@inproceedings{eb2a886c35be4b50a5d324686e368793,
title = "Predicting twitter user demographics from names alone",
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 models which may enable new demographic analyses that would otherwise require enormous amounts of data collection.",
author = "Zach Wood-Doughty and Nicholas Andrews and Rebecca Marvin and Mark Dredze",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics.; 2nd Workshop on Computational Modeling of PFople's Opinions, PersonaLity, and Emotions in Social Media, PEOPLES 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018 ; Conference date: 06-06-2018",
year = "2018",
language = "English (US)",
series = "Proceedings of the 2nd Workshop on Computational Modeling of PFople's Opinions, PersonaLity, and Emotions in Social Media, PEOPLES 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018",
publisher = "Association for Computational Linguistics (ACL)",
pages = "105--111",
editor = "Malvina Nissim and Viviana Patti and Barbara Plank and Claudia Wagner",
booktitle = "Proceedings of the 2nd Workshop on Computational Modeling of PFople's Opinions, PersonaLity, and Emotions in Social Media, PEOPLES 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics",
address = "United States",
}