mlsanityData to 7 Oct 2026

What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models

arXiv 2102.03692 ↗First posted 7 Feb 2021cs.LG cs.IRcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 11 of its 42 references that are in the map. First posted on arXiv 7 Feb 2021.

Builds on

11 of 42 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
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  7. 2018Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendationscited by 11
  8. 2017Nationality Classification Using Name Embeddingscited by 10
  9. 2016Gender Inference using Statistical Name Characteristics in Twittercited by 3
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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 5; 2024: 3; 2025: 2.

12 papers.

  1. 2024Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictionscited by 17
  2. 2024Evaluating LLMs for Gender Disparities in Notable Personscited by 3
  3. 2022Decoding Demographic un-fairness from Indian Namescited by 2
  4. 2023Gender inference: can chatGPT outperform common commercial tools?cited by 2
  5. 2023For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Networkcited by 1
  6. 2023Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Namescited by 1
  7. 2021Predicting Gender by First Name Using Character-level Machine Learningcited by 0
  8. 2023Instate: Predicting the State of Residence From Last Namecited by 0
  9. 2023Can We Trust Race Prediction?cited by 0
  10. 2024For the Misgendered Chinese in Gender Bias Research: Multi-Task Learning with Knowledge Distillation for Pinyin Name-Gender Predictioncited by 0
  11. 2025Measuring and Analyzing Subjective Uncertainty in Scientific Communicationscited by 0
  12. 2025Agentic Username Suggestion and Multimodal Gender Detection in Online Platforms: Introducing the PNGT-26K Datasetcited by 0