Introduction

Over the past decades, social scientists gained access to many large-scale data sets thanks to the proliferation of digital traces [1]. The explosive growth in new data even raised hopes that social science was entering its golden age [2]. However, digital traces are typically not collected with a research purpose in mind and are framed by the needs of data providers. As a result, they often lack information on individuals that is important to researchers. One potential solution is to infer missing information using machine learning methods. For example, various socio-demographic characteristics were predicted from profile images [3], mobile phone metadata [4], Facebook likes [5], and images of street scenes [6].

One of important characteristics that is of great interest to social scientists but rarely present in digital traces is ethnicity. Taking ethnicity into account is important for analysing social inequalities in health [7], political participation [8], the labour market and housing [9], among other areas.

While lacking information on ethnicity, some large-scale data sets have not been anonymised and include personal names. Examples of such data sets include US voter registration data [10] or Twitter data [11]. Personal names can be used as a signal for ethnicity for many ethnic groups. Experimental studies of discrimination in the labour market and in housing have been using this feature [9]; it was also applied to historic studies of social mobility [12]. An ability to infer ethnicity from personal names allows social scientists to use new administrative and social media data.

While several ethnicity classifiers are already available to researchers, most of them are focusing on a few immigration destination countries and are limited to a small number of ethnic groups [13]. In this paper, we are addressing this gap by develo** a machine learning approach to coding perceived ethnicity from personal names for ethnic groups populating Russia, using data from VK, the largest Russian social media website.

There are several approaches to classifying ethnicity with data on personal names. Early studies employ a dictionary-based method where names were matched to a reference list of names already classified by ethnicity. [14] is perhaps the first example of automatic name binary classification, developed to separate Chinese from non-Chinese names in Canada. [15] offers a review of 13 studies published up to 2007 that use similar methodology. In a more recent application, [16, 17] use both matching and supervised learning to classify ethnicity of Facebook users in the Netherlands on the basis of their fist names, using the Dutch census as the reference list (also see [18]).

A successful application of the matching approach requires a large reference list that covers most ethnic first names and/or surnames. However, for many ethnic groups, the reference lists of names do not exist or are incomplete. These classifiers also rely on the assumption that name/ethnicity distributions are similar for the reference list and the target population. As reference lists are often compiled from census data, it is not clear how well they will work with social media data.

Another approach to ethnic name classification is based on supervised learning algorithms. The main advantage of this approach is that it allows researchers to classify previously unseen names. [19] develop a multiclass name classifier for 13 ethnic groups with the data from Wikipedia using hidden Markov models and decision trees. [20] use recurrent neural networks to predict ethnicity from names from the Olympic records data. In other recent studies, [1 shows the list of ethnic groups and their population and sample sizes.

Table 1 Ethnic groups and their population and sample sizes
Full size table

Machine learning pipeline

To apply ML algorithms, text must be transformed into numerical vectors. We use three different vectorisation methods (Bag of Words, TFIDF, and fastText) and compare their performance with different ML algorithms.

The Bag of Words (BoW) converts text into a vector with dimensionality equal to the size of the vocabulary formed with unique tokens (extracted n-grams in our case) from a corpus. n-gram is a sequence of n characters from a name: for example, 3-grams of the name \(\ll\)Alice\(\gg\) are \(\ll\)Ali\(\gg\), \(\ll\)lic\(\gg\), \(\ll \! ice \! \gg\). Vectorisation is then performed according to the token (n-gram) frequency. As an example, if a corpus contains only tokens ‘A’, ‘T’, ‘G’, ‘C’, then \(\ll\)AATGA\(\gg\) would be converted to < 3, 1, 1, 0> .

TFIDF (term frequency-inverse document frequency) shares the same idea, but it uses the \(tf\text {-}idf\) function of a token, i.e., normalise the token frequency by the share of all words that contain the token. The motivation for this transformation is to decrease the impact of frequent tokens that often provide little information and to increase the impact of rare tokens that are more informative [29].

Finally, the fastText model (FT) [43]. The issue of ethnicity in Russia, very sensitive in the Soviet times, remains significant today in interpersonal relations, as well as in the labour market and housing. Its significance increased after the Russian invasion of Ukraine in 2022 (please note that this study was started and largely concluded before February 2022). It is important to recognize that our classifier cannot, and is not intended to identify ethnicity at the individual level. While this tool can produce reliable distributions for ethnicity for data sets with hundreds and thousands names, for each individual name, there remains a margin of error that does not let the classifier to be used for individual profiling.