ethnic groups. It can be used for research on ethnicity and ethnic relations in Russia, with the data sets that have personal names but not ethnicity.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Journal of Computational Social Science
dc.relation.isbasedon	10.1007/s42001-023-00205-y
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4410 Sociology
dc.subject.classification	4609 Information systems
dc.subject.classification	4701 Communication and media studies
dc.title	Predicting perceived ethnicity with data on personal names in Russia
dc.type	Journal Article
utslib.citation.volume	6
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/DVC (Research)
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-14T20:10:35Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	2

Abstract:

In this paper, we develop a machine learning classifier that predicts perceived ethnicity from data on personal names for major ethnic groups populating Russia. We collect data from VK, the largest Russian social media website. Ethnicity was coded from languages spoken by users and their geographical location, with the data manually cleaned by crowd workers. The classifier shows the accuracy of 0.82 for a scheme with 24 ethnic groups and 0.92 for 15 aggregated ethnic groups. It can be used for research on ethnicity and ethnic relations in Russia, with the data sets that have personal names but not ethnicity.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176720