@inproceedings{f0d2cc873980436e80746ff1c3cfa89c,
title = "Demographer: Extremely Simple Name Demographics",
abstract = "The lack of demographic information available when conducting passive analysis of social media content can make it difficult to compare results to traditional survey results. We present DEMOGRAPHER,1 a tool that predicts gender from names, using name lists and a classifier with simple character-level features. By relying only on a name, our tool can make predictions even without extensive user-authored content. We compare DEMOGRAPHER to other available tools and discuss differences in performance. In particular, we show that DEMOGRAPHER performs well on Twitter data, making it useful for simple and rapid social media demographic inference.",
author = "Rebecca Knowles and Josh Carroll and Mark Dredze",
note = "Publisher Copyright: {\textcopyright}2016 Association for Computational Linguistics.; EMNLP 2016 1st Workshop on Natural Language Processing and Computational Social Science, NLP + CSS 2016 ; Conference date: 05-11-2016 Through 05-11-2016",
year = "2016",
language = "English (US)",
series = "NLP + CSS 2016 - EMNLP 2016 Workshop on Natural Language Processing and Computational Social Science, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "108--113",
booktitle = "NLP + CSS 2016 - EMNLP 2016 Workshop on Natural Language Processing and Computational Social Science, Proceedings of the Workshop",
address = "United States",
}