# Gender Inference using Statistical Name Characteristics in Twitter

arXiv: https://arxiv.org/abs/1606.05467 · HTML: https://mlsanity.com/paper/1606.05467/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models](https://mlsanity.com/paper/2102.03692/) (2021)
- [ArabGend: Gender Analysis and Inference on Arabic Twitter](https://mlsanity.com/paper/2203.00271/) (2022)
- [Decoding Demographic un-fairness from Indian Names](https://mlsanity.com/paper/2209.03089/) (2022)

## Abstract (arXiv)

Much attention has been given to the task of gender inference of Twitter users. Although names are strong gender indicators, the names of Twitter users are rarely used as a feature; probably due to the high number of ill-formed names, which cannot be found in any name dictionary. Instead of relying solely on a name database, we propose a novel name classifier. Our approach extracts characteristics from the user names and uses those in order to assign the names to a gender. This enables us to classify international first names as well as ill-formed names.