Predicting perceived ethnicity with data on personal names in Russia

被引：0
作者：
Alexey Bessudnov
Denis Tarasov
Viacheslav Panasovets
Veronica Kostenko
Ivan Smirnov
Vladimir Uspenskiy
机构：
[1] University of Exeter,Social and Political Sciences, Philosophy and Anthropology
[2] Constructor University Bremen,Computer Science
[3] St Petersburg State University,Applied Mathematics and Control Processes
[4] Sociology,Computational Social Sciences and Humanities
[5] European University at St Petersburg,Digital Transformation
[6] RWTH Aachen University,undefined
[7] ITMO University,undefined
来源：
Journal of Computational Social Science | 2023年 / 6卷
关键词：
Ethnicity; Russia; Machine learning; Prediction; Personal names;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：589 / 608
页数：19
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