# The Secret Lives of Names? Name Embeddings from Social Media

arXiv: https://arxiv.org/abs/1905.04799 · HTML: https://mlsanity.com/paper/1905.04799/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2019.

## Built on by (most cited first, 4 of 4)

- [Applications of Machine Learning in Document Digitisation](https://mlsanity.com/paper/2102.03239/) (2021)
- ['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)

## Abstract (arXiv)

Your name tells a lot about you: your gender, ethnicity and so on. It has been shown that name embeddings are more effective in representing names than traditional substring features. However, our previous name embedding model is trained on private email data and are not publicly accessible. In this paper, we explore learning name embeddings from public Twitter data. We argue that Twitter embeddings have two key advantages: textit(i) they can and will be publicly released to support research community. textit(ii) even with a smaller training corpus, Twitter embeddings achieve similar performances on multiple tasks comparing to email embeddings. As a test case to show the power of name embeddings, we investigate the modeling of lifespans. We find it interesting that adding name embeddings can further improve the performances of models using demographic features, which are traditionally used for lifespan modeling. Through residual analysis, we observe that fine-grained groups (potentially reflecting socioeconomic status) are the latent contributing factors encoded in name embeddings. These were previously hidden to demographic models, and may help to enhance the predictive power of a wide class of research studies.