# Nationality Classification Using Name Embeddings

arXiv: https://arxiv.org/abs/1708.07903 · HTML: https://mlsanity.com/paper/1708.07903/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2017.

## Built on by (most cited first, 10 of 10)

- [When Fair Ranking Meets Uncertain Inference](https://mlsanity.com/paper/2105.02091/) (2021)
- [What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models](https://mlsanity.com/paper/2102.03692/) (2021)
- [China and the U.S. produce more impactful AI research when collaborating together](https://mlsanity.com/paper/2304.11123/) (2023)
- [Using Embedding Models to Improve Probabilistic Race Prediction](https://mlsanity.com/paper/2604.22555/) (2026)
- [Nationality and Region Prediction from Names: A Comparative Study of Neural Models and Large Language Models](https://mlsanity.com/paper/2601.08692/) (2026)
- ['Moving On' -- Investigating Inventors' Ethnic Origins Using Supervised Learning](https://mlsanity.com/paper/2201.00578/) (2022)
- [Instate: Predicting the State of Residence From Last Name](https://mlsanity.com/paper/2303.06823/) (2023)
- [Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models](https://mlsanity.com/paper/2505.06107/) (2025)
- [Who Does This Name Remind You of ? Nationality Prediction via Large Language Model Associative Memory](https://mlsanity.com/paper/2601.12771/) (2026)
- [NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data](https://mlsanity.com/paper/2604.10401/) (2026)

## Abstract (arXiv)

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.