Nationality identification unlocks important demographic information, with
many applications in biomedical and sociological research. Existing name-based
nationality classifiers use name substrings as features and are trained on
small, unrepresentative sets of labeled names, typically extracted from
Wikipedia. As a result, these methods achieve limited performance and cannot
support fine-grained classification.
We exploit the phenomena of homophily in communication patterns to learn name
embeddings, a new representation that encodes gender, ethnicity, and
nationality which is readily applicable to building classifiers and other
systems. Through our analysis of 57M contact lists from a major Internet
company, we are able to design a fine-grained nationality classifier covering
39 groups representing over 90% of the world population. In an evaluation
against other published systems over 13 common classes, our F1 score (0.795) is
substantial better than our closest competitor Ethnea (0.580). To the best of
our knowledge, this is the most accurate, fine-grained nationality classifier
available.
As a social media application, we apply our classifiers to the followers of
major Twitter celebrities over six different domains. We demonstrate stark
differences in the ethnicities of the followers of Trump and Obama, and in the
sports and entertainments favored by different groups. Finally, we identify an
anomalous political figure whose presumably inflated following appears largely
incapable of reading the language he posts in.

通过分析互联网公司的 5700 万个联系人列表，我们可以设计一个涵盖 90% 以上世界人口的 39 个代表细粒度国籍分类器，并将其应用于 Twitter 名人的追随者，演示了不同族裔追随者之间的鲜明差异。