Predicting Twitter User Demographics from Names Alone
Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, Mark Dredze
Abstract
Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends. These tools often require hundreds of user-authored messages for each user, which may be prohibitive to obtain when analyzing millions of users. We explore character-level neural models that learn a representation of a user’s name and screen name to predict gender and ethnicity, allowing for demographic inference with minimal data. We release trained models1 which may enable new demographic analyses that would otherwise require enormous amounts of data collection- Anthology ID:
- W18-1114
- Volume:
- Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana, USA
- Venue:
- PEOPLES
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 105–111
- Language:
- URL:
- https://aclanthology.org/W18-1114
- DOI:
- 10.18653/v1/W18-1114
- Cite (ACL):
- Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, and Mark Dredze. 2018. Predicting Twitter User Demographics from Names Alone. In Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, pages 105–111, New Orleans, Louisiana, USA. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Twitter User Demographics from Names Alone (Wood-Doughty et al., PEOPLES 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W18-1114.pdf
- Code
- mdredze/demographer