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Data Mining and Knowledge Discovery
Published in: Data Mining and Knowledge Discovery 4/2021

12-05-2021

What’s in a name? – gender classification of names with character based machine learning models

Authors: Yifan Hu, Changwei Hu, Thanh Tran, Tejaswi Kasturi, Elizabeth Joseph, Matt Gillingham

Published in: Data Mining and Knowledge Discovery | Issue 4/2021

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Abstract

Gender information is no longer a mandatory input when registering for an account at many leading Internet companies. However, prediction of demographic information such as gender and age remains an important task, especially in intervention of unintentional gender/age bias in recommender systems. Therefore it is necessary to infer the gender of those users who did not to provide this information during registration. We consider the problem of predicting the gender of registered users based on their declared name. By analyzing the first names of 100M+ users, we found that genders can be very effectively classified using the composition of the name strings. We propose a number of character based machine learning models, and demonstrate that our models are able to infer the gender of users with much higher accuracy than baseline models. Moreover, we show that using the last names in addition to the first names improves classification performance further.
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Appendix
Available only for authorised users
Footnotes
1
E.g., if a user read an article about the department store Macy’s, a categorical variable wiki_Macy’s is added to the list of features describing the user
 
2
As an example, in SSA Data, around 21% of people with the name “Avery” are male. In a regression setting, we can fit a model to predict a value of 0.21 given this name. On the other hand, in a binary classification setting, we seek to predict a value of 0 for this name.
 
3
Given a predominantly female name with a male probability of \(p < 0.5\), and given k randomly selected people with this name, the probability that more than half of these people are male is: \(f(p, k) = \sum _{i=\lceil k/2\rceil )}^{k} C_n^i p^i(1-p)^{k-i}\). For \(p = 0.3\), we found that \(f(p,k) < 0.05\) when \(k \ge 16.\) For \(p=0.4\), we found that \(f(p,k) < 0.05\) when \(k \ge 66.\)
 
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Metadata
Title
What’s in a name? – gender classification of names with character based machine learning models
Authors
Yifan Hu
Changwei Hu
Thanh Tran
Tejaswi Kasturi
Elizabeth Joseph
Matt Gillingham
Publication date
12-05-2021
Publisher
Springer US
Published in
Data Mining and Knowledge Discovery / Issue 4/2021
Print ISSN: 1384-5810
Electronic ISSN: 1573-756X
DOI
https://doi.org/10.1007/s10618-021-00748-6

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