What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models Article Swipe
Yifan Hu
,
Changwei Hu
,
Thanh Tran
,
Tejaswi Kasturi
,
Elizabeth Joseph
,
Matt Gillingham
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.03692
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.03692
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.03692
- https://arxiv.org/pdf/2102.03692
- OA Status
- green
- Cited By
- 3
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3128181571
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Raw OpenAlex JSON
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https://openalex.org/W3128181571Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.03692Digital Object Identifier
- Title
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What's in a Name? -- Gender Classification of Names with Character Based Machine Learning ModelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
- Publication date
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2021-02-07Full publication date if available
- Authors
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Yifan Hu, Changwei Hu, Thanh Tran, Tejaswi Kasturi, Elizabeth Joseph, Matt GillinghamList of authors in order
- Landing page
-
https://arxiv.org/abs/2102.03692Publisher landing page
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https://arxiv.org/pdf/2102.03692Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2102.03692Direct OA link when available
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Character (mathematics), Task (project management), Computer science, Baseline (sea), Artificial intelligence, The Internet, Natural language processing, Machine learning, World Wide Web, Political science, Mathematics, Engineering, Law, Systems engineering, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2023: 2Per-year citation counts (last 5 years)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 20, 35, 50, 66, 70, 83, 98, 106, 123 |
| abstract_inverted_index.on | 74 |
| abstract_inverted_index.to | 46, 56, 119, 142 |
| abstract_inverted_index.we | 86, 133 |
| abstract_inverted_index.age | 27 |
| abstract_inverted_index.and | 26, 112 |
| abstract_inverted_index.are | 117 |
| abstract_inverted_index.can | 90 |
| abstract_inverted_index.did | 54 |
| abstract_inverted_index.for | 10 |
| abstract_inverted_index.not | 55 |
| abstract_inverted_index.our | 115 |
| abstract_inverted_index.the | 48, 64, 68, 80, 96, 99, 121, 137, 143 |
| abstract_inverted_index.who | 53 |
| abstract_inverted_index.able | 118 |
| abstract_inverted_index.bias | 38 |
| abstract_inverted_index.last | 138 |
| abstract_inverted_index.many | 14 |
| abstract_inverted_index.much | 126 |
| abstract_inverted_index.name | 100 |
| abstract_inverted_index.show | 134 |
| abstract_inverted_index.such | 23 |
| abstract_inverted_index.than | 129 |
| abstract_inverted_index.that | 88, 114, 135 |
| abstract_inverted_index.this | 58 |
| abstract_inverted_index.very | 92 |
| abstract_inverted_index.when | 8 |
| abstract_inverted_index.with | 125 |
| abstract_inverted_index.100M+ | 84 |
| abstract_inverted_index.based | 73, 108 |
| abstract_inverted_index.first | 81, 144 |
| abstract_inverted_index.found | 87 |
| abstract_inverted_index.infer | 47, 120 |
| abstract_inverted_index.input | 7 |
| abstract_inverted_index.name. | 77 |
| abstract_inverted_index.names | 82, 139, 145 |
| abstract_inverted_index.task, | 31 |
| abstract_inverted_index.their | 75 |
| abstract_inverted_index.those | 51 |
| abstract_inverted_index.users | 52, 72, 124 |
| abstract_inverted_index.using | 95, 136 |
| abstract_inverted_index.Gender | 0 |
| abstract_inverted_index.during | 60 |
| abstract_inverted_index.gender | 25, 49, 69, 122 |
| abstract_inverted_index.higher | 127 |
| abstract_inverted_index.longer | 4 |
| abstract_inverted_index.models | 116 |
| abstract_inverted_index.number | 105 |
| abstract_inverted_index.users, | 85 |
| abstract_inverted_index.account | 12 |
| abstract_inverted_index.genders | 89 |
| abstract_inverted_index.leading | 15 |
| abstract_inverted_index.machine | 109 |
| abstract_inverted_index.models, | 111 |
| abstract_inverted_index.models. | 131 |
| abstract_inverted_index.problem | 65 |
| abstract_inverted_index.propose | 103 |
| abstract_inverted_index.provide | 57 |
| abstract_inverted_index.remains | 28 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.Internet | 16 |
| abstract_inverted_index.accuracy | 128 |
| abstract_inverted_index.addition | 141 |
| abstract_inverted_index.baseline | 130 |
| abstract_inverted_index.consider | 63 |
| abstract_inverted_index.declared | 76 |
| abstract_inverted_index.further. | 149 |
| abstract_inverted_index.improves | 146 |
| abstract_inverted_index.learning | 110 |
| abstract_inverted_index.strings. | 101 |
| abstract_inverted_index.systems. | 41 |
| abstract_inverted_index.Moreover, | 132 |
| abstract_inverted_index.Therefore | 42 |
| abstract_inverted_index.analyzing | 79 |
| abstract_inverted_index.character | 107 |
| abstract_inverted_index.important | 30 |
| abstract_inverted_index.mandatory | 6 |
| abstract_inverted_index.necessary | 45 |
| abstract_inverted_index.classified | 94 |
| abstract_inverted_index.companies. | 17 |
| abstract_inverted_index.especially | 32 |
| abstract_inverted_index.gender/age | 37 |
| abstract_inverted_index.predicting | 67 |
| abstract_inverted_index.prediction | 19 |
| abstract_inverted_index.registered | 71 |
| abstract_inverted_index.composition | 97 |
| abstract_inverted_index.demographic | 21 |
| abstract_inverted_index.demonstrate | 113 |
| abstract_inverted_index.effectively | 93 |
| abstract_inverted_index.information | 1, 22, 59 |
| abstract_inverted_index.performance | 148 |
| abstract_inverted_index.recommender | 40 |
| abstract_inverted_index.registering | 9 |
| abstract_inverted_index.intervention | 34 |
| abstract_inverted_index.registration. | 61 |
| abstract_inverted_index.unintentional | 36 |
| abstract_inverted_index.classification | 147 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100708726 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |