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Predicting perceived ethnicity with data on personal names in Russia

Author

Listed:
  • Alexey Bessudnov

    (University of Exeter)

  • Denis Tarasov

    (Constructor University Bremen)

  • Viacheslav Panasovets

    (St Petersburg State University)

  • Veronica Kostenko

    (Sociology, European University at St Petersburg)

  • Ivan Smirnov

    (RWTH Aachen University)

  • Vladimir Uspenskiy

    (ITMO University)

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.

Suggested Citation

  • Alexey Bessudnov & Denis Tarasov & Viacheslav Panasovets & Veronica Kostenko & Ivan Smirnov & Vladimir Uspenskiy, 2023. "Predicting perceived ethnicity with data on personal names in Russia," Journal of Computational Social Science, Springer, vol. 6(2), pages 589-608, October.
    • Handle: RePEc:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-023-00205-y
    DOI: 10.1007/s42001-023-00205-y
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